scholarly journals Electricity Market Empowered by Artificial Intelligence: A Platform Approach

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4128 ◽  
Author(s):  
Yueqiang Xu ◽  
Petri Ahokangas ◽  
Jean-Nicolas Louis ◽  
Eva Pongrácz
Keyword(s):  
Artificial Intelligence ◽  
Electricity Market ◽  
Energy System ◽  
Holistic View ◽  
Aggregated Data ◽  
Horizon 2020 ◽  
Distributed Energy System ◽  
Innovation Project ◽  
Energy Domain ◽  
High Level

Artificial intelligence (AI) techniques and algorithms are increasingly being utilized in energy and renewable research to tackle various engineering problems. However, a majority of the AI studies in the energy domain have been focusing on solving specific technical issues. There is limited discussion on how AI can be utilized to enhance the energy system operations, particularly the electricity market, with a holistic view. The purpose of the study is to introduce the platform architectural logic that encompasses both technical and economic perspectives to the development of AI-enabled energy platforms for the future electricity market with massive and distributed renewables. A constructive and inductive approach for theory building is employed for the concept proposition of the AI energy platform by using the aggregated data from a European Union (EU) Horizon 2020 project and a Finnish national innovation project. Our results are presented as a systemic framework and high-level representation of the AI-enabled energy platform design with four integrative layers that could enable not only value provisioning but also value utilization for a distributed energy system and electricity market as the new knowledge and contribution to the extant research. Finally, the study discusses the potential use cases of the AI-enabled energy platform.

Designing the Electricity Market with Energy Storage Devices

2020 ◽  
Vol 12 (12) ◽  
pp. 31-43
Author(s):  
Tatiana A. VASKOVSKAYA ◽  
◽  
Boris A. KLUS ◽  
Keyword(s):  
Energy Storage ◽  
Electricity Market ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Storage Systems ◽  
Energy System ◽  
Energy Storage Systems ◽  
Storage Model ◽  
Optimal Power ◽  
Wide Range

The development of energy storage systems allows us to consider their usage for load profile leveling during operational planning on electricity markets. The paper proposes and analyses an application of an energy storage model to the electricity market in Russia with the focus on the day ahead market. We consider bidding, energy storage constraints for an optimal power flow problem, and locational marginal pricing. We show that the largest effect for the market and for the energy storage system would be gained by integration of the energy storage model into the market’s optimization models. The proposed theory has been tested on the optimal power flow model of the day ahead market in Russia of 10000-node Unified Energy System. It is shown that energy storage systems are in demand with a wide range of efficiencies and cycle costs.

The Importance Of Energy Solutions For A Safe And Rational Development

2014 ◽  
Vol 1 (1) ◽  
pp. 379-384
Author(s):  
Daniela Cristina Momete ◽  
Tudor Prisecaru
Keyword(s):  
Industrial Revolution ◽  
New Technology ◽  
Energy System ◽  
Crucial Importance ◽  
Gas Processing ◽  
Energy Technologies ◽  
Rational Development ◽  
Oil Exploitation ◽  
New Energy ◽  
Energy Domain

AbstractA new industrial revolution is on the verge in the energy domain considering the knowledge and skills acquired through the development of new energy technologies. Shale gas processing, unconventional oil exploitation, new exploring/drilling methods, mature renewable energy or in progress, all generated a wealth of knowledge in new technology. Therefore, this paper aims to analyse the positive and negative aspects of energy solutions, and to reveal the way to a world where a valid sustainable development, based on safe and rational premises, is actually considered. The paper also introduces suggestions for the energy system, which has a crucial importance in coping with the resource management of the future, where the economic, social, and environmental/climate needs of the post-crisis world should be suitably considered.

Europe

2020 ◽  
pp. 649-666
Author(s):  
Andrea Renda
Keyword(s):  
Artificial Intelligence ◽  
Digital Technology ◽  
Policy Framework ◽  
Expert Group ◽  
Ethical Guidelines ◽  
Ethical Implications ◽  
Strong Focus ◽  
Ethics Guidelines ◽  
Definition Of ◽  
High Level

This chapter assesses Europe’s efforts in developing a full-fledged strategy on the human and ethical implications of artificial intelligence (AI). The strong focus on ethics in the European Union’s AI strategy should be seen in the context of an overall strategy that aims at protecting citizens and civil society from abuses of digital technology but also as part of a competitiveness-oriented strategy aimed at raising the standards for access to Europe’s wealthy Single Market. In this context, one of the most peculiar steps in the European Union’s strategy was the creation of an independent High-Level Expert Group on AI (AI HLEG), accompanied by the launch of an AI Alliance, which quickly attracted several hundred participants. The AI HLEG, a multistakeholder group including fifty-two experts, was tasked with the definition of Ethics Guidelines as well as with the formulation of “Policy and Investment Recommendations.” With the advice of the AI HLEG, the European Commission put forward ethical guidelines for Trustworthy AI—which are now paving the way for a comprehensive, risk-based policy framework.

scholarly journals Evaluation of the energy demands for a floating O&M-hub

2021 ◽  
Author(s):  
F. Wittmann ◽  
C. Schmitt ◽  
F. Adam ◽  
P. Dierken
Keyword(s):  
Software Tool ◽  
Energy System ◽  
Spare Parts ◽  
Simulation Software ◽  
Offshore Wind ◽  
Baseline Scenario ◽  
Horizon 2020 ◽  
Energy Supply System ◽  
The North ◽  
Energy Demands

AbstractThe Energyhub@Sea concept is one of the four research applications of the Space@Sea project funded by the EU’s Horizon 2020 research program (GA number: 774253). The focus of this paper is the evaluation of the energy demands of an energy self-sufficient maintenance platform at the location of Helgoland in the North Sea. In view of this, a standardized modular floater was developed as an offshore wind operation and maintenance base, which in the following paper is referred to as an O&M hub. The O&M hub is intended to be equipped with accommodation facilities and various renewable energy infrastructure as well as spare parts logistics, enabling the platform to perform maintenance of offshore gearless wind turbines with a capacity of up to 10 MW. To be energy self-sustaining, an energy supply system for the hub was developed and simulated at a resolution of ten minutes by means of the Top-Energy simulation software, a commercial software tool. As a basis for the simulation, an approach for the automated determination of flexible load profiles, in resolutions of up to ten minutes was developed. This load profile generator creates load profiles on the basis of environmental conditions, technical characteristics, and expected behaviors of the inhabitants. On the basis of the generated load profiles, a first layout (referred to as baseline scenario) for the different components of the energy system was evaluated and tested through simulation. In a second step, three optimization scenarios were developed and simulated with regards to the financial feasibility of the Energyhub.

scholarly journals DRIvE Project Unlocks Demand Response Potential with Digital Twins

Proceedings ◽  
2020 ◽  
Vol 65 (1) ◽  
pp. 14
Author(s):  
Laura Pérez ◽  
Juan Espeche ◽  
Tatiana Loureiro ◽  
Aleksandar Kavgić
Keyword(s):  
Knowledge Transfer ◽  
Demand Response ◽  
Low Carbon ◽  
Distribution Grid ◽  
Horizon 2020 ◽  
Research And Innovation ◽  
Digital Twins ◽  
Future Workshop ◽  
Innovative Solutions ◽  
Innovation Project

DRIvE (Demand Response Integration Technologies) is a research and innovation project funded under the European Union’s Horizon 2020 Framework Program, whose main objective is unlocking the demand response potential in the distribution grid. DRIvE presented how the use of digital twins de-risks the implementation of demand response applications at the “Flexibility 2.0: Demand response and self-consumption based on the prosumer of Europe’s low carbon future” workshop within the conference “Sustainable Places 2020”. This workshop was organized to cluster and foster knowledge transfer between several EU projects, each developing innovative solutions within the field of demand response, energy flexibility, and optimized synergies between actors of the built environment and the power grid.

scholarly journals A Hybrid Energy System Workflow for Energy Portfolio Optimization

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4392
Author(s):  
Jia Zhou ◽  
Hany Abdel-Khalik ◽  
Paul Talbot ◽  
Cristian Rabiti
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Electricity Market ◽  
Process Model ◽  
Net Present Value ◽  
Historical Data ◽  
Energy System ◽  
Initial Guess ◽  
Reduced Order ◽  
The Impact

This manuscript develops a workflow, driven by data analytics algorithms, to support the optimization of the economic performance of an Integrated Energy System. The goal is to determine the optimum mix of capacities from a set of different energy producers (e.g., nuclear, gas, wind and solar). A stochastic-based optimizer is employed, based on Gaussian Process Modeling, which requires numerous samples for its training. Each sample represents a time series describing the demand, load, or other operational and economic profiles for various types of energy producers. These samples are synthetically generated using a reduced order modeling algorithm that reads a limited set of historical data, such as demand and load data from past years. Numerous data analysis methods are employed to construct the reduced order models, including, for example, the Auto Regressive Moving Average, Fourier series decomposition, and the peak detection algorithm. All these algorithms are designed to detrend the data and extract features that can be employed to generate synthetic time histories that preserve the statistical properties of the original limited historical data. The optimization cost function is based on an economic model that assesses the effective cost of energy based on two figures of merit: the specific cash flow stream for each energy producer and the total Net Present Value. An initial guess for the optimal capacities is obtained using the screening curve method. The results of the Gaussian Process model-based optimization are assessed using an exhaustive Monte Carlo search, with the results indicating reasonable optimization results. The workflow has been implemented inside the Idaho National Laboratory’s Risk Analysis and Virtual Environment (RAVEN) framework. The main contribution of this study addresses several challenges in the current optimization methods of the energy portfolios in IES: First, the feasibility of generating the synthetic time series of the periodic peak data; Second, the computational burden of the conventional stochastic optimization of the energy portfolio, associated with the need for repeated executions of system models; Third, the inadequacies of previous studies in terms of the comparisons of the impact of the economic parameters. The proposed workflow can provide a scientifically defendable strategy to support decision-making in the electricity market and to help energy distributors develop a better understanding of the performance of integrated energy systems.

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