scholarly journals Multi-scale Simulation of Energy Systems-Electricity Market Interactions.

2020 ◽  
Author(s):  
Xian Gao ◽  
Alexander Dowling ◽  
Bernard Knueven ◽  
John Siirola
Keyword(s):  
Electricity Market ◽  
Energy Systems ◽  
Market Interactions ◽  
Multi Scale

Multi-Scale, Multi-Dimensional Modelling of Future Energy Systems

2015 ◽  
pp. 113-135 ◽  
Author(s):  
Catalina Spataru ◽  
Andreas Koch ◽  
Pierrick Bouffaron
Keyword(s):  
Material Science ◽  
Control Strategies ◽  
Energy Systems ◽  
Simulation Models ◽  
Energy System ◽  
System Modelling ◽  
Multi Scale ◽  
Future Challenges ◽  
Urban Energy ◽  
Power System Engineering

This chapter provides a discussion of current multi-scale energy systems expressed by a multitude of data and simulation models, and how these modelling approaches can be (re)designed or combined to improve the representation of such system. It aims to address the knowledge gap in energy system modelling in order to better understand its existing and future challenges. The frontiers between operational algorithms embedded in hardware and modelling control strategies are becoming fuzzier: therefore the paradigm of modelling intelligent urban energy systems for the future has to be constantly evolving. The chapter concludes on the need to build a holistic, multi-dimensional and multi-scale framework in order to address tomorrow's urban energy challenges. Advances in multi-scale methods applied to material science, chemistry, fluid dynamics, and biology have not been transferred to the full extend to power system engineering. New tools are therefore necessary to describe dynamics of coupled energy systems with optimal control.

A multi-scale optimization framework for electricity market participation

2017 ◽  
Vol 190 ◽  
pp. 147-164 ◽  
Author(s):  
Alexander W. Dowling ◽  
Ranjeet Kumar ◽  
Victor M. Zavala
Keyword(s):  
Electricity Market ◽  
Market Participation ◽  
Multi Scale ◽  
Optimization Framework ◽  
Scale Optimization

Proposing a master’s programme on participatory integrated assessment of energy systems to promote energy access and energy efficiency in Southern Africa

2018 ◽  
Vol 19 (3) ◽  
pp. 622-641 ◽  
Author(s):  
Cheddi Kiravu ◽  
François Diaz-Maurin ◽  
Mario Giampietro ◽  
Alan C. Brent ◽  
Sandra G.F. Bukkens ◽  
...  
Keyword(s):  
Higher Education ◽  
Energy Efficiency ◽  
Southern Africa ◽  
Integrated Assessment ◽  
Energy Systems ◽  
Energy Access ◽  
Transdisciplinary Approach ◽  
Holistic View ◽  
Content Type ◽  
Multi Scale

Purpose This paper aims to present a new master’s programme for promoting energy access and energy efficiency in Southern Africa. Design/methodology/approach A transdisciplinary approach called “participatory integrated assessment of energy systems” (PARTICIPIA) was used for the development of the curriculum. This approach is based on the two emerging fields of “multi-scale integrated assessment” and “science for governance”, which bring innovative concepts and methods. Findings The application of the PARTICIPIA methodology to three case studies reveals that the proposed transdisciplinary approach could support energy and development policies in the region. The implementation of the PARTICIPIA curriculum in three higher education institutions reveals its ability to respond to the needs of specific contexts and its connection with existing higher education programmes. Practical implications Considering energy issues from a transdisciplinary approach in higher education is absolutely critical because such a holistic view cannot be achieved through engineering curricula. Deliberate and greater efforts should be made to integrate methods from “multi-scale integrated assessment” and “science for governance” in higher education curricula to train a new breed of modern-day energy planners in charge of coming up with solutions that are shared by all relevant stakeholders. Originality/value This paper presents an innovative higher education curriculum in terms of the attention given to energy access and energy efficiency that affect the southern Africa region and the nature of the methodology adopted to face these issues.

How clean is your ‘clean’ energy? The ENVIRO module for energy system models

2021 ◽  
Author(s):  
Nicholas Martin ◽  
Cristina Madrid-López ◽  
Laura Talens-Peiró ◽  
Bryn Pickering
Keyword(s):  
Renewable Energy ◽  
Environmental Impacts ◽  
Energy Systems ◽  
Energy System ◽  
Raw Material ◽  
Energy System Model ◽  
Multi Scale ◽  
Renewable Energy System ◽  
Trade Offs ◽  
The Eu

<p>A decarbonized, renewable energy system is generally assumed to represent a cleaner and more sustainable one. However, while they do promise day-to-day reductions in carbon emissions, many other environmental impacts could occur, and these are often overlooked. Indeed, in the two documents that form the EU Energy Union Strategy (COM/2015/080) the words ‘water’, ‘biodiversity’ or ‘raw materials’ do not appear. This ‘tunnel vision’ is often also adopted in current energy systems models, which do not generally provide a detailed analysis of all of the environmental impacts that accompany different energy scenarios. Ignoring the trade-offs between energy systems and other resources can result in misleading information and misguided policy making.</p><p>The environmental assessment module ENVIRO combines the bottom up, high resolution capabilities of life cycle assessment (LCA) with the hierarchical multi-scale upscaling capabilities of the Multi-Scale Integrated Assessment of Socioecosystem Metabolism (MuSIASEM) approach in an effort to address this gap. ENVIRO also takes the systemic trade-offs associated with the water-energy-food-(land-climate-etc.) nexus from MuSIASEM while considering the supply chain perspective of LCA. The module contains a built-in set of indicators that serve to assess the constraints that greenhouse gas (GHG) emissions, pollution, water use and raw material demands pose to renewable energy system scenarios. It can be used to assess the coherence between energy decarbonization targets and water or raw material targets; this can be extended to potentially any economic or political target that has a biophysical component.</p><p>In this work, we introduce the semantics and formalization aspects of ENVIRO, its integration with the energy system model Calliope, and the results of a first testing of the module in the assessment of decarbonization scenarios for the EU. The work is part of the research developed in the H2020 Project SENTINEL: Sustainable Energy Transition Laboratory (contract 837089).</p>

Multi-scale energy systems engineering for optimal natural gas utilization

2020 ◽  
Vol 356 ◽  
pp. 18-26 ◽  
Author(s):  
William W. Tso ◽  
C. Doga Demirhan ◽  
Christodoulos A. Floudas ◽  
Efstratios N. Pistikopoulos
Keyword(s):  
Natural Gas ◽  
Systems Engineering ◽  
Energy Systems ◽  
Multi Scale ◽  
Gas Utilization

scholarly journals Clustering time series applied to energy markets

2019 ◽  
Vol 2 (S1) ◽  
Author(s):  
Cornelia Krome ◽  
Jan Höft ◽  
Volker Sander
Keyword(s):  
Time Series ◽  
Power Plant ◽  
Electricity Market ◽  
Power Plants ◽  
Large Scale ◽  
Energy Systems ◽  
Weather Data ◽  
Virtual Power ◽  
Virtual Power Plant ◽  
Virtual Power Plants

Abstract In Germany and many other countries the energy market has been subject to significant changes. Instead of only a few large-scale producers that serve aggregated consumers, a shift towards regenerative energy sources is taking place. Energy systems are increasingly being made more flexible by decentralised producers and storage facilities, i.e. many consumers are also producers. The aggregation of producers form another type of power plants: a virtual power plant. On the basis of aggregated production and consumption, virtual power plants try to make decisions under the conditions of the electricity market or the grid condition. They are influenced by many different aspects. These include the current feed-in, weather data, or the demands of the consumers. Clearly, a virtual power plant is focusing on developing strategies to influence and optimise these factors. To accomplish this, many data sets can and should be analysed in order to interpret and create forecasts for energy systems. Time series based analytics are therefore of particular interest for virtual power plants. Classifying the different time series according to generators, consumers or customer types simplifies processes. In this way, scalable solutions for forecasts can be found. However, one has to first find the according clusters efficiently. This paper presents a method for determining clusters of time series. Models are adapted and model-based clustered using ARIMA parameters and an individual quality measure. In this way, the analysis of generic time series can be simplified and additional statements can be made with the help of graphical evaluations. To facilitate large scale virtual power plants, the presented clustering workflow is prepared to be applied on big data capable platforms, e.g. time series stored in Apache Cassandra, analysed through an Apache Spark execution framework. The procedure is shown here using the example of the Day-Ahead prices of the electricity market for 2018.

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