Related Experiment Video
Updated: Jun 12, 2025
05:30
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
Published on: September 8, 2023
496
EnergyShare AI: Transforming P2P energy trading through advanced deep learning
Nouf Atiahallah Alghanmi1, Hanadi Alkhudhayr2
1Faculty of Computing and Information Technology, Department of Information Technology, King Abdulaziz University, Rabigh, 21911, Saudi Arabia.
Heliyon
|September 19, 2024
Summary
EnergyShare AI enables peer-to-peer (P2P) energy trading using machine learning, connecting homes with solar arrays, storage, and EVs. This system optimizes energy sharing, reducing costs and improving sustainability for consumers and prosumers.
Area of Science:
- Energy Systems Engineering
- Artificial Intelligence
- Sustainable Energy
Background:
- Peer-to-peer (P2P) energy trading is emerging as a transformative approach to energy demand management.
- Traditional energy systems face challenges in integrating distributed energy resources and optimizing energy flow.
Purpose of the Study:
- To introduce EnergyShare AI, a novel P2P energy exchange system leveraging machine learning.
- To demonstrate the system's capability in managing energy sharing among consumers and prosumers with solar arrays, energy storage systems (ESS), and electric vehicles (EVs).
Main Methods:
- Development and application of Deep Reinforcement Learning (DRL) algorithms within the EnergyShare AI framework.
- Optimization of bidirectional energy transfer, considering the roles of ESS and photovoltaic (PV) systems.
Main Results:
- EnergyShare AI significantly enhances energy management efficiency and reduces operational costs.
- The system facilitates substantial cost savings and promotes improved sustainability through efficient P2P energy exchange.
- Increased energy transfer observed between diverse household profiles and developmental stages.
Conclusions:
- EnergyShare AI offers a superior alternative to traditional linear integer programming models for P2P energy trading.
- The integration of DRL, ESS, and EVs is critical for efficient and cost-effective P2P energy transactions.
- Successful P2P energy trading contributes to a more sustainable and economically viable energy landscape.
Keywords:
And sustainabilityCost optimizationDeep learningElectric vehiclesEnergy storage systemsPeer-to-peer energy exchangeSolar power systemsMore Related Videos
Related Concept Videos
01:16Maximum Power Transfer
239
Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
By substituting the entire circuit with...
239
01:27Transformers in Distribution System
99
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
99
01:17Energy and Power Signals
268
In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
268
01:21Energy Losses in Transformers
843
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality, the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be the high resistance of the...
There are four main reasons for energy losses in transformers.
The first cause can be the high resistance of the...
843
01:23Maximum Power Flow and Line Loadability
97
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
97
01:12Power and Energy
688
The power and energy delivered to an element are subjects of great significance in the field of electrical engineering. It is a well-known fact that a 100-watt light bulb emits more light than a 60-watt one. Therefore, power and energy calculations play a crucial role in the analysis of electrical circuits.
Power, defined as the time rate of expending or absorbing energy, is quantified in units called watts (W). The relation between power and energy is mathematically given as
Power, defined as the time rate of expending or absorbing energy, is quantified in units called watts (W). The relation between power and energy is mathematically given as
688