Energy Trading among Power Grid and Renewable Energy Sources: A Dynamic Pricing and Demand Scheme for Profit Maximization

Yoo, Yoon-Sik; Jeon, Seung Hyun; Newaz, S. H. Shah; Lee, Il-Woo; Choi, Jun Kyun

doi:10.3390/s21175819

Cited by 10 publications

(5 citation statements)

References 39 publications

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“…Two well-known distributed algorithms used are the coordination-based dual-decomposition and the coordination-based alternating direction method of multiplier (ADMM). Based on the dual-decomposition algorithm, the optimal energy trading schedules between microgrids [53], EVs [54], and between utility company and users [55,56] were given.…”

Section: Classification Of Market Mechanismsmentioning

confidence: 99%

“…Two well‐known distributed algorithms used are the coordination‐based dual‐decomposition and the coordination‐based alternating direction method of multiplier (ADMM). Based on the dual‐decomposition algorithm, the optimal energy trading schedules between microgrids [53], EVs [54], and between utility company and users [55, 56] were given. The dual decomposition method is straightforward, however, its convergence relies on a set of strong assumptions [66], which may not hold for engineering problems.…”

Section: Market Mechanismsmentioning

confidence: 99%

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Towards transactive energy: An analysis of information‐related practical issues

Chen

Yong

2022

Energy Conversion and Econom

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The development of distributed energy resources, such as rooftop photovoltaic (PV) panels, batteries, and electric vehicles (EVs), has decentralized the power system operation, where transactive energy markets empower local energy exchanges. Transactive energy contributes to building a low‐carbon energy system by better matching the distributed renewable sources and demand. Effective market mechanisms are a key part of transactive energy market design. Despite fruitful research on related topics, some practical challenges must be addressed. This review surveys three practical issues related to information exchange in transactive energy markets: asynchronous computing, truthful reporting, and privacy preservation. The state‐of‐the‐art results are summarized and relevant multidisciplinary theories are introduced. Based on these findings, several potential research directions are suggested that could provide insights for future studies.

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Section: Classification Of Market Mechanismsmentioning

confidence: 99%

Section: Market Mechanismsmentioning

confidence: 99%

Towards transactive energy: An analysis of information‐related practical issues

Chen

Yong

2022

Energy Conversion and Econom

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“…Since we assume that the price of renewable energy is negligible, only the grid electricity price is taken into account. Grid energy prices are dynamically adjusted in response to when electricity is used [59,60]. From 18 September 2018, to 17 October 2018, we utilize the hourly energy prices q i (t)-in dollar MWh for three geo-dispersed data centers.…”

Section: Description Of Energy Pricesmentioning

confidence: 99%

Renewable-Aware Geographical Load Balancing Using Option Pricing for Energy Cost Minimization in Data Centers

et al. 2022

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It is becoming increasingly difficult to properly control the power consumption of widely dispersed data centers. Energy consumption is high because of the need to run these data centers (DCs) that handle incoming user requests. The rising cost of electricity at the data center is a contemporary problem for cloud service providers (CSPs). Recent studies show that geo-distributed data centers may share the load and save money using variable power prices and pricing derivatives in the wholesale electricity market. In this study, we evaluate the problem of reducing energy expenditures in geographically dispersed data centers while accounting for variable system dynamics, power price fluctuations, and renewable energy sources. We present a renewable energy-based load balancing employing an option pricing (RLB-Option) online algorithm based on a greedy approach for interactive task allocation to reduce energy costs. The basic idea of RLB-Option is to process incoming user requests using available renewable energy sources. In contrast, in the case of unprocessed user requests, the workload will be processed using brown energy or call option contract at each timeslot. We formulate the energy cost minimization in geo-distributed DCs as an optimization problem considering geographical load balancing, renewable energy, and an option pricing contract from the derivative market while satisfying the set of constraints. We prove that the RLB-Option can reduce the energy cost of the DCs close to that of the optimal offline algorithm with future information. Compared to standard workload allocation methods, RLB-Option shows considerable cost savings in experimental evaluations based on real-world data.

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“…Furthermore, it is advantageous to install a PV generator in the downtown area so that the capacity size of the PV generator can be determined considering user preference. However, the PV generator has a problem because it is generation-sensitive to environmental change and seasonal factors [4]. The fact that the generator responds sensitively means that it is hard to predict power generation accurately, which means that the system operator could install incorrect generation capacity and induce various problems.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Model-Based Predicting PV Generation with the Conjugation of IoT Sensor Data

Heo,

Kim,

Choi

2023

Sensors

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This paper proposes a novel short-term photovoltaic voltage (PV) prediction scheme using IoT sensor data with the two-stage neural network model. It is efficient to use environmental data provided by the meteorological agency to predict future PV generation. However, such environmental data represent the average value of the wide area, and there is a limitation in detecting environmental changes in the specific area where the solar panel is installed. In order to solve such issues, it is essential to establish IoT sensor data to detect environmental changes in the specific area. However, most conventional research focuses only on the efficiency of IoT sensor data without taking into account the timing of data acquisition from the sensors. In real-world scenarios, IoT sensor data is not available precisely when needed for predictions. Therefore, it is necessary to predict the IoT data first and then use it to forecast PV generation. In this paper, we propose a two-stage model to achieve high-accuracy prediction results. In the first stage, we use predicted environmental data to access IoT sensor data in the desired future time point. In the second stage, the predicted IoT sensors and environmental data are used to predict PV generation. Here, we determine the appropriate prediction scheme at each stage by analyzing the model characteristics to increase prediction accuracy. In addition, we show that the proposed prediction scheme could increase prediction accuracy by more than 12% compared to the baseline scheme that only uses a meteorological agency to predict PV generation.

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Energy Trading among Power Grid and Renewable Energy Sources: A Dynamic Pricing and Demand Scheme for Profit Maximization

Cited by 10 publications

References 39 publications

Towards transactive energy: An analysis of information‐related practical issues

Towards transactive energy: An analysis of information‐related practical issues

Renewable-Aware Geographical Load Balancing Using Option Pricing for Energy Cost Minimization in Data Centers

Two-Stage Model-Based Predicting PV Generation with the Conjugation of IoT Sensor Data

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