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Energies
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Optimisation Models and Methods in Energy Systems
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Optimisation Models and Methods in Energy Systems

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (6 May 2019) | Viewed by 25195

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editor

Prof. Dr. Carlos Henggeler Antunes

E-Mail Website
Guest Editor
INESC Coimbra, Department of Electrical and Computer Engineering, University of Coimbra, Polo 2, 3030-290 Coimbra, Portugal
Interests: energy efficiency; demand side management; demand response; optimization models and methods in energy systems; multi-objective optimization; multi-criteria decision analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Challenging problems arise in all segments of energy industries—generation, transmission, distribution and consumption. Optimization models and methods play a key role to offer decision/policy makers better information to assist sounder decisions at different levels, ranging from operational to strategic planning.

Energy systems and networks are increasingly complex; therefore, optimization models and methods are essential tools for the development of smart(er) networks within more integrated and sustainable energy systems, encompassing electricity, gas, district heating/cooling, etc., with pervasive deployment of information and communication technologies.

Technical design, operational, economic, regulatory, social and environmental issues, among others, are at stake requiring interdisciplinary approaches, with contributions from engineering, economics and social sciences fields to the definition of adequate optimization models and methods to support more informed decision processes.

Planning tasks are increasingly complex due to the unbundling of the industry value chain and the emergence of new players (e.g., aggregators) and market structures. The ongoing evolution of energy systems to smart grids comprises the deployment of new network automation technologies, bi-directional communication, smart metering, analysis and extraction of value from massive amounts of data. This process enables the integration of further renewable-based generation, which contributes to the decarbonization of the economy but in turn creates new technical and market challenges due to its variable nature, and the empowerment of consumers who may have a more proactive role through demand response mechanisms. The global aim is to develop more sustainable, reliable and efficient grids.

Contributions are expected to cover a wide range of topics including: Electricity smart grids; gas smart grids; district heating/cooling; integration of renewable generation; storage; demand side management and demand response; selection and location of equipment including communication issues; system reliability and provision of ancillary services; market design and operation. Further topics related to optimization models and methods to tackle challenging problems in energy networks and systems, namely regarding the evolution to smart grids, are welcome. Contributions reporting real-world case studies are particularly appreciated.

All papers will undergo a stringent review procedure according to the quality standards of Energies. Papers must contain original research results including comprehensive mathematical models, algorithmic advances and extensive numerical experiments. Numerical illustrations cannot be toy examples, but real or realistic case studies for which all data should be provided (in the paper or as supplementary material) to ensure the replicability of results. The research reported in contributed papers should convey novel and significant work relative to the relevant literature.

Prof. Dr. Carlos Henggeler Antunes
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • optimization models and methods
  • energy systems
  • smart grids
  • electrical networks
  • gas networks
  • district heating/cooling

Published Papers (6 papers)

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Research

20 pages, 2454 KiB  
Article
Including Wind Power Generation in Brazil’s Long-Term Optimization Model for Energy Planning
by Paula Medina Maçaira, Yasmin Monteiro Cyrillo, Fernando Luiz Cyrino Oliveira and Reinaldo Castro Souza
Energies 2019, 12(5), 826; https://doi.org/10.3390/en12050826 - 02 Mar 2019
Cited by 7 | Viewed by 3402
Abstract
In the past two decades, wind power’s share of the energy mix has grown significantly in Brazil. However, nowadays planning electricity operation in Brazil basically involves evaluating the future conditions of energy supply from hydro and thermal sources over the planning horizon. In [...] Read more.
In the past two decades, wind power’s share of the energy mix has grown significantly in Brazil. However, nowadays planning electricity operation in Brazil basically involves evaluating the future conditions of energy supply from hydro and thermal sources over the planning horizon. In this context, wind power sources are not stochastically treated. This work applies an innovative approach that incorporates wind power generation in the Brazilian hydro-thermal dispatch using the analytical method of Frequency & Duration. The proposed approach is applied to Brazil’s Northeast region, covering the planning period from July 2017 to December 2021, using the Markov chain Monte Carlo method to simulate wind power scenarios. The obtained results are more conservative than the one currently used by the National Electric System Operator, since the proposed approach forecasts 1.8% less wind generation, especially during peak periods, and 0.67% more thermal generation. This conservatism can reduce the chance of water reservoir depletion and, also an ineffective dispatch. Full article
(This article belongs to the Special Issue Optimisation Models and Methods in Energy Systems)
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15 pages, 2478 KiB  
Article
A University Building Test Case for Occupancy-Based Building Automation
by Siva Swaminathan, Ximan Wang, Bingyu Zhou and Simone Baldi
Energies 2018, 11(11), 3145; https://doi.org/10.3390/en11113145 - 14 Nov 2018
Cited by 5 | Viewed by 3306
Abstract
Heating, ventilation and air-conditioning (HVAC) units in buildings form a system-of-subsystems entity that must be accurately integrated and controlled by the building automation system to ensure the occupants’ comfort with reduced energy consumption. As control of HVACs involves a standardized hierarchy of high-level [...] Read more.
Heating, ventilation and air-conditioning (HVAC) units in buildings form a system-of-subsystems entity that must be accurately integrated and controlled by the building automation system to ensure the occupants’ comfort with reduced energy consumption. As control of HVACs involves a standardized hierarchy of high-level set-point control and low-level Proportional-Integral-Derivative (PID) controls, there is a need for overcoming current control fragmentation without disrupting the standard hierarchy. In this work, we propose a model-based approach to achieve these goals. In particular: the set-point control is based on a predictive HVAC thermal model, and aims at optimizing thermal comfort with reduced energy consumption; the standard low-level PID controllers are auto-tuned based on simulations of the HVAC thermal model, and aims at good tracking of the set points. One benefit of such control structure is that the PID dynamics are included in the predictive optimization: in this way, we are able to account for tracking transients, which are particularly useful if the HVAC is switched on and off depending on occupancy patterns. Experimental and simulation validation via a three-room test case at the Delft University of Technology shows the potential for a high degree of comfort while also reducing energy consumption. Full article
(This article belongs to the Special Issue Optimisation Models and Methods in Energy Systems)
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22 pages, 607 KiB  
Article
Fuzzy Portfolio Optimization of Power Generation Assets
by Barbara Glensk and Reinhard Madlener
Energies 2018, 11(11), 3043; https://doi.org/10.3390/en11113043 - 06 Nov 2018
Cited by 4 | Viewed by 4675
Abstract
Fuzzy theory is proposed as an alternative to the probabilistic approach for assessing portfolios of power plants, in order to capture the complex reality of decision-making processes. This paper presents different fuzzy portfolio selection models, where the rate of returns as well as [...] Read more.
Fuzzy theory is proposed as an alternative to the probabilistic approach for assessing portfolios of power plants, in order to capture the complex reality of decision-making processes. This paper presents different fuzzy portfolio selection models, where the rate of returns as well as the investor’s aspiration levels of portfolio return and risk are regarded as fuzzy variables. Furthermore, portfolio risk is defined as a downside risk, which is why a semi-mean-absolute deviation portfolio selection model is introduced. Finally, as an illustration, the models presented are applied to a selection of power generation mixes. The efficient portfolio results show that the fuzzy portfolio selection models with different definitions of membership functions as well as the semi-mean-absolute deviation model perform better than the standard mean-variance approach. Moreover, introducing membership functions for the description of investors’ aspiration levels for the expected return and risk shows how the knowledge of experts, and investors’ subjective opinions, can be better integrated in the decision-making process than with probabilistic approaches. Full article
(This article belongs to the Special Issue Optimisation Models and Methods in Energy Systems)
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22 pages, 3054 KiB  
Article
Optimal Energy Management of Building Microgrid Networks in Islanded Mode Considering Adjustable Power and Component Outages
by Van-Hai Bui, Akhtar Hussain, Hak-Man Kim and Yong-Hoon Im
Energies 2018, 11(9), 2351; https://doi.org/10.3390/en11092351 - 06 Sep 2018
Cited by 7 | Viewed by 2806
Abstract
In this paper, an optimal energy management scheme for islanded building microgrid networks is proposed. The proposed building microgrid network comprises of several inter-connected building microgrids (BMGs) and an external energy supplier. Each BMG has a local combined heat and power (CHP) unit, [...] Read more.
In this paper, an optimal energy management scheme for islanded building microgrid networks is proposed. The proposed building microgrid network comprises of several inter-connected building microgrids (BMGs) and an external energy supplier. Each BMG has a local combined heat and power (CHP) unit, energy storage, renewables and loads (electric and thermal). The external energy system comprises of an external CHP unit, chillers, electric heat pumps and heat pile line, for thermal energy storage. The BMGs can trade energy with other BMGs of the network and can also trade energy with the external energy supplier. In order to efficiently utilize the components of the BMGs and the network, the concept of adjustable power is adopted in this study. Adjustable power can reduce the operation cost of the network by increasing/decreasing the power of dispatchable units. In addition, the failure/recovery of components in the BMGs and the external system are also considered to analyze the performance of the proposed operation method. In order to optimally utilize the available resources during events, precedence among loads of BMGs and the external energy supplier is considered. Simulation results have proved the applicability of the proposed method for both normal islanded mode and with outage/recovery of equipment during the operation horizon. Finally, sensitivity analysis is carried out to analyze the impact of change in components’ parameters values on the saved cost of the network. Full article
(This article belongs to the Special Issue Optimisation Models and Methods in Energy Systems)
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24 pages, 5451 KiB  
Article
A Stochastic Inexact Robust Model for Regional Energy System Management and Emission Reduction Potential Analysis—A Case Study of Zibo City, China
by Yulei Xie, Linrui Wang, Guohe Huang, Dehong Xia and Ling Ji
Energies 2018, 11(8), 2108; https://doi.org/10.3390/en11082108 - 13 Aug 2018
Cited by 4 | Viewed by 3095
Abstract
In this study, in order to improve regional energy system adjustment, a multistage stochastic inexact robust programming (MSIRP) is proposed for electric-power generation planning and structure adjustment management under uncertainty. Scenario-based inexact multistage stochastic programming and stochastic robust optimization were integrated into general [...] Read more.
In this study, in order to improve regional energy system adjustment, a multistage stochastic inexact robust programming (MSIRP) is proposed for electric-power generation planning and structure adjustment management under uncertainty. Scenario-based inexact multistage stochastic programming and stochastic robust optimization were integrated into general programming to reflect uncertainties that were expressed as interval values and probability distributions in the objective function and constraints. An MSIRP-based energy system optimization model is proposed for electric-power structure management of Zibo City in Shandong Province, China. Three power demand scenarios associated with electric-power structure adjustment, imported electricity, and emission reduction were designed to obtain multiple decision schemes for supporting regional sustainable energy system development. The power generation schemes, imported electricity, and emissions of CO2 and air pollutants were analyzed. The results indicated that the model can effectively not only provide a more stable energy supply strategies and electric-power structure adjustment schemes, but also improve the balanced development between conventional and new clear power generation technologies under uncertainty. Full article
(This article belongs to the Special Issue Optimisation Models and Methods in Energy Systems)
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23 pages, 2701 KiB  
Article
A Framework for the Selection of Optimum Offshore Wind Farm Locations for Deployment
by Varvara Mytilinou, Estivaliz Lozano-Minguez and Athanasios Kolios
Energies 2018, 11(7), 1855; https://doi.org/10.3390/en11071855 - 16 Jul 2018
Cited by 28 | Viewed by 4843
Abstract
This research develops a framework to assist wind energy developers to select the optimum deployment site of a wind farm by considering the Round 3 available zones in the UK. The framework includes optimization techniques, decision-making methods and experts’ input in order to [...] Read more.
This research develops a framework to assist wind energy developers to select the optimum deployment site of a wind farm by considering the Round 3 available zones in the UK. The framework includes optimization techniques, decision-making methods and experts’ input in order to support investment decisions. Further, techno-economic evaluation, life cycle costing (LCC) and physical aspects for each location are considered along with experts’ opinions to provide deeper insight into the decision-making process. A process on the criteria selection is also presented and seven conflicting criteria are being considered for implementation in the technique for the order of preference by similarity to the ideal solution (TOPSIS) method in order to suggest the optimum location that was produced by the nondominated sorting genetic algorithm (NSGAII). For the given inputs, Seagreen Alpha, near the Isle of May, was found to be the most probable solution, followed by Moray Firth Eastern Development Area 1, near Wick, which demonstrates by example the effectiveness of the newly introduced framework that is also transferable and generic. The outcomes are expected to help stakeholders and decision makers to make better informed and cost-effective decisions under uncertainty when investing in offshore wind energy in the UK. Full article
(This article belongs to the Special Issue Optimisation Models and Methods in Energy Systems)
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