scholarly journals The Role of Blockchains in Multi-Stakeholder Transactive Energy Systems

2020 ◽  
Vol 3 ◽  
Author(s):  
Scott Eisele ◽  
Aron Laszka ◽  
Douglas C. Schmidt ◽  
Abhishek Dubey
Keyword(s):  
Power Systems ◽  
Market Efficiency ◽  
Energy Systems ◽  
Power Grids ◽  
Embedded Devices ◽  
Blockchain Technology ◽  
Energy Domain ◽  
Transactive Energy ◽  
Battery Technology ◽  
Multi Stakeholder

Power grids are undergoing major changes due to rapid growth in renewable energy and improvements in battery technology. Prompted by the increasing complexity of power systems, decentralized solutions are emerging that arrange local communities into transactive microgrids. This paper addresses the problem of implementing transactive energy mechanisms in a distributed setting, providing both privacy and safety. Specifically, we design and implement an automated auction and matching system that ensures safety (e.g., satisfaction of line capacity constraints), preserves privacy, and promotes local trade and market efficiency for transactive energy systems. This design problem is challenging because safety, market efficiency, and privacy are competing objectives. We implement our solution as a decentralized trading platform built on blockchain technology and smart contracts. To demonstrate the viability of our platform, we analyze the results of experiments with dozens of embedded devices and energy production and consumption profiles using an actual dataset from the transactive energy domain.

scholarly journals Eco-Emission Analysis of Multi-Carrier Microgrid Integrated with Compressed Air and Power-to-Gas Energy Storage Technologies

2021 ◽  
Vol 13 (9) ◽  
pp. 4681
Author(s):  
Khashayar Hamedi ◽  
Shahrbanoo Sadeghi ◽  
Saeed Esfandi ◽  
Mahdi Azimian ◽  
Hessam Golmohamadi
Keyword(s):  
Energy Storage ◽  
Power Systems ◽  
High Efficiency ◽  
Objective Assessment ◽  
Energy Systems ◽  
Vital Role ◽  
Compressed Air ◽  
Emission Analysis ◽  
Multi Objective ◽  
Power To Gas

Growing concerns about global greenhouse gas emissions have led power systems to utilize clean and highly efficient resources. In the meantime, renewable energy plays a vital role in energy prospects worldwide. However, the random nature of these resources has increased the demand for energy storage systems. On the other hand, due to the higher efficiency of multi-energy systems compared to single-energy systems, the development of such systems, which are based on different types of energy carriers, will be more attractive for the utilities. Thus, this paper represents a multi-objective assessment for the operation of a multi-carrier microgrid (MCMG) in the presence of high-efficiency technologies comprising compressed air energy storage (CAES) and power-to-gas (P2G) systems. The objective of the model is to minimize the operation cost and environmental pollution. CAES has a simple-cycle mode operation besides the charging and discharging modes to provide more flexibility in the system. Furthermore, the demand response program is employed in the model to mitigate the peaks. The proposed system participates in both electricity and gas markets to supply the energy requirements. The weighted sum approach and fuzzy-based decision-making are employed to compromise the optimum solutions for conflicting objective functions. The multi-objective model is examined on a sample system, and the results for different cases are discussed. The results show that coupling CAES and P2G systems mitigate the wind power curtailment and minimize the cost and pollution up to 14.2% and 9.6%, respectively.

scholarly journals Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4776
Author(s):  
Seyed Mahdi Miraftabzadeh ◽  
Michela Longo ◽  
Federica Foiadelli ◽  
Marco Pasetti ◽  
Raul Igual
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Smart Grids ◽  
State Of The Art ◽  
Smart Cities ◽  
Power Grids ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
New Research ◽  
Traditional Approaches

The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models.

scholarly journals Special Issue: “Wind Power Integration into Power Systems: Stability and Control Aspects”

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3680
Author(s):  
Lasantha Meegahapola ◽  
Siqi Bu
Keyword(s):  
Power Systems ◽  
Wind Power ◽  
Renewable Energy Sources ◽  
Power Grids ◽  
Energy Sources ◽  
Low Carbon ◽  
Carbon Neutrality ◽  
Stability And Control ◽  
Wind Power Integration ◽  
And Control

Power network operators are rapidly incorporating wind power generation into their power grids to meet the widely accepted carbon neutrality targets and facilitate the transition from conventional fossil-fuel energy sources to the clean and low-carbon renewable energy sources [...]

scholarly journals Optimal Pricing of Vehicle-to-Grid Services Using Disaggregate Demand Models

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1090
Author(s):  
Charilaos Latinopoulos ◽  
Aruna Sivakumar ◽  
John W. Polak
Keyword(s):  
Power Systems ◽  
Electric Vehicles ◽  
Power Grids ◽  
Local Distribution ◽  
Technical Problems ◽  
Vehicle To Grid ◽  
Distribution Transformers ◽  
Demand Models ◽  
Multiple Vehicles ◽  
Infrastructure Investments

The recent revolution in electric mobility is both crucial and promising in the coordinated effort to reduce global emissions and tackle climate change. However, mass electrification brings up new technical problems that need to be solved. The increasing penetration rates of electric vehicles will add an unprecedented energy load to existing power grids. The stability and the quality of power systems, especially on a local distribution level, will be compromised by multiple vehicles that are simultaneously connected to the grid. In this paper, the authors propose a choice-based pricing algorithm to indirectly control the charging and V2G activities of electric vehicles in non-residential facilities. Two metaheuristic approaches were applied to solve the optimization problem, and a comparative analysis was performed to evaluate their performance. The proposed algorithm would result in a significant revenue increase for the parking operator, and at the same time, it could alleviate the overloading of local distribution transformers and postpone heavy infrastructure investments.

Toward Online Control of Local Bifurcation in Power Systems via Network Topology Optimization

2015 ◽  
Vol 25 (12) ◽  
pp. 1550167
Author(s):  
Lei Wang ◽  
Hsiao-Dong Chiang
Keyword(s):  
Topology Optimization ◽  
Power Systems ◽  
Transmission Line ◽  
Network Topology ◽  
Online Control ◽  
Power Grids ◽  
Local Bifurcations ◽  
Network Topology Optimization ◽  
Online Methods ◽  
The Impact

This paper presents online methods for controlling local bifurcations of power grids with the goal of increasing bifurcation values (i.e. increasing load margins) via network topology optimization, a low-cost control. In other words, this paper presents online methods for increasing power transfer capability subject to static stability limit via switching transmission line out/in (i.e. disconnecting a transmission line or connecting a transmission line). To illustrate the impact of network topology on local bifurcations, two common local bifurcations, i.e. saddle-node bifurcation and structure-induced bifurcation on small power grids with different network topologies are shown. A three-stage online control methodology of local bifurcations via network topology optimization is presented to delay local bifurcations of power grids. Online methods must meet the challenging requirements of online applications such as the speed requirement (in the order of minutes), accuracy requirement and robustness requirement. The effectiveness of the three-stage methodology for online applications is demonstrated on the IEEE 118-bus and a 1648-bus practical power systems.

Design and Optimization of Integrated Distributed Energy Systems for Off-grid Buildings

2021 ◽  
pp. 1-27
Author(s):  
Jian Zhang ◽  
Heejin Cho ◽  
Pedro Mago
Keyword(s):  
Energy Storage ◽  
Optimal Design ◽  
Power Systems ◽  
Thermal Energy ◽  
Waste Heat ◽  
Carbon Dioxide Emission ◽  
Energy Systems ◽  
Solar Thermal ◽  
Solar Thermal Energy ◽  
Distributed Energy

Abstract Off-grid concepts for homes and buildings have been a fast-growing trend worldwide in the last few years because of the rapidly dropping cost of renewable energy systems and their self-sufficient nature. Off-grid homes/buildings can be enabled with various energy generation and storage technologies, however, design optimization and integration issues have not been explored sufficiently. This paper applies a multi-objective genetic algorithm (MOGA) optimization to obtain an optimal design of integrated distributed energy systems for off-grid homes in various climate regions. Distributed energy systems consisting of renewable and non-renewable power generation technologies with energy storage are employed to enable off-grid homes/buildings and meet required building electricity demands. In this study, the building types under investigation are residential homes. Multiple distributed energy resources are considered such as combined heat and power systems (CHP), solar photovoltaic (PV), solar thermal collector (STC), wind turbine (WT), as well as battery energy storage (BES) and thermal energy storage (TES). Among those technologies, CHP, PV, and WT are used to generate electricity, which satisfies the building's electric load, including electricity consumed for space heating and cooling. Solar thermal energy and waste heat recovered from CHP are used to partly supply the building's thermal load. Excess electricity and thermal energy can be stored in the BES and TES for later use. The MOGA is applied to determine the best combination of DERs and each component's size to reduce the system cost and carbon dioxide emission for different locations. Results show that the proposed optimization method can be effectively and widely applied to design integrated distributed energy systems for off-grid homes resulting in an optimal design and operation based on a trade-off between economic and environmental performance.

scholarly journals Performance Evaluation for Transactive Energy Systems Using Double-Auction Market

2019 ◽  
Vol 34 (5) ◽  
pp. 4128-4137 ◽  
Author(s):  
Jianming Lian ◽  
Huiying Ren ◽  
Yannan Sun ◽  
Donald J. Hammerstrom
Keyword(s):  
Performance Evaluation ◽  
Energy Systems ◽  
Double Auction ◽  
Auction Market ◽  
Transactive Energy

scholarly journals Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics

2019 ◽  
Vol 9 (20) ◽  
pp. 4417 ◽  
Author(s):  
Sana Mujeeb ◽  
Turki Ali Alghamdi ◽  
Sameeh Ullah ◽  
Aisha Fatima ◽  
Nadeem Javaid ◽  
...  
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Power Systems ◽  
Prediction Model ◽  
Wind Power ◽  
Fossil Fuel ◽  
Energy Systems ◽  
Accurate Estimation ◽  
Wind Power Forecasting ◽  
Power Forecasting

Recently, power systems are facing the challenges of growing power demand, depleting fossil fuel and aggravating environmental pollution (caused by carbon emission from fossil fuel based power generation). The incorporation of alternative low carbon energy generation, i.e., Renewable Energy Sources (RESs), becomes crucial for energy systems. Effective Demand Side Management (DSM) and RES incorporation enable power systems to maintain demand, supply balance and optimize energy in an environmentally friendly manner. The wind power is a popular energy source because of its environmental and economical benefits. However, the uncertainty of wind power makes its incorporation in energy systems really difficult. To mitigate the risk of demand-supply imbalance, an accurate estimation of wind power is essential. Recognizing this challenging task, an efficient deep learning based prediction model is proposed for wind power forecasting. The proposed model has two stages. In the first stage, Wavelet Packet Transform (WPT) is used to decompose the past wind power signals. Other than decomposed signals and lagged wind power, multiple exogenous inputs (such as, calendar variable and Numerical Weather Prediction (NWP)) are also used as input to forecast wind power. In the second stage, a new prediction model, Efficient Deep Convolution Neural Network (EDCNN), is employed to forecast wind power. A DSM scheme is formulated based on forecasted wind power, day-ahead demand and price. The proposed forecasting model’s performance was evaluated on big data of Maine wind farm ISO NE, USA.

Sign in / Sign up

Export Citation Format

Share Document