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Multi-Utility Energy System Optimization

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (15 May 2021) | Viewed by 18680

Special Issue Editor


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Guest Editor
Fraunhofer Institut for Windenergy and Energysystemtechnology (IWES), Königstor 59, 34119 Kassel, Germany
Interests: concepts of expansion grid planning (power, heat, gas grids); central and decentral grid control (power, heat, gas grids); energy management systems; hybrid-energy networks; meta-heuristics; model-predictive control; agent-based operation; economic optimization; grid integration of flexibilities and storage systems; robust optimization; bi-level optimization; distributed optimizations; hybrid energy systems (e.g. PV, CHP, battery, thermal storage); generation and analysis of load profiles and other time series; open source energy data; data analysis of energy grids

Special Issue Information

Dear Colleagues,


A successful transition to an energy system largely based on renewable energy resources cannot be achieved in the electricity sector alone. The necessary flexibility and efficiency call for a combined approach that involves heating, cooling, and mobility as well as gas and water grids. These multi-utility approaches are, however, very complex, include various feedback loops and interdependencies, and operate on different time scales. Recent advances in mathematical optimization and machine learning show great potential in finding the optimal economical or ecological solution in these different domains and for the different actors involved.
Over the last few decades, the usage of optimization in energy science has increased drastically. Finding an optimal solution for complex problems almost automatically, without being limited to a handful of use cases and examples, has influenced decision-making processes and the development of new products and business cases. The optimization of a single energy system, like enabling a smart home to include weather forecasts, is already a standard in the heating sector and also used in the electricity sector. The combined optimization of two or more different energy systems which share common boundary conditions is making progress, as is the optimization of one system towards two or more cost-functions.


The scope of this Special Issue is to apply optimization methods to the problem of a multi-utility energy system. Such a system can cover a whole nation or continent. It can also be a small geographical region, a city or even a single building that contains different utilities. Optimization goals can be as varied as the purpose of the study, from minimal cost or maximal revenue to minimal CO2 emissions, achieved in planning, management or control.
The scope of optimization models covered in this issue is not limited to classical mathematical modeling and heuristics but can also include the application of artificial intelligence methods like machine learning or multi-agent models.


The purpose of this Special Issue is to cover a broad spectrum of reviews and original research contributions on the topic of a combined simulation and modeling of different utilities. With a wide range of different ideas, approaches and use cases can include gas, electricity, heat, and water networks but also include waste disposal and other utilities which are typical for a local energy provider.
The state of the art and current research is still mostly separated in different publishing societies (e.g., power, heat, building), distributing scientific contributions in various different journals. Our goal is to bring these excellent studies and results from multiple topics together in one Special Issue.


The current Special Issue invites contributions on the topic of multi-utility energy system optimization, of both the energy system on the national and international scale as well as single multi-utility energy systems in the urban and rural environment. Of special interest are submissions including mathematical optimization methods like heuristics, MILP, distributed optimization, bi-level optimization, robust optimization, agent-based optimization, neural networks, deep learning, etc. applied on more than one utility (power, heat, gas, water, waste, etc.) We welcome both original research articles and review articles.

Dr. Tanja M. Kneiske
Guest Editor

Manuscript Submission Information

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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. Sustainability 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 2400 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

  • multi-utility energy systems
  • energy system analysis
  • grid planning
  • grid control
  • energy management
  • hybrid energy networks
  • meta-heuristics
  • model predictive control
  • multi-agents
  • economic optimization
  • flexibilities
  • storage systems
  • robust optimization
  • bi-level optimization
  • distributed optimization
  • heat
  • power
  • gas
  • smart energy
  • system integration
  • holistic design
  • synergies
  • neural networks
  • deep learning

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Published Papers (7 papers)

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Research

25 pages, 448 KiB  
Article
Slime Mold Algorithm for Optimal Reactive Power Dispatch Combining with Renewable Energy Sources
by Salah K. ElSayed and Ehab E. Elattar
Sustainability 2021, 13(11), 5831; https://doi.org/10.3390/su13115831 - 22 May 2021
Cited by 11 | Viewed by 2010
Abstract
The optimal reactive power dispatch (ORPD) is a complex, nonlinear, and constrained optimization problem. This paper presents the application of a new metaheuristic optimization technique called the slime mold algorithm (SMA) for solving the developed objective function of ORPD combining with renewable energy [...] Read more.
The optimal reactive power dispatch (ORPD) is a complex, nonlinear, and constrained optimization problem. This paper presents the application of a new metaheuristic optimization technique called the slime mold algorithm (SMA) for solving the developed objective function of ORPD combining with renewable energy sources. The presented objective function is to minimize the total operating cost of the system through the minimization of all reactive power costs, total real power loss, voltage deviation of load buses, the system overload and improve voltage stability. The formulation of the ORPD problem combining with renewable energy sources with five different objective functions is then converted to a coefficient single objective function achieving various operating constraints. The SMA technique has been tested and proven on the IEEE 30-bus system and IEEE-118 bus system using different scenarios. Five different scenarios, with and without renewable energy sources, are presented on the two-test system and the simulation results of the SMA is compared to some optimization techniques from the literature under the same test system data, optimal control variables, and operational constraints. The superiority and effectiveness of the SMA are proven through comparison with the other obtained results from recently published optimization techniques. Full article
(This article belongs to the Special Issue Multi-Utility Energy System Optimization)
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20 pages, 1628 KiB  
Article
Lagrangian Relaxation Based on Improved Proximal Bundle Method for Short-Term Hydrothermal Scheduling
by Zhiyu Yan, Shengli Liao, Chuntian Cheng, Josué Medellín-Azuara and Benxi Liu
Sustainability 2021, 13(9), 4706; https://doi.org/10.3390/su13094706 - 22 Apr 2021
Cited by 4 | Viewed by 1930
Abstract
Short-term hydrothermal scheduling (STHS) can improve water use efficiency, reduce carbon emissions, and increase economic benefits by optimizing the commitment and dispatch of hydro and thermal generating units together. However, limited by the large system scale and complex hydraulic and electrical constraints, STHS [...] Read more.
Short-term hydrothermal scheduling (STHS) can improve water use efficiency, reduce carbon emissions, and increase economic benefits by optimizing the commitment and dispatch of hydro and thermal generating units together. However, limited by the large system scale and complex hydraulic and electrical constraints, STHS poses great challenges in modeling for operators. This paper presents an improved proximal bundle method (IPBM) within the framework of Lagrangian relaxation for STHS, which incorporates the expert system (ES) technique into the proximal bundle method (PBM). In IPBM, initial values of Lagrange multipliers are firstly determined using the linear combination of optimal solutions in the ES. Then, each time PBM declares a null step in the iterations, the solution space is inferred from the ES, and an orthogonal design is performed in the solution space to derive new updates of the Lagrange multipliers. A case study in a large-scale hydrothermal system in China is implemented to demonstrate the effectiveness of the proposed method. Results in different cases indicate that IPBM is superior to standard PBM in global search ability and computational efficiency, providing an alternative for STHS. Full article
(This article belongs to the Special Issue Multi-Utility Energy System Optimization)
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23 pages, 3034 KiB  
Article
Genetic Algorithm for Energy Commitment in a Power System Supplied by Multiple Energy Carriers
by Mohammad Dehghani, Mohammad Mardaneh, Om P. Malik, Josep M. Guerrero, Carlos Sotelo, David Sotelo, Morteza Nazari-Heris, Kamal Al-Haddad and Ricardo A. Ramirez-Mendoza
Sustainability 2020, 12(23), 10053; https://doi.org/10.3390/su122310053 - 2 Dec 2020
Cited by 26 | Viewed by 2960
Abstract
In recent years, energy consumption has notably been increasing. This poses a challenge to the power grid operators due to the management and control of the energy supply and consumption. Here, energy commitment is an index criterion useful to specify the quality level [...] Read more.
In recent years, energy consumption has notably been increasing. This poses a challenge to the power grid operators due to the management and control of the energy supply and consumption. Here, energy commitment is an index criterion useful to specify the quality level and the development of human life. Henceforth, continuity of long-term access to resources and energy delivery requires an appropriate methodology that must consider energy scheduling such as an economic and strategic priority, in which primary energy carriers play an important role. The integrated energy networks such as power and gas systems lead the possibility to minimize the operating costs; this is based on the conversion of energy from one form to another and considering the starting energy in various types. Therefore, the studies toward multi-carrier energy systems are growing up taking into account the interconnection among various energy carriers and the penetration of energy storage technologies in such systems. In this paper, using dynamic programming and genetic algorithm, the energy commitment of an energy network that includes gas and electrical energy is carried out. The studied multi-carrier energy system has considered defending parties including transportation, industrial and agriculture sectors, residential, commercial, and industrial consumers. The proposed study is mathematically modeled and implemented on an energy grid with four power plants and different energy consumption sectors for a 24-h energy study period. In this simulation, an appropriate pattern of using energy carriers to supply energy demand is determined. Simulation results and analysis show that energy carriers can be used efficiently using the proposed energy commitment method. Full article
(This article belongs to the Special Issue Multi-Utility Energy System Optimization)
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23 pages, 4060 KiB  
Article
Optimum Design and Control of Heat Pumps for Integration into Thermohydraulic Networks
by Maximilian Sporleder, Max Burkhardt, Thomas Kohne, Daniel Moog and Matthias Weigold
Sustainability 2020, 12(22), 9421; https://doi.org/10.3390/su12229421 - 12 Nov 2020
Cited by 2 | Viewed by 2042
Abstract
Germany has become one of the leading players in the transformation of the electricity sector, now having up to 42% of electricity coming from renewable sources. However, the transformation of the heating sector is still in its infancy, and especially the provision of [...] Read more.
Germany has become one of the leading players in the transformation of the electricity sector, now having up to 42% of electricity coming from renewable sources. However, the transformation of the heating sector is still in its infancy, and especially the provision of industrial process heating is highly dependent on unsustainable fuels. One of the most promising heating technologies for renewable energies is power-to-heat, especially heat pump technology, as it can use renewable electricity to generate heat efficiently. This research explores the economic and technical boundary conditions regarding the integration of heat pumps into existing industrial thermohydraulic heating and cooling networks. To calculate the optimum design and control of heat pumps, a mixed-integer linear programming model (MILP) is developed. The model seeks the most cost-efficient configuration of heat pumps and stratified thermal storage tanks. Additionally, it optimizes the operation of all energy converters and stratified thermal storage tanks to meet a specified heating and cooling demand over one year. The objective function is modeled after the net present value (NPV) method and considers capital expenditures (costs for heat pumps and stratified thermal storage tanks) and operational expenditures (electricity costs and costs for conventional heating and cooling). The comparison of the results via a simulation model reveals an accuracy of more than 90%. Full article
(This article belongs to the Special Issue Multi-Utility Energy System Optimization)
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15 pages, 1662 KiB  
Article
Unified Energy Agents for Combined District Heating and Electrical Network Simulation
by Nils Loose, Christian Thommessen, Jan Mehlich, Christian Derksen and Stefan Eicker
Sustainability 2020, 12(21), 9301; https://doi.org/10.3390/su12219301 - 9 Nov 2020
Cited by 6 | Viewed by 2595
Abstract
A sustainable and climate-friendly energy supply needs flexible and efficient distribution systems. Key factors to implement this kind of systems are intelligent coordination (smart grid approaches) and the integration of different energy sectors. This article introduces the unified energy agent as an agent-based [...] Read more.
A sustainable and climate-friendly energy supply needs flexible and efficient distribution systems. Key factors to implement this kind of systems are intelligent coordination (smart grid approaches) and the integration of different energy sectors. This article introduces the unified energy agent as an agent-based approach for a comprehensive modelling and control of energy conversion systems. This approach enables both the simulation and optimization of coupled energy networks, and then in a next step, the development of corresponding smart grid solutions to be applied in the field. Its applicability for the simulation of coupled networks is presented by a real-world use-case of an innovative combined heat and electrical network, which was implemented for the city of Lemgo, Germany. Preliminary results from the project are discussed and an outlook on future work is given. Full article
(This article belongs to the Special Issue Multi-Utility Energy System Optimization)
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42 pages, 10277 KiB  
Article
Analysis of Dependencies between Gas and Electricity Distribution Grid Planning and Building Energy Retrofit Decisions
by Daniel Then, Patrick Hein, Tanja M. Kneiske and Martin Braun
Sustainability 2020, 12(13), 5315; https://doi.org/10.3390/su12135315 - 1 Jul 2020
Cited by 10 | Viewed by 2963
Abstract
Most macroeconomic studies predict a decline in final energy demand and the use of natural gas in the heating sector in Europe. In the course of building retrofitting, gas-based heating systems are predominantly replaced by electricity-based solutions. This influences the business models of [...] Read more.
Most macroeconomic studies predict a decline in final energy demand and the use of natural gas in the heating sector in Europe. In the course of building retrofitting, gas-based heating systems are predominantly replaced by electricity-based solutions. This influences the business models of electricity and especially gas distribution network operators (DNOs), where grid charges tend to rise. The resulting feedback effect could accelerate the decrease of demand and finally lead to the defection of the gas grid—an effect that has been neglected in energy system analysis so far. We present a multi-agent simulation with a rule-based gas and electricity DNO model and a building retrofit optimization model to analyze these interdependencies during the transformation path, focusing on the role of different technical, economic, and regulatory triggers. Our case studies for a real grid area of a German city shows that an interplay of the gas and electricity DNO’s strategy, as well as the building-, heating system-, grid-, and trigger-configuration, determine the decision on the extension, continuation, or defection of the gas grid infrastructure. Finally, strategies for how to reduce the risk of a gas grid defection, which are relevant for DNOs, policy makers, and creators of macro-economic models, are discussed. Full article
(This article belongs to the Special Issue Multi-Utility Energy System Optimization)
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20 pages, 3619 KiB  
Article
An Efficient Representation Using Harmony Search for Solving the Virtual Machine Consolidation
by MinJun Kim, JuneSeok Hong and Wooju Kim
Sustainability 2019, 11(21), 6030; https://doi.org/10.3390/su11216030 - 30 Oct 2019
Cited by 7 | Viewed by 2619
Abstract
A data center with a large number of servers, large storage, and many network devices requires power for cooling to reduce heat generation, air conditioning, and emergency power generation facilities, in addition to power for operation internally consumed by infrastructure equipment. The power [...] Read more.
A data center with a large number of servers, large storage, and many network devices requires power for cooling to reduce heat generation, air conditioning, and emergency power generation facilities, in addition to power for operation internally consumed by infrastructure equipment. The power consumed by data centers worldwide makes up a large proportion. Although the size of data centers is expected to increase, we are already faced with power problems because stability is prioritized over efficiency when operating data centers in order to meet the Service Level Agreement (SLA) conditions. Most data centers are in a virtualization environment, and virtual machine consolidation using physical machine (PM) transitions to the idle mode through virtual machine (VM) migration has been suggested as one of the most effective ways to reduce the amount of power usage in a data center. This study takes into account the characteristics of virtualization environments and presents an algorithm that effectively solves VM consolidation (VMC) through operator design using a grouping representation method and a meta-heuristic method known as harmony search. Full article
(This article belongs to the Special Issue Multi-Utility Energy System Optimization)
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