Energy Sharing Research Articles

EnergyShare AI: Transforming P2P energy trading through advanced deep learning

Peer-to-peer (P2P) energy trading is an innovative concept poised to transform energy demand management and utilization. EnergyShare AI is a powerful peer-to-peer energy exchange system that operates on a P2P model that integrates advanced machine learning with distributed energy sharing. This paper presents EnergyShare AI, a technology that connects consumers and prosumers through solar arrays, energy storage systems (ESS), and electric vehicles (EVs). Using Deep Reinforcement Learning (DRL) algorithms, Energy Share AI significantly improves energy management efficiency and substantially reduces costs. Our approach offers several advantages over traditional linear integer programming models, particularly in optimizing bidirectional energy transfer involving EVs and highlighting the critical role of ESS and photovoltaic (PV) systems in facilitating efficient P2P energy trading. Our research results show that successful P2P exchange can lead to significant cost savings and improved sustainability, thereby increasing the amount of energy transferred between different household profiles and stages of human development.

Developing Resilient Peer-to-Peer Energy Sharing Scheme Using “N-1” Contingency

Developing Resilient Peer-to-Peer Energy Sharing Scheme Using “N-1” Contingency

A Cyber-Physical–Social System Approach for Peer-to-Peer Energy Sharing

A Cyber-Physical–Social System Approach for Peer-to-Peer Energy Sharing

Coordinated Optimization of Hydrogen-Integrated Energy Hubs with Demand Response-Enabled Energy Sharing

Coordinated Optimization of Hydrogen-Integrated Energy Hubs with Demand Response-Enabled Energy Sharing

Optimizing Thermal Energy Sharing in Smart District Heating Networks

Optimizing Thermal Energy Sharing in Smart District Heating Networks

Decentralized utilization of distributed energy storage resources for simultaneous frequency regulation in a microgrid

A microgrid has emerged for the transformation from centralized into localized electricity generation, which increases the opportunity for renewable-based distributed generators (DGs) applications such as photovoltaic (PV) and wind as well as small-scale conventional generation units. PV and wind integration provides environmental, technical, and economic benefits. However, the low inertia of the PV system and decoupling due to the presence of the power electronics converter in the wind generation system led to a significant declination in the microgrid's inertia, which causes an excessive frequency nadir and a high rate of change of frequency (ROCOF) in the presence of disturbances. The microgrid's inertia can be enhanced by storing or releasing energy within the microgrid. The wind generation system has intrinsic kinetic energy stored in the rotating parts, while the PV system's sorted energy is improved by introducing an external supercapacitor on the DC link. The aforementioned energy storage elements provide limited energy resources, which should be employed in a proper manner. This article proposes a control scheme to boost the cooperation of wind and PV generation systems for the enhancement of inertia and frequency support. Therefore, the proposed scheme prevents excessive stress on individual generators and improves the utilization of available energy resources. For example, at load increase by 0.5 MW, with frequency support using PV only or wind only, the frequency deviation is enhanced by 44.4 % and 29.5 %, respectively. While for both PV and wind support the frequency simultaneously, the frequency deviation is enhanced by 53.3 %. In addition, with the cooperation of PV and wind, the required energy from PV and wind is reduced by 29.5 % and 13.7 % respectively and the rate of change of frequency is enhanced by 61.9 %. The energy share among PV and wind generation systems is investigated and verified by simulations; tuning formulas are also developed to attain predefined energy support with equal energy sharing among different resources. For instance, with a non-equal energy share strategy and load increases by 0.5 MW, the wind delivers almost 1.6 times the energy delivered from the PV. Hence, more stress occurs on the wind generation compared to PV. While, using equal energy share strategy, the required energy from wind is about 1.03 times the energy delivered from the PV. The proposed control scheme is applied to an AC microgrid that comprises a thermal generation plant, a PV generation system, and a doubly-feed induction generator-based wind generation system. To verify the effectiveness of the proposed scheme, different simulation cases considering load change, wind speed variation, irradiance variations, and weak grid integration are implemented. When compared to individual frequency support, the proposed scheme provides a superior response.

Post Facto Calculation of Peer to Peer Transactions: Proportional and Preferential Energy Sharing Models

Global rooftop solar grows at a rate of 20.14% annually, according to global solar consortium energypedia. Conventionally prosumers sell excess solar generated to the utility grid using a feed-in-tariff policy. Feed-in-tariff policy is effective if the grid is the only customer, but prosumers with peer-to-peer (P2P) tariff mechanism yield better profits to prosumers and energy savings to consumers. Existing P2P energy-sharing models are computationally intensive and have scalability issues. Further it’s difficult to implement in the real world. To overcome these problems, this paper proposes two post-facto P2P energy-sharing models. One is the preferential energy sharing model (PES) where consumer and prosumer pairs are ranked based on energy generated and consumed. The second model is the proportional energy sharing model (PRES) where consumer energy generated and consumed are shared proportionally. Both models are illustrated using seasonal variations by comparing with feed-in tariff policy and observed an effective reduction of bills for consumers and increased profit of prosumers. The results concluded that microgrids with excess solar will profit more when they are in the PRES model, whereas microgrids with significant demand will benefit when they are in the PES model. Further, PRES model executes in 107 milliseconds, and the PES model executes in 52 milliseconds. These results show that the proposed models are computationally less intensive and easily scalable.

Optimal Pricing and Energy Sharing of EV Charging Stations With an Augmented User Equilibrium Model

With the proliferation of electrical vehicles (EVs), tariff-based monetary incentives (e.g., charging service fees, electricity prices) have been widey adopted by network operators to efficiently manage EV charging demands. The imposed tariff would enforce drivers to bear an extra travel expense for satisfying the charging demand distributions favored by network operators. Accurately modeling users' responsive behavior to price signals is a prerequisite for properly designing the tariff and achieving the desired goal. The present work bridges the gap of existing studies by proposing an augmented user equilibrium (AUE) model that captures EV users' equity-aware decision makings and closed-loop commuting behavior. The model enables network operators to consider 1) the acceptability of EV travelers on regulation policies and 2) the elasticity of EV charging demand as well its rebound effect. Based on the AUE model, we further investigate the optimal pricing and energy sharing strategy of the charging network operator, who aims to maximize his/her profit by tapping the flexibility of EVs and scheduling energy transactions among charging stations while respecting the operating constraints of power and transportation networks. Finally, a relaxation based iterative algorithm is devised to efficiently solve the mathematical problem with complementarity conditions. Numerical results on two test systems validate the effectiveness and merits of the proposed models and methods.

Backstage Matters: Collective Energy and Information Sharing on Global Teams

Backstage Matters: Collective Energy and Information Sharing on Global Teams

A New Management Method for Reliable Peer-to-Peer Energy Sharing in Power Distribution Systems

A New Management Method for Reliable Peer-to-Peer Energy Sharing in Power Distribution Systems

MicroTrust: Empowering Microgrids With Smart Peer-to-Peer Energy Sharing Through Trust Management in IoT

MicroTrust: Empowering Microgrids With Smart Peer-to-Peer Energy Sharing Through Trust Management in IoT

Hierarchical Game for Coupled Power System with Energy Sharing and Transportation System

Hierarchical Game for Coupled Power System with Energy Sharing and Transportation System

Optimal Planning of Multi-Microgrid System with Shared Energy Storage Based on Capacity Leasing and Energy Sharing

Optimal Planning of Multi-Microgrid System with Shared Energy Storage Based on Capacity Leasing and Energy Sharing

Overview of Vehicle-to-Vehicle Energy Sharing Infrastructure

Overview of Vehicle-to-Vehicle Energy Sharing Infrastructure

V2V-ESP: Vehicle-to-Vehicle Energy Sharing Privacy Protection Scheme Based on SDP Algorithm

V2V-ESP: Vehicle-to-Vehicle Energy Sharing Privacy Protection Scheme Based on SDP Algorithm

A Preference-Informed Energy Sharing Framework for a Renewable Energy Community

A Preference-Informed Energy Sharing Framework for a Renewable Energy Community

Privacy-Preserving Energy Sharing Among Cloud Service Providers via Collaborative Job Scheduling

Privacy-Preserving Energy Sharing Among Cloud Service Providers via Collaborative Job Scheduling

Day-Ahead P2P Energy Sharing Strategy Among Energy Hubs Considering Flexibility of Energy Storage and Loads

Day-Ahead P2P Energy Sharing Strategy Among Energy Hubs Considering Flexibility of Energy Storage and Loads

Vehicle-to-Vehicle Energy Sharing Scheme: A Privacy-Preserving Solution based on Local Differential Privacy Method

Vehicle-to-Vehicle Energy Sharing Scheme: A Privacy-Preserving Solution based on Local Differential Privacy Method

Optimal Energy Sharing in Hybrid Microgrid System Using Battery Energy Storage

A smart micro grid technology application facilitates the integration of renewable energy and increase its penetration. A smart grid is an electrical network which is built on advanced technology that uses dual-way digital communication to transmit electricity to buyers. The smart grid was created with the aim of using smart meters to overcome the problems faced by traditional grids. Microgrids allow for the integration of multiple renewable energy sources at different levels, improving the power system’s reliability, sustainability and efficiency. Remote places, spacecraft and maritime applications all use DC microgrids. Solar photovoltaic (PV) systems, wind energy, fuel cells, battery management systems, supercapacitors, and loads make up a DC microgrid. In this paper, some of the interesting approaches for optimal energy sharing in the hybrid microgrid are discussed.