scholarly journals Operational planning of large-scale energy systems with high share of renewable generation

2021 ◽  
Author(s):  
Juan Gea Bermúdez ◽  
Kaushik Das ◽  
Matti Juhani Koivisto ◽  
Hardi koduvere
Keyword(s):  
Large Scale ◽  
Unit Commitment ◽  
Price Volatility ◽  
Energy Systems ◽  
Operational Planning ◽  
Computational Time ◽  
Energy Unit ◽  
Integer Variables ◽  
The Cost ◽  
High Share

<div>This paper proposes a mathematical model to perform optimal operational planning of large-scale energy systems with high share of renewable energy. Furthermore, it analyses the influence of different unit commitment modelling approaches on the operational planning outcomes. The value of co-optimisation of electricity and heating sector is emphasized in this paper. The results show the influence of massive renewable penetration in the energy sector towards 2050, and how this influences generation from other sources such as thermal and hydro. Including unit commitment constraints with integer variables leads to more realistic behaviour of the units, at the cost of increasing considerably the computational time. Relaxing integer variables reduces significantly the computational time, without highly compromising the accuracy of the results. Neglecting the unit commitment constraints leads to inaccurate results in terms of underestimation of costs, curtailment, wind’s and solar PV’s average revenue per energy unit sold, price volatility, and to overestimation of the flexibility of thermal units. Hence, depending on the purpose of the analysis, it is recommended to consider carefully the choice of unit commitment modelling approach and acknowledge the limitations. When the focus is on prices and revenues, using unit commitment constraints with integer variables is preferable.</div>

scholarly journals Operational planning of large-scale energy systems with high share of renewable generation

2020 ◽  
Author(s):  
Juan Gea Bermúdez ◽  
Kaushik Das ◽  
Matti Juhani Koivisto ◽  
Hardi koduvere
Keyword(s):  
Large Scale ◽  
Unit Commitment ◽  
Price Volatility ◽  
Energy Systems ◽  
Operational Planning ◽  
Computational Time ◽  
Energy Unit ◽  
Integer Variables ◽  
The Cost ◽  
High Share

<div>This paper proposes a mathematical model to perform optimal operational planning of large-scale energy systems with high share of renewable energy. Furthermore, it analyses the influence of different unit commitment modelling approaches on the operational planning outcomes. The value of co-optimisation of electricity and heating sector is emphasized in this paper. The results show the influence of massive renewable penetration in the energy sector towards 2050, and how this influences generation from other sources such as thermal and hydro. Including unit commitment constraints with integer variables leads to more realistic behaviour of the units, at the cost of increasing considerably the computational time. Relaxing integer variables reduces significantly the computational time, without highly compromising the accuracy of the results. Neglecting the unit commitment constraints leads to inaccurate results in terms of underestimation of costs, curtailment, wind’s and solar PV’s average revenue per energy unit sold, price volatility, and to overestimation of the flexibility of thermal units. Hence, depending on the purpose of the analysis, it is recommended to consider carefully the choice of unit commitment modelling approach and acknowledge the limitations. When the focus is on prices and revenues, using unit commitment constraints with integer variables is preferable.</div>

scholarly journals Operational planning of large-scale energy systems with high share of renewable generation

2020 ◽  
Author(s):  
Juan Gea Bermúdez ◽  
Kaushik Das ◽  
Matti Juhani Koivisto ◽  
Hardi koduvere
Keyword(s):  
Large Scale ◽  
Unit Commitment ◽  
Price Volatility ◽  
Energy Systems ◽  
Operational Planning ◽  
Computational Time ◽  
Energy Unit ◽  
Integer Variables ◽  
The Cost ◽  
High Share

<div>This paper proposes a mathematical model to perform optimal operational planning of large-scale energy systems with high share of renewable energy. Furthermore, it analyses the influence of different unit commitment modelling approaches on the operational planning outcomes. The value of co-optimisation of electricity and heating sector is emphasized in this paper. The results show the influence of massive renewable penetration in the energy sector towards 2050, and how this influences generation from other sources such as thermal and hydro. Including unit commitment constraints with integer variables leads to more realistic behaviour of the units, at the cost of increasing considerably the computational time. Relaxing integer variables reduces significantly the computational time, without highly compromising the accuracy of the results. Neglecting the unit commitment constraints leads to inaccurate results in terms of underestimation of costs, curtailment, wind’s and solar PV’s average revenue per energy unit sold, price volatility, and to overestimation of the flexibility of thermal units. Hence, depending on the purpose of the analysis, it is recommended to consider carefully the choice of unit commitment modelling approach and acknowledge the limitations. When the focus is on prices and revenues, using unit commitment constraints with integer variables is preferable.</div>

scholarly journals Day-ahead Market Modelling of Large-scale Highly-renewable Multi-energy Systems: Analysis of the North Sea Region Towards 2050

2020 ◽  
Author(s):  
Juan Gea Bermúdez ◽  
Kaushik Das ◽  
Hardi Koduvere ◽  
Matti Juhani Koivisto
Keyword(s):  
North Sea ◽  
Large Scale ◽  
Unit Commitment ◽  
District Heating ◽  
Energy Systems ◽  
Energy System ◽  
Computational Time ◽  
Integer Variables ◽  
The North ◽  
The North Sea

This paper proposes a mathematical model to simulate Day-ahead markets of large-scale multi-energy systems with high share of renewable energy. Furthermore, it analyses the importance of including unit commitment when performing such analysis. The results of the case study, which is performed for the North Sea region, show the influence of massive renewable penetration in the energy sector and increasing electrification of the district heating sector towards 2050, and how this impacts the role of other energy sources such as thermal and hydro. The penetration of wind and solar is likely to challenge the need for balancing in the system as well as the profitability of thermal units. The degree of influence of the unit commitment approach is found to be dependent on the configuration of the energy system. Overall, including unit commitment constraints with integer variables leads to more realistic behaviour of the units, at the cost of increasing considerably the computational time. Relaxing integer variables reduces significantly the computational time, without highly compromising the accuracy of the results. The proposed model, together with the insights from the study case, can be specially useful for system operators for optimal operational planning.

scholarly journals Day-Ahead Market Modelling of Large-Scale Highly-Renewable Multi-Energy Systems: Analysis of the North Sea Region towards 2050

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 88
Author(s):  
Juan Gea-Bermúdez ◽  
Kaushik Das ◽  
Hardi Koduvere ◽  
Matti Juhani Koivisto
Keyword(s):  
North Sea ◽  
Large Scale ◽  
Unit Commitment ◽  
District Heating ◽  
Energy Systems ◽  
Energy System ◽  
Computational Time ◽  
Integer Variables ◽  
The North ◽  
The North Sea

This paper proposes a mathematical model in order to simulate Day-ahead markets of large-scale multi-energy systems with a high share of renewable energy. Furthermore, it analyses the importance of including unit commitment when performing such analysis. The results of the case study, which is performed for the North Sea region, show the influence of massive renewable penetration in the energy sector and increasing electrification of the district heating sector towards 2050, and how this impacts the role of other energy sources, such as thermal and hydro. The penetration of wind and solar is likely to challenge the need for balancing in the system as well as the profitability of thermal units. The degree of influence of the unit commitment approach is found to be dependent on the configuration of the energy system. Overall, including unit commitment constraints with integer variables leads to more realistic behaviour of the units, at the cost of considerably increasing the computational time. Relaxing integer variables significantly reduces the computational time, without highly compromising the accuracy of the results. The proposed model, together with the insights from the study case, can be especially useful for system operators for optimal operational planning.

scholarly journals Backbone—An Adaptable Energy Systems Modelling Framework

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3388 ◽  
Author(s):  
Niina Helistö ◽  
Juha Kiviluoma ◽  
Jussi Ikäheimo ◽  
Topi Rasku ◽  
Erkka Rinne ◽  
...  
Keyword(s):  
Power Plants ◽  
Large Scale ◽  
Unit Commitment ◽  
Energy Systems ◽  
Mixed Integer ◽  
Investment Planning ◽  
Planning And Scheduling ◽  
Modelling Framework ◽  
Wide Range ◽  
Systems Modelling

Backbone represents a highly adaptable energy systems modelling framework, which can be utilised to create models for studying the design and operation of energy systems, both from investment planning and scheduling perspectives. It includes a wide range of features and constraints, such as stochastic parameters, multiple reserve products, energy storage units, controlled and uncontrolled energy transfers, and, most significantly, multiple energy sectors. The formulation is based on mixed-integer programming and takes into account unit commitment decisions for power plants and other energy conversion facilities. Both high-level large-scale systems and fully detailed smaller-scale systems can be appropriately modelled. The framework has been implemented as the open-source Backbone modelling tool using General Algebraic Modeling System (GAMS). An application of the framework is demonstrated using a power system example, and Backbone is shown to produce results comparable to a commercial tool. However, the adaptability of Backbone further enables the creation and solution of energy systems models relatively easily for many different purposes and thus it improves on the available methodologies.

scholarly journals Advantages of Applying Large-Scale Energy Storage for Load-Generation Balancing

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3093
Author(s):  
Dawid Chudy ◽  
Adam Leśniak
Keyword(s):  
Energy Storage ◽  
Power Systems ◽  
Large Scale ◽  
Unit Commitment ◽  
Renewable Energy Sources ◽  
Frequency Regulation ◽  
The Cost ◽  
Further Development ◽  
Proper Operation ◽  
Load Generation

The continuous development of energy storage (ES) technologies and their wider utilization in modern power systems are becoming more and more visible. ES is used for a variety of applications ranging from price arbitrage, voltage and frequency regulation, reserves provision, black-starting and renewable energy sources (RESs), supporting load-generation balancing. The cost of ES technologies remains high; nevertheless, future decreases are expected. As the most profitable and technically effective solutions are continuously sought, this article presents the results of the analyses which through the created unit commitment and dispatch optimization model examines the use of ES as support for load-generation balancing. The performed simulations based on various scenarios show a possibility to reduce the number of starting-up centrally dispatched generating units (CDGUs) required to satisfy the electricity demand, which results in the facilitation of load-generation balancing for transmission system operators (TSOs). The barriers that should be encountered to improving the proposed use of ES were also identified. The presented solution may be suitable for further development of renewables and, in light of strict climate and energy policies, may lead to lower utilization of large-scale power generating units required to maintain proper operation of power systems.

Dynamic Ride-Matching for Large-Scale Transportation Systems

2021 ◽  
pp. 036119812110494
Author(s):  
Taner Cokyasar ◽  
Felipe de Souza ◽  
Joshua Auld ◽  
Omer Verbas
Keyword(s):  
Large Scale ◽  
Programming Model ◽  
Learning Algorithm ◽  
Transportation Systems ◽  
Computational Time ◽  
System Optimum ◽  
Solution Quality ◽  
Optimum Approach ◽  
The Cost

Efficient dynamic ride-matching (DRM) in large-scale transportation systems is a key driver in transport simulations to yield answers to challenging problems. Although the DRM problem is simple to solve, it quickly becomes a computationally challenging problem in large-scale transportation system simulations. Therefore, this study thoroughly examines the DRM problem dynamics and proposes an optimization-based solution framework to solve the problem efficiently. To benefit from parallel computing and reduce computational times, the problem’s network is divided into clusters utilizing a commonly used unsupervised machine learning algorithm along with a linear programming model. Then, these sub-problems are solved using another linear program to finalize the ride-matching. At the clustering level, the framework allows users adjusting cluster sizes to balance the trade-off between the computational time savings and the solution quality deviation. A case study in the Chicago Metropolitan Area, U.S., illustrates that the framework can reduce the average computational time by 58% at the cost of increasing the average pick up time by 26% compared with a system optimum, that is, non-clustered, approach. Another case study in a relatively small city, Bloomington, Illinois, U.S., shows that the framework provides quite similar results to the system-optimum approach in approximately 62% less computational time.

Development of Unit Commitment Model Considering Confidence Intervals of Photovoltaics Forecast and Analysis of a Large Scale Power System

2016 ◽  
Vol 136 (5) ◽  
pp. 484-496 ◽  
Author(s):  
Yusuke Udagawa ◽  
Kazuhiko Ogimoto ◽  
Takashi Oozeki ◽  
Hideaki Ohtake ◽  
Takashi Ikegami ◽  
...  
Keyword(s):  
Power System ◽  
Confidence Intervals ◽  
Large Scale ◽  
Unit Commitment

Unit Commitment in Electric Energy Systems

2017 ◽  
Author(s):  
Miguel F. Anjos ◽  
Antionio J. Conejo
Keyword(s):  
Unit Commitment ◽  
Electric Energy ◽  
Energy Systems

Combustion Driven by Fragment-based Ab Initio Molecular Dynamics Simulation

2019 ◽  
Author(s):  
Liqun Cao ◽  
Jinzhe Zeng ◽  
Mingyuan Xu ◽  
Chih-Hao Chin ◽  
Tong Zhu ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Ab Initio ◽  
Large Scale ◽  
Methane Combustion ◽  
Combustion Method ◽  
Dynamics Simulation ◽  
Ab Initio Molecular Dynamics ◽  
Computational Time ◽  
Combustion Mechanism ◽  
Daily Lives

Combustion is a kind of important reaction that affects people's daily lives and the development of aerospace. Exploring the reaction mechanism contributes to the understanding of combustion and the more efficient use of fuels. Ab initio quantum mechanical (QM) calculation is precise but limited by its computational time for large-scale systems. In order to carry out reactive molecular dynamics (MD) simulation for combustion accurately and quickly, we develop the MFCC-combustion method in this study, which calculates the interaction between atoms using QM method at the level of MN15/6-31G(d). Each molecule in systems is treated as a fragment, and when the distance between any two atoms in different molecules is greater than 3.5 Å, a new fragment involved two molecules is produced in order to consider the two-body interaction. The deviations of MFCC-combustion from full system calculations are within a few kcal/mol, and the result clearly shows that the calculated energies of the different systems using MFCC-combustion are close to converging after the distance thresholds are larger than 3.5 Å for the two-body QM interactions. The methane combustion was studied with the MFCC-combustion method to explore the combustion mechanism of the methane-oxygen system.

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