Blockchain Technology Applied to Energy Demand Response Service Tracking and Data Sharing

Demand response (DR) services have the potential to enable large penetration of renewable energy by adjusting load consumption, thus providing balancing support to the grid. The success of such load flexibility provided by industry, communities, or prosumers and its integration in electricity markets, will depend on a redesign and adaptation of the current interactions between participants. New challenges are, however, bound to appear with the large scale contribution of smaller assets to flexibility, including, among others, the dispatch coordination, the validation of delivery of the DR provision, and the corresponding settlement of contracts, while assuring secured data access among interested parties. In this study we applied distributed ledger (DLT)/blockchain technology to securely track DR provision, focusing on the validation aspect, assuring data integrity, origin, fast registry, and sharing within a permissioned system, between all relevant parties (including transmission system operators (TSOs), aggregators, distribution system operators (DSOs), balance responsible parties (BRP), and prosumers). We propose a framework for DR registry and implemented it as a proof of concept on Hyperledger Fabric, using real assets in a laboratory environment, in order to study its feasibility and performance. The lab set up includes a 450 kW energy storage system, scheduled to provide DR services, upon a system operator request and the corresponding validations and verifications are done, followed by the publication on a blockchain. Results show the end to end execution time remained below 1 s, when below 32 requests/sec. The smart contract memory utilization did not surpass 1% for both active and passive nodes and the peer CPU utilization, remained below 5% in all cases simulated (3, 10, and 28 nodes). Smart Contract CPU utilization remained stable, below 1% in all cases. The performance of the implementation showed scalable results, which enables real world adoption of DLT in supporting the development of flexibility markets, with the advantages of blockchain technology.

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Monitoring of a Grid Storage Virtualization Service

International Journal of Grid and High Performance Computing ◽

10.4018/jghpc.2013010104 ◽

2013 ◽

Vol 5 (1) ◽

pp. 53-69

Author(s):

Jacques Jorda ◽

Aurélien Ortiz ◽

Abdelaziz M’zoughi ◽

Salam Traboulsi

Keyword(s):

Monitoring System ◽

Data Storage ◽

Large Scale ◽

Distributed Storage ◽

Storage System ◽

Data Access ◽

Data Placement ◽

Workload Prediction ◽

Storage Virtualization

Grid computing is commonly used for large scale application requiring huge computation capabilities. In such distributed architectures, the data storage on the distributed storage resources must be handled by a dedicated storage system to ensure the required quality of service. In order to simplify the data placement on nodes and to increase the performance of applications, a storage virtualization layer can be used. This layer can be a single parallel filesystem (like GPFS) or a more complex middleware. The latter is preferred as it allows the data placement on the nodes to be tuned to increase both the reliability and the performance of data access. Thus, in such a middleware, a dedicated monitoring system must be used to ensure optimal performance. In this paper, the authors briefly introduce the Visage middleware – a middleware for storage virtualization. They present the most broadly used grid monitoring systems, and explain why they are not adequate for virtualized storage monitoring. The authors then present the architecture of their monitoring system dedicated to storage virtualization. We introduce the workload prediction model used to define the best node for data placement, and show on a simple experiment its accuracy.

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Blockchain in Energy Efficiency: Potential Applications and Benefits

Energies ◽

10.3390/en12173317 ◽

2019 ◽

Vol 12 (17) ◽

pp. 3317 ◽

Cited By ~ 12

Author(s):

Asma Khatoon ◽

Piyush Verma ◽

Jo Southernwood ◽

Beth Massey ◽

Peter Corcoran

Keyword(s):

Energy Efficiency ◽

Information And Communication Technologies ◽

Asset Management ◽

Large Scale ◽

Energy System ◽

Energy Trading ◽

Trading Systems ◽

Energy Company ◽

Blockchain Technology ◽

Smart Contract

Blockchain technology is ready to disrupt nearly every industry and business model, and the energy sector is no exception. Energy businesses across the world have already started exploring the use of blockchain technology in large-scale energy trading systems, peer-to-peer energy trading, project financing, supply chain tracking, and asset management among other applications. Information and Communication Technologies (ICTs) recently started revolutionizing the energy landscape, and now blockchain technology is providing an additional opportunity to make the energy system more intelligent, efficient, transparent, and secure in the longer term. The idea of this paper is to examine more closely the use of blockchain technology for its possible application in the energy efficiency industry and to determine how it could make energy efficiency markets more secure and transparent in the longer term. This paper examines in detail the key benefits and implications of using blockchain in the energy efficiency sector through the presentation and discussion of two case studies as possible blockchain applications—(i) the UK Energy Company Obligation scheme and (ii) the Italian White Certificate Scheme. We have presented how the key issues around trading energy efficiency savings—correctly estimating the savings, data transparency among stakeholders, and inefficient administrative processes—can be solved through the application of a blockchain-based smart contract system. Finally, this paper presents an implementation of a smart contract for trading of energy-saving certificates achieved via execution of smart contract transactions on the Ethereum blockchain.

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An approach towards developments of smart COVID-19 patient's management and triaging using blockchain framework

10.21203/rs.3.rs-70583/v1 ◽

2020 ◽

Author(s):

Pralay Kumar Lahiri ◽

Riman Mandal ◽

Sourav Banerjee ◽

Utpal Biswas

Keyword(s):

Health Care ◽

Information Management ◽

Health Care Systems ◽

Storage System ◽

Information Management System ◽

Government Agencies ◽

Public Health Emergencies ◽

Blockchain Technology ◽

Smart Contract ◽

Care Systems

Abstract The explosive epidemic of the coronavirus (COVID-19) has exposed the constraints in health care systems to handle public health emergencies. It's evident that adopting innovative technologies reminiscent of blockchain will facilitate in eective designing operations and resource deployments. Within the health care sector to improve the information management system by reducing delays in regulative approvals, communication between dierent stakeholders of the chain with the help of blockchain technology. To ensure authenticity of the information collected from public and government agencies, blockchain based system plays an important role. This paper tends to review implementation of blockchain application and opportunities in combating the COVID-19 pandemic. To trace according information involving in recent cases, deaths and recovered cases maintaining through blockchain storage system that has been proposed and implemented blockchain system based on Ethereum smart contract. An interactive model and respective algorithm has been explained with detailed analysis on information integrity, security, transparency and traceability.

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A Memory Architecture Design for High-Performance Cloud Computing

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.532-533.677 ◽

2012 ◽

Vol 532-533 ◽

pp. 677-681

Author(s):

Li Qun Luo ◽

Si Jin He

Keyword(s):

High Performance ◽

Large Scale ◽

Storage System ◽

Data Access ◽

Memory Model ◽

Low Latency ◽

Memory Architecture ◽

Distributed Environment ◽

Access Data ◽

Performance Computing

The advent of cloud is drastically changing the High Performance Computing (HPC) application scenarios. Current virtual machine-based IaaS architectures are not designed for HPC applications. This paper presents a new cloud oriented storage system by constructing a large scale memory grid in a distributed environment in order to support low latency data access of HPC applications. This Cloud Memory model is built through the implementation of a private virtual file system (PVFS) upon virtual operating system (OS) that allows HPC applications to access data in such a way that Cloud Memory can access local disks in the same fashion.

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A Large Site Centralized Data Classification Strategy Based on User Value

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1030-1032.1619 ◽

2014 ◽

Vol 1030-1032 ◽

pp. 1619-1622

Author(s):

Bing Xin Zhu ◽

Jing Tao Li

Keyword(s):

High Performance ◽

Large Scale ◽

Storage System ◽

Data Classification ◽

Data Access ◽

Heat Index ◽

Storage Devices ◽

Hierarchical Storage ◽

Classification Strategy ◽

User Value

In large-scale storage system, variety of calculations, transfer, and storage devices both in performance and in characteristics such as reliability, there are physical differences. While operational load data access for storage devices is also not uniform, there is a big difference in space and time. If all the data is stored in the high-performance equipment is unrealistic and unwise. Hierarchical storage concept effectively solves this problem. It is able to monitor the data access loads, and depending on the load and application requirements based on storage resources optimally configure properties [1]. Traditional classification policy is generally against file data, based on frequency of access to files, file IO heat index for classification. This paper embarks from the website user value concept, aiming at the disadvantages of traditional data classification strategy, puts forward the centralized data classification strategy based on user value.

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Design and Evaluation of Bupt-Cloud-Storage System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.566.560 ◽

2012 ◽

Vol 566 ◽

pp. 560-567

Author(s):

Li Feng Zhou ◽

Wen Bin Yao ◽

De Yan Jiang ◽

Cong Wang

Keyword(s):

Cloud Storage ◽

Large Scale ◽

Storage System ◽

Data Access ◽

Experimental Results ◽

Mass Storage ◽

Storage Devices ◽

Concurrent Control

Cloud storage, which is composed of a large number storage devices and servers, provides large-scale flexible storage services through Internet. BCSS (Bupt-Cloud-Storage System) based on some cheap irresponsible PCs is designed as a mass storage platform to offer high reliable and available storage services. Meanwhile, it improves performance of data access by providing support of multi-user concurrent control. The experimental results verify efficiency of storage services and the performance of BCSS.

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Optimal Operation of the Campus Microgrid considering the Resource Uncertainty and Demand Response Schemes

Mathematical Problems in Engineering ◽

10.1155/2021/5569701 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Hafiz Abd ul Muqeet ◽

Hafiz Mudassir Munir ◽

Aftab Ahmad ◽

Intisar Ali Sajjad ◽

Guang-Jun Jiang ◽

...

Keyword(s):

Power Systems ◽

Demand Response ◽

Solar Irradiance ◽

Distribution System ◽

Energy Demand ◽

Mathematical Problem ◽

Optimal Solution ◽

Optimal Operation ◽

Mixed Integer ◽

Proposed Model

Present power systems face problems such as rising energy charges and greenhouse gas (GHG) releases. These problems may be assuaged by participating distributed generators (DGs) and demand response (DR) policies in the distribution system (DS). The main focus of this paper is to propose an energy management system (EMS) approach for campus microgrid (µG). For this purpose, a Pakistani university has been investigated and an optimal solution has been proposed. Conventionally, it contains electricity from the national grid only as a supply to fulfil the energy demand. Under the proposed setup, it contains campus owned nondispatchable DGs such as solar photovoltaic (PV) panels and microturbines (MTs) as dispatchable sources. To overcome the random nature of solar irradiance, station battery has been integrated as energy storage. The subsequent nonlinear mathematical problem has been scheduled by mixed-integer nonlinear programming (MINLP) in MATLAB for saving energy cost and battery aging cost. The framework has been validated under deterministic and stochastic environments. Among random parameters, solar irradiance and load have been taken into consideration. Case studies have been carried out considering the demand response strategies to analyze the proposed model. The obtained results show that optimal management and scheduling of storage in the presence of DGs mutually benefit by minimizing consumption cost (customer) and grid load (utility) which show the efficacy of the proposed model. The results obtained are compared to the existing literature and a significant cost reduction is found.

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Integrated Transportation, Building, and Electricity System Models to Explore Decarbonization Pathways in Regina, Saskatchewan

Frontiers in Sustainable Cities ◽

10.3389/frsc.2021.674848 ◽

2021 ◽

Vol 3 ◽

Author(s):

Madeleine Seatle ◽

Lauren Stanislaw ◽

Robert Xu ◽

Madeleine McPherson

Keyword(s):

Renewable Energy ◽

Spatial Resolution ◽

Demand Response ◽

Energy Demand ◽

Large Scale ◽

Travel Demand ◽

Electricity Demand ◽

Demand Side ◽

Battery Storage ◽

Electricity System

In Canada, the majority of urban energy demand services the transportation or building sectors, primarily with non-renewable energy sources including gasoline and natural gas. As a result, these two sectors account for 70% of urban greenhouse gas (GHG) emissions. The objective of this paper is to explore the potential for co-benefits when simultaneously electrifying transportation and building demand sectors while expanding variable renewable energy (VRE) production. The investigation uses a novel integrated framework of the transportation, building, and electricity sectors to represent the operational implications of demand side flexibility on both the demand and supply side of the energy system. This original approach allows for very fine temporal and spatial resolution within models, while still performing a multi-sector analysis. First, the activity-based transportation model produces passenger travel demand profiles, allowing for investigation of potential electricity demand and demand response from electric vehicles with high spatial and temporal resolution. Second, the archetype-based building model predicts electricity demand of the residential building sector, allowing for investigation into demand-side management strategies such as load-shifting, building retrofits, and changes in appliance technology. Third, the electricity system production cost dispatch model is used to model the operations of Regina's electricity grid and has a spatial resolution capable of assessing individual and connected positive energy districts as well as VRE integration. Through linking of these three models, the effects of consumer flexibility in transportation and building energy demand are explored, especially in the context of introducing much needed flexibility for large-scale VRE integration. A utility-controlled demand response (DR) strategy is explored as means for Regina to reach their renewable target, along with battery storage. Various pathways to Regina's target are considered, based on the various proposed scopes of the target. The results show that Regina can meet their renewable target with large-scale rooftop solar and wind capacity. DR strategies are marginally effective in aiding toward the renewable target, but, when implemented in conjunction with battery storage, is able to get Regina to within 1% of their renewable target.

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Scheduling And Sizing of Campus Microgrid Considering Demand Response And Economic Analysis

10.21203/rs.3.rs-877951/v1 ◽

2021 ◽

Author(s):

Muhammad Shahzad Pansota ◽

Haseeb Javed ◽

Abdul Muqeet ◽

Muhammad Irfan ◽

Moazzam Shehzad ◽

...

Keyword(s):

Energy Storage ◽

Demand Response ◽

Distribution System ◽

Large Scale ◽

Economic Benefits ◽

Mixed Integer ◽

General Effect ◽

Energy Prices ◽

Solar Pv ◽

Diesel Generator

Abstract Background: Current energy systems face multiple problems related to inflation in the energy prices, reduction of fossil fuels, and greenhouse gas emissions in disturbing the comfort zone of energy consumers and affordability of power for large commercial customers. This kind of problem can be alleviated with the help of optimal planning of Demand Response policies and with distributed generators in the distribution system. The objective of this article is to give a strategic proposition of an energy management system for a campus microgrid (µG) to minimize the operating costs and to increase the self-consuming energy of green DGs. To this end, a real-time-based campus is considered that is currently providing its loads from the utility grid only. Yet, according to the proposed given scenario, it contains the solar panels and wind turbine as a non-dispatchable DG while a diesel generator is considered as a dispatchable DG. It also incorporates the energy storage system with the optimal sizing of BESS to tackle with multiple disturbances that arise from solar radiations. Results: The resultant problem of linear mathematics has been simulated and plotted in MATLAB with mixed-integer linear programming. Simulation results show that the proposed given model of EMS minimizes the grid electricity costs by 31% in case of summer and 38% in case of winter respectively, while the reduction of GHG emissions per day is 780.68 and 730.46 kg for the corresponding summer and winter seasons. The general effect of a medium-sized solar PV installation on carbon emissions and energy consumption costs is also observed. Conclusion: The substantial environmental and economic benefits compared to the present case prompt campus owners to put investment in the DGs and to install large-scale energy storage.

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