Predictive Optimization Based Energy Cost Minimization and Energy Sharing Mechanism for Peer-to-Peer Nanogrid Network

Abstract

The concept of distributed generation has made photovoltaic an integral source of energy in smart grid systems, especially in peer-to-peer energy trading frameworks that exploit excess power to fulfill the energy requirements of consumers in cost-efficient and eco-friendly manner. It is believed that P2P energy trading will dominate a significant portion of research in forthcoming power generation systems due to excessive rise of energy demands across the globe. Despite a plethora of studies on energy optimization solutions in P2P trading, minimizing nanogrid energy trading cost and efficient energy sharing between consumers and prosumers are deemed challenging problems. This study overcomes essential issues overlooked by contemporary P2P energy trading models by introducing a predictive optimization-oriented nanogrid energy trading model. The study encompasses two stages: (1) predictive optimization model, which harnesses BD-LSTM-based forecasted energy parameters (energy load, energy consumption, and PV generation) that are later incorporated in PSO-enabled objective function to reduce nanogrid trading cost, (2) optimal energy sharing plan to trade energy efficiently by meeting the load requirements most of the time. The plan also decides optimal ESS charging and discharging operation to manage excess power efficiently. The proposed model is validated on the case study containing nanogrid houses data. The simulation provides detailed experiments by comparing the energy demand and response using the proposed energy sharing model. Outcomes yield that the energy sharing plan holds a significant potential to fulfill maximum energy requirements of nanogrid house in P2P cluster and significantly reduces the energy cost compared to grid.

Integrating Forecasting Service and Gen2 Blockchain Into a Local Energy Trading Platform to Promote Sustainability Goals

“…Reduced electricity costs are one of the major factors, according to [16], that can significantly affect P2P trading decisions in the LEM. This reality is acknowledged by the authors of [17], who then create an optimum P2P decisionforming method that ensures a reduction in LEM participants' energy bills. LEM participants are instructed to transact between feed-in-tariff (FiT) rates and time-of-use (ToU) tariffs in [18] to make P2P trading profitable for both buyers (who can both be prosumers and consumers) and sellers (who can be prosumers only).…”

Secure Hydrogen Production Analysis and Prediction Based on Blockchain Service Framework for Intelligent Power Management System

“…Additionally, blockchain enables peer-to-peer trading of green hydrogen, eliminating the need for intermediaries. Through smart contracts, producers and consumers can directly trade hydrogen and settle transactions securely and efficiently [12,13]. Moreover, blockchain technology facilitates the integration of renewable energy sources into green hydrogen production, enabling real-time monitoring of energy generation, consumption, and storage, optimizing the use of renewable energy sources [14].…”

“…Finance And Insurance Industry [29,30] The blockchain platform provides a secure and transparent ledger for storing and managing insurance data. Smart contracts automate many manual processes in insurance claims processing and underwriting.…”

Cooperative Application of Onboard Energy Storage and Stationary Energy Storage in Rail Transit Based on Genetic Algorithm

“…Similarly, Mundra also takes advantage of NSGA-II, achieving dual-objective optimization for the peak-to-average ratio of the total energy demand and electricity usage charge in smart grid [45]. On the other hand, Qayyum adopted PSO to minimize the nano grid energy trading cost while meeting energy demand [46]. Meanwhile, Li utilized Improved Particle Swarm Optimization (IPSO) to balance system economy and stability in the distribution grid [47].…”