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Abstract
While electric vehicles are generally seen as clean vehicles, they are not completely clean because the production of electricity might generate emissions as well. This paper on a solar powered electric vehicle charging station is a working solution to close the gap in achieving a truly renewable and clean vehicle. The currently scenario of today solar energy ecosystem is that, it is highly unstructured and localized. There are about 50 solar power plants in India but none of them are connect in a manner that there would be a method to perform analytical analysis of the solar energy produced. This paper aims to finding a possible method to connect the solar powered electric vehicle charging station and to perform analytical operations to increase efficiency of Solar Energy.
... The datasets contains both diverse spatial and temporal resolutions. Prominent examples include the city of Palo Alto, U.S. which is on a dense spatial resolution with observations going back to 2011 (CityofPaloAlto, 2021), and the city of Perth, Scotland which spans a spatially sparse area with observations from 2016 to 2019 (PerthandKinross, 2020). Two other prominent datasets are from Boulder, U.S. (Colorado, 2020) and Dundee, Scotland (CityOfDundee, 2019), which again spans a different spatial and temporal resolutions. ...
... The proposed models are empirically evaluated using data from the city of Palo Alto (CityofPaloAlto, 2021). The data consists of EV charging transactions at the locations Figure 2. The data set contains various metadata on the charging transaction such as Gasoline Savings, Charging time, Plug type. ...
Electric vehicles can offer a low carbon emission solution to reverse rising emission trends. However, this requires that the energy used to meet the demand is green. To meet this requirement, accurate forecasting of the charging demand is vital. Short and long-term charging demand forecasting will allow for better optimisation of the power grid and future infrastructure expansions. In this paper, we propose to use publicly available data to forecast the electric vehicle charging demand. To model the complex spatial-temporal correlations between charging stations, we argue that Temporal Graph Convolution Models are the most suitable to capture the correlations. The proposed Temporal Graph Convolutional Networks provide the most accurate forecasts for short and long-term forecasting compared with other forecasting methods.
... The computational experiments are conducted on a desktop computer with the following specifications: AMD E1-6015 APU with radeon (TM) R2 graphics, 1.4 GHz processor, operating system is Microsoft windows 10 with 4.00 GB of RAM, codes are executed using MATLAB2018a. The dataset used for the EVs' charging load forecasting is taken from [364]. The values of S, α, and β are set as 0.8, 0.6, and 0.4 in the simulations, respectively. ...
The rapid deployment of Electric Vehicles (EVs) and usage of renewable energy in day-to-day
activities of energy consumers have contributed toward the development of a greener smart
community. However, load balancing problems, security threats, privacy leakages, and lack
of incentive mechanisms remain unresolved in energy systems. Many approaches have been
used in the literature to solve the aforementioned challenges. However, these approaches are
not sufficient to obtain satisfactory results because of the resource and time-intensiveness of
the primitive cryptographic executions on the network devices. In most cases, energy trading
systems manage transactions using a centralized approach. This approach increases the risk of
a single point of failure and overall system cost. In this study, a blockchain based Local Energy
Market (LEM) model considering Home Energy Management (HEM) system and demurrage
mechanism is proposed to tackle the issue of a single point of failure in the energy trading
system. It allows both the prosumers and consumers to optimize their energy consumption
and minimize electricity costs. This model also allows end-users to shift their load to off-peak
hours and to use cheap energy from the LEM. On the other hand, users’ privacy leakages
are still not solved in blockchain and can limit its usage in many applications. This research
also proposes a blockchain based distributed matching and privacy-preservation model that
uses a reputation system for both residential homes and EVs to preserve users’ privacy and
efficiently allocate energy. A starvation free energy allocation policy is presented in the model.
In addition, a charging forecasting scheme for EVs is introduced that allows users to plan
and manage their intermittent EVs’ charging. Partial homomorphic encryption based on a
reputation system is used to hide the EVs users’ whereabouts. Identity Based Encryption (ID
Based encryption) technique is incorporated in the model to preserve the users’ information
privacy in the blockchain. Another bottleneck in the energy trading systems is to perform
efficient and privacy-preserving transactions. Therefore, an efficient and secure energy trading
model leveraging contract theory, consortium blockchain, and a reputation system is proposed.
Firstly, a secure energy trading mechanism based on consortium blockchain is developed.
Then, an efficient contract theory based incentive mechanism considering the information
asymmetry scenario is introduced. Afterwards, a reputation system is integrated to improve
x
transaction confirmation latency and block creation. Next, a shortest route and distance
algorithm is implemented in order to reduce the traveling distance and energy consumption
by the EVs during energy trading. Cheating attacks launched by both buyers and sellers
are also issues, which are still not resolved. Thus, a mutual-verifiable fairness mechanism
during energy trading based on timed commitment is presented. Proof-of-Energy Reputation
Generation (PoERG) and Proof-of Energy Reputation Consumption (PoERC) consensus
mechanisms are proposed to solve the high computational cost and huge monetary investment
issues created by Proof-of-Work (PoW) and Proof-of-Stake (PoS) existing mechanisms. The
mechanisms are developed based on reputation where energy trading transactions are audited,
validated, and added into blocks of a blockchain. In order to protect the proposed model
from impersonation attacks and minimize malicious validators, a two-stage peer-to-peer
secure energy trading model based on blockchain is proposed. The proposed model has two
layers: a mutual authentication process layer, and a secure and privacy-preserving energy
trading layer. Afterwards, an incentive-punishment algorithm is introduced to motivate
energy prosumers to contribute more energy in the proposed model. Next, a dynamic contract
theory based supply-demand ratio pricing scheme is proposed. The purpose of the proposed
pricing scheme is to solve the issues associated with the existing pricing scheme. Also, to
preserve the privacy of the actual energy consumption behavior of the trading participants.
Furthermore, storage overhead and delay in communication are challenges that need urgent
attention, especially in resource constrained devices for sustainable and efficient transactions.
Therefore, a consortium blockchain based vehicular system is proposed in this work for
secure communication and optimized data storage in Internet of Vehicles (IoV) network.
To secure the proposed system from active and passive attacks, an encryption technique
and an authentication mechanism are proposed based on public key encryption scheme and
hashing algorithm, i.e., Advanced Encryption Standard-256 and Rivest Shamir Adleman
(AES-256+RSA), and Keccak-256. It also protects the model from double spending attack.
Moreover, a cache memory technique is introduced to reduce service delay and high resource consumption. In the cache memory, the information of frequently used services is stored, which results in the reduction of service delivery delay. Simulation results show that all of the proposed models perform significantly better as compared to the existing schemes.
... Installing bollards, curbs, or wheel stops could help minimize equipment damage. Sub-surface heating of the charging space is another option, including hydronic (tubes under the pavement circulating heated water and anti-freeze) and electric radiant heating (low-voltage mats under the pavement heated by electricity) [15]. ...
The deployment of plug-in electric vehicles has the potential to help communities realize significant benefits. However, in cold weather climates, there are significant barriers to electric vehicle adoption. This paper conducts a market assessment in the Yukon Territory of Canada, including a review of the unique characteristics of the Yukon that may facilitate electric vehicle adoption, while also recognizing the challenges in the region. We find modest potential for electric vehicles in the Yukon, with comparably modest GHG reduction potential.
This paper studies the heterogeneous energy cost and charging demand impact of autonomous electric vehicle (EV) fleet under different ambient temperature. A data-driven method is introduced to formulate a two-dimensional grid stochastic energy consumption model for electric vehicles. The energy consumption model aids in analyzing EV energy cost and describing uncertainties under variable average vehicle trip speed and ambient temperature conditions. An integrated eco-routing and optimal charging decision making framework is designed to improve the capability of autonomous EV’s trip level energy management in a shared fleet. The decision making process helps to find minimum energy cost routes with consideration of charging strategies and travel time requirements. By taking advantage of derived models and technologies, comprehensive case studies are performed on a data-driven simulated transportation network in New York City. Detailed results show us the heterogeneous energy impact and charging demand under different ambient temperature. By giving the same travel demand and charging station information, under the low and high ambient temperature within each month, there exist more than 20% difference of overall energy cost and 60% difference of charging demand. All studies will help to construct sustainable infrastructure for autonomous EV fleet trip level energy management in real world applications.
This study introduces an optimal charging decision making framework for connected and automated electric vehicles under a personal usage scenario. This framework aims to provide charging strategies, i.e. the choice of charging station and the amount of charged energy, by considering constraints from personal daily itineraries and existing charging infrastructure. A data-driven method is introduced to establish a stochastic energy consumption prediction model with consideration of realistic uncertainties. This is performed by analyzing a large scale electric vehicle data set. A real-time updating method is designed to construct this prediction model from new consecutive data points in an adaptive way for real-world applications. Based on this energy cost prediction framework from real electric vehicle data, multistage optimal charging decision making models are introduced, including a deterministic model for average outcome decision making and a robust model for safest charging strategies. A dynamic programming algorithm is proposed to find the optimal charging strategies. Detailed simulations and case studies demonstrate the performance of the proposed algorithms to find optimal charging strategies. They also show the potential capability of connected and automated electric vehicles to reduce the range anxiety and charging infrastructure dependency.
The fundamental economic reality of fossil fuels is that such fuels are found only in a relatively small number of locations across the globe, yet are consumed everywhere. The economic reality, by contrast, is that solar resources are available, in varying degrees, all over the world. Fossil fuel and solar resource use are thus poles apart-not just because of the environmental effects, but also because of the fundamentally different economical, logical and differing political, social and cultural consequences. These differences must be acknowledged if the full spectrum of opportunity for solar resources is to be exploited. Therefore, this study concentrates on solar power as a renewable source of energy. It has many benefits compared to fossil fuels. It is clean and green, non-polluting and everlasting energy. For this reason it has attracted more attention than other alternative sources of energy in recent years. Many energy economists say that solar energy is going to play an increasingly important role in all our lives. To highlight the importance of such a source of energy becomes not only important but also inevitable. This paper analyzes the determining factors of solar energy usage and also analyse the cost benefit of the different solar energy devises usage.
We review the technical progress made in the past several years in the area of mono- and polycrystalline thin-film photovoltaic (PV) technologies based on Si, III–V, II–VI, and I–III–VI2 semiconductors, as well as nano-PV. PV electricity is one of the best options for sustainable future energy requirements of the world. At present, the PV market is growing rapidly at an annual rate of 35–40%, with PV production around 10.66 GW in 2009. Si and GaAs monocrystalline solar cell efficiencies are very close to the theoretically predicted maximum values. Mono- and polycrystalline wafer Si solar cells remain the predominant PV technology with module production cost around $1.50 per peak watt. Thin-film PV was developed as a means of substantially reducing the cost of solar cells. Remarkable progress has been achieved in this field in recent years. CdTe and Cu(In,Ga)Se2 thin-film solar cells demonstrated record efficiencies of 16.5% and almost 20%, respectively. These values are the highest achieved for thin-film solar cells. Production cost of CdTe thin-film modules is presently around $0.76 per peak watt.
Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers. Many projects at Google store data in Bigtable, including web indexing, Google Earth, and Google Finance. These applications place very different demands on Bigtable, both in terms of data size (from URLs to web pages to satellite imagery) and latency requirements (from backend bulk processing to real-time data serving). Despite these varied demands, Bigtable has successfully provided a flexible, high-performance solution for all of these Google products. In this paper we describe the simple data model provided by Bigtable, which gives clients dynamic control over data layout and format, and we describe the design and implementation of Bigtable.
Reliability Analysis of Residential Photovoltaic Systems
- A Garro
A. Garro, 'Reliability Analysis of Residential Photovoltaic Systems', International Conference on Renewable Energies and Power Quality (ICREPQ'11)