Abstract
The scientific community believes that peer-to-peer energy trading will dominate a significant portion of forthcoming power generation systems research. Despite a plethora of optimal energy trading solutions, optimizing the trading cost and intelligent formation of energy sharing strategies are deemed exigent problems. Contemplating the excessive rise of energy demands across the globe, this study introduces a predictive optimization-based nanogrid energy trading model that minimizes energy trading cost and provides an optimal energy sharing plan between peers connected within a nanogrid network/cluster. The proposed study comprises two folds: (1) PSO-enabled objective function incorporating actual and predicted values of essential energy attributes, is implemented to reduce the trading cost, (2) an intelligent time-aware energy sharing strategy to determine the role of peers, and foster the harness of renewable energy to meet the energy requirements. The study also comprehensively analyzes essential nano-grid energy parameters and predicts energy load, consumption, and cost to grasp the time-interval-based energy trends. In addition, an optimal ESS charging and discharging operation is devised to manage excess power efficiently. The proposed model is validated on the case study containing data of 12 nanogrid houses. The outcomes yield that the proposed study holds significant potential in reducing the trading cost and optimally sharing the energy within the P2P network.
Highlights
S INCE the last decade, non-renewable energy resources such as coal, oil, and natural gas have been deemed primary sources for satiating the global energy demand [1] [2]
This study focuses on optimizing two important aspects related to energy trading, (1) optimize energy trading cost and (2) optimal energy exchange plan among nanogrid houses connected within a cluster
This article presented an intelligent energy trading model that contemplates important aspects overlooked by contemporary state-of-the-art in energy trading
Summary
S INCE the last decade, non-renewable energy resources such as coal, oil, and natural gas have been deemed primary sources for satiating the global energy demand [1] [2]. F. Qayyum et al.: Predictive Optimization based Energy Cost Minimization and Energy Sharing Mechanism for peer-to-peer Nanogrid Network cluster [10]. Day-ahead information forecasting in the energy trading sector has been proven helpful for energy providers to schedule the power loads or predict energy cost to ensure the balance between energy demand and production at an optimal cost [35]. We present an intelligent P2P nano-grid energy trading platform that integrates prediction outcomes implemented using BD-LSTM and PSObased optimization mechanisms to meet the real-time energy demands at a minimum cost. An intelligent time-aware energy sharing plan is devised, which decides the role of nanogrids as prosumer or consumer and prefers the use of PV generated energy over grid energy for local trading;.
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