time-series-1

energy trading

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

A Stochastic Game Framework for Efficient Energy Management in Microgrid Networks

Shravan Nayak, C. Ekbote, Annanya Pratap Singh Chauhan, Raghuram Bharadwaj Diddigi, Prishita Ray, Abhinava Sikdar, Sai Koti Reddy Danda, S. Bhatnagar•Wed Feb 05 2020

A novel approach that makes use of independent learners Deep Q-learning algorithm to solve the problem of energy management in microgrid networks in the framework of stochastic games is proposed.

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Additive Covariance Matrix Models: Modeling Regional Electricity Net-Demand in Great Britain

Vincenzo Gioia, Matteo Fasiolo, J. Browell, R. Bellio•Sun Nov 13 2022

Abstract Forecasts of regional electricity net-demand, consumption minus embedded generation, are an essential input for reliable and economic power system operation, and energy trading. While such forecasts are typically performed region by region, operations such as managing power flows require spatially coherent joint forecasts, which account for cross-regional dependencies. Here, we forecast the joint distribution of net-demand across the 14 regions constituting Great Britain’s electricity network. Joint modeling is complicated by the fact that the net-demand variability within each region, and the dependencies between regions, vary with temporal, socio-economic and weather-related factors. We accommodate for these characteristics by proposing a multivariate Gaussian model based on a modified Cholesky parameterization, which allows us to model each unconstrained parameter via an additive model. Given that the number of model parameters and covariates is large, we adopt a semi-automated approach to model selection, based on gradient boosting. In addition to comparing the forecasting performance of several versions of the proposed model with that of two non-Gaussian copula-based models, we visually explore the model output to interpret how the covariates affect net-demand variability and dependencies. The code for reproducing the results in this article is available at https://doi.org/10.5281/zenodo.7315105. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

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Peer-to-Peer Energy Trading in Smart Grid Through Blockchain: A Double Auction-Based Game Theoretic Approach

Hien Thanh Doan, Jeongho Cho, Daehee Kim•Thu Dec 31 2020

A peer-to-peer energy trading system among prosumers using a double auction-based game theoretic approach, where the buyer adjusts the amount of energy to buy according to varying electricity price in order to maximize benefit, to show the feasibility of real-time P2P trading.

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Decentralized Energy Marketplace via NFTs and AI-based Agents

Rasoul Nikbakht, Farhana Javed, Farhad Rezazadeh, N. Bartzoudis, J. Mangues-Bafalluy•Thu Nov 16 2023

The paper introduces an advanced Decentralized Energy Marketplace (DEM) integrating blockchain technol-ogy and artificial intelligence to manage energy exchanges among smart homes with energy storage systems. The proposed framework uses Non-Fungible Tokens (NFTs) to represent unique energy profiles in a transparent and secure trading environment. Leveraging Federated Deep Reinforce-ment Learning (FDRL), the system promotes collaborative and adaptive energy management strategies, maintaining user privacy. A notable innovation is the use of smart contracts, ensuring high efficiency and integrity in energy transactions. Extensive evaluations demonstrate the system's scalability and the effectiveness of the FDRL method in optimizing energy distribution. This research significantly contributes to developing sophisticated decentralized smart grid infras-tructures. Our approach broadens potential blockchain and AI applications in sustainable energy systems and addresses incentive alignment and transparency challenges in traditional energy trading mechanisms. The implementation of this paper is publicly accessible1,

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