AI-powered Energy
Harvesting and Management [AI を活用した分散型エネルギーハーベスティングと管理]
A Virtual Power
Plant (VPP) is a network of distributed power generating
units, flexible power consumers, and storage systems. A
VPP balances the load on
the grid by allocating the power generated by different linked
units during periods of peak load. Demand-side energy
equipment, such as Electric Vehicles (EVs) and mobile
robots, can also balance the energy supply-demand when
effectively deployed. However, fluctuation of the power
generated by the various power units makes the supply power
balance a challenging goal. Moreover, the communication
security between a VPP aggregator and end facilities is
critical and has not been carefully investigated.
In this project, we collaborate with Aizu
Computer Science Laboratories, Inc. and Banpu Japan to
develop an AI-enabled, block-chain-based Electric
Vehicle (EV) integration system for power management
in a smart grid platform based on EV and solar
carport. We have developed a low-power AI-chip
and various software tools for EV charge prediction,
in which the EV fleet is employed as a consumer and as
a supplier of electrical
energy.
-
Z.
Wang, A. Ben Abdallah, ''A Robust
Multi-stage Power Consumption Prediction
Method in a Semi-decentralized
Network of Electric Vehicles,'' IEEE
Access, 2022. DOI:
10.1109/ACCESS.2022.3163455
A Virtual Power Plant (VPP) balances the load
on a power grid by allocating power generated
by various interconnected units during periods
of peak demand. In addition, demand-side
energy devices such as Electric Vehicles (EVs)
and mobile robots can also balance energy
supply and demand when effectively deployed.
However, the fluctuation of energy generated
by renewable resources makes balancing energy
supply a challenging goal. This paper proposes
a semi-decentralized robust network of
electric vehicles (NoEV) integration system
for power management in a smart grid platform.
The proposed approach integrates an aggregator
with EV fleets into a blockchain framework.
The EVs execute a multi-stage algorithm to
predict the power consumption based on a novel
federated learning algorithm named Federated
Learning for Qualified Local Model Selection
(FL-QLMS). From the evaluation results, the
proposed system requires 35% fewer
transactions in short intervals and
propagation delays than the previous
approaches and achieves better network
efficiency while maintaining a high level of
security. Moreover, NoEV achieves a 5.7% lower
root mean square error (RMSE) than the
conventional approach for power consumption
prediction, which is a significant
improvement. In addition, the FL-QLMS approach
outperforms state-of-the-art methods in terms
of robustness to client-side attacks. The
evaluation results also show that the
performance of FL-QLMS is not affected when
10% to 40% percent of the models are
manipulated.
-
Z.
Wang, M. Ogbodo, H. Huang, C. Qiu, M.
Hisada, A. Ben Abdallah, "AEBIS:
AI-Enabled Blockchain-based Electric Vehicle
Integration System for Power Management in
Smart Grid Platform," IEEE
Access, vol. 8, pp. 226409-226421, 2020,
doi:10.1109/ACCESS.2020.3044612.
A Virtual Power Plant (VPP) is a network of
distributed power generating units, flexible
power consumers, and storage systems. A VPP
balances the load on the grid by allocating
the power generated by different linked units
during periods of peak load. Demand-side
energy equipment, such as Electric Vehicles
(EVs) and mobile robots, can also balance the
energy supply-demand when effectively
deployed. However, fluctuation of the power
generated by the various power units makes the
supply power balance a challenging goal.
Moreover, the communication security between a
VPP aggregator and end facilities is critical
and has not been carefully investigated. This
paper proposes an AI-enabled, blockchain-based
electric vehicle integration system, named
AEBIS for power management in a smart grid
platform. The system is based on an artificial
neural-network and federated learning
approaches for EV charge prediction, in which
the EV fleet is employed as a consumer and as
a supplier of electrical energy within a VPP
platform. The evaluation results show that the
proposed approach achieved high power
consumption forecast with R 2 score
of 0.938 in the conventional training
scenario. When applying a federated learning
approach, the accuracy decreased by only 1.7%.
Therefore, with the accurate prediction of
power consumption, the proposed system
produces reliable and timely service to supply
extra electricity from the vehicular network,
decreasing the power fluctuation level. Also,
the employment of AI-chip ensures a
cost-efficient performance. Moreover,
introducing blockchain technology in the
system further achieves a secure and
transparent service at the expense of an
acceptable memory and latency cost.
-
[特
許第6804072 号] (2020.12.04) Abderazek Ben
Abdallah, Masayuki Hisada, ''Virtual
Power Platform Control System [仮 想
発電所制 御システム]'', 特 願
2020-033678号 (2020.02.28)
-
Abderazek
Ben Abdallah,Wang Zhishang, Khanh N. Dang,
Masayuki Hisada, ''EV Power Consumption
Prediction Method and System for
Power Management in Smart Grid [
スマートグリッドにおける電力管理のためのEV消 費電力予測 方法とシステム ]'',
特願2022.
Trustworthy
Campus Energy Trading
Method and System [信頼できるキャンパスエネルギー取引方法とシステム]
Energy
trading policies are revolutionizing the efforts and
policies geared toward addressing global carbon
emissions and protecting the environment. Smart grids
and electric vehicles (EVs) are energy-saving tools
for efficient power management. Although EVs can act
as both energy consumers and suppliers, the effort
required to balance the energy supply and demand in
typical centralized trading systems inevitably reduces
trading reliability. Another challenge is distributing
EVs’ energy rationally to achieve better demand
response and energy utilization. This project
investigates a secure block-chain-based energy trading
system using the vehicle-to-grid (V2G) network. The
system combines a blockchain of energy exch
anges and a
blockchain of EVs with the distinct transmission of
energy requests and offers.
-
Y.
Liang, Z. Wang and A. Ben Abdallah, "Robust
Vehicle-to-Grid Energy Trading Method Based on
Smart Forecast and Multi-Blockchain Network,"
in IEEE Access, vol. 12, pp. 8135-8153,
2024, doi:10.1109/ACCESS.2024.3352631
In the present era, energy issues are a
significant concern, and the energy trading market
is the crucial sector to facilitate supply-demand
balance and sustainable development. For better
demand response and grid balancing,
vehicle-to-grid (V2G) technology is rapidly
gaining importance in energy markets. To narrow
the gap between ideal V2G goals and actual
applications needs, energy trading system has to
overcome the challenges of over-centralized
structure, inflexible timeline adaptation, limited
market scale and energy efficiency, excessive
feedback time costs, and low rate of economic
return. To address these issues and ensure a
secure energy market, we propose a decentralized
intelligent V2G system called V2G Forecasting and
Trading Network (V2GFTN) to achieve efficient and
robust energy trading in campus EV networks. A
multiple blockchain structure is proposed in
V2GFTN to ensure trading security and data privacy
between energy requests and offers. V2GFTN also
integrates energy forecasting functions for EVs
with a smart energy trading and EV allocation
mechanism called SRET so that the EVs with driving
tasks can supply their extra power back to the
grid and achieve higher energy efficiency and
economic profit. Through rigorous experimentation
and compared with equivalent studies, V2GFTN
system has demonstrated higher economic profit and
energy demand fill rate by up to 1.6 times and 1.9
times than the state-of-the-art V2G approaches.
-
Y.
Liang, Z. Wang and A. Ben Abdallah, "V2GNet:
Robust Blockchain-Based Energy Trading
Method and Implementation in
Vehicle-to-Grid Network," in IEEE
Access, vol. 10, pp. 131442-131455, 2022,
doi: 10.1109/ACCESS.2022.3229432.
Nowadays, energy trading policies are
revolutionizing the efforts and policies
geared toward addressing global carbon
emissions and protecting the environment.
Smart grids and electric vehicles (EVs) are
energy-saving tools for efficient power
management. Although EVs can act as both
energy consumers and suppliers, the effort
required to balance the energy supply and
demand in typical centralized trading systems
inevitably reduces trading reliability.
Another challenge is distributing EVs’ energy
rationally to achieve better demand response
and energy utilization. To manage the market
securely and efficiently, we propose V2GNet, a
blockchain-based energy trading system using
the vehicle-to-grid (V2G) network. The system
combines a blockchain of energy exchanges
(BoE) and a blockchain of EVs (BoEV), with the
distinct transmission of energy requests and
offers. Furthermore, to consider energy
management from an economic viewpoint, we
address the attack issue by proposing a robust
energy trading (RET) algorithm. The proposed
system demonstrates high robustness to
malicious attacks. Our experimental results
show that the RET reduces 30% energy loss when
20% of consumers are attacked. Moreover,
malicious exchanges are excluded progressively
from the trading market during each trading
round. Also, the RET algorithm achieves better
energy fulfillment and higher profit compared
to state-of-the-art approaches.
|