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Mathematics
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Mathematical Programming Methods in Energy Optimization
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Mathematical Programming Methods in Energy Optimization

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: closed (15 June 2024) | Viewed by 8751

Special Issue Editors

Dr. Aspassia Daskalopulu

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Thessaly, 382 21 Volos, Greece
Interests: formal models; multiagent systems; artificial intelligence applications for energy optimization; smart grids
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dimitrios Bargiotas

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Thessaly, 38333 Volos, Greece
Interests: power systems; control systems; industrial automation; smart grids

Special Issue Information

Dear Colleagues,

The call for energy optimization has been increasingly pressing in recent years, in light of population growth, climate change, resource scarcity and the expansion of industry and commerce. Energy optimization can be addressed: (i) at the level of energy production and the integration of multiple energy sources (some of which may be volatile) into the Grid; (ii) at the level of energy distribution to cover the (often fluctuating) needs of individual and corporate consumers; and (iii) at the level of consumption to reduce waste and climate impact. Much research effort has concentrated on the application of artificial intelligence techniques to such problems, especially machine learning, given the increasing availability of consumption data afforded by smart meters, with considerable advances especially in relation to load forecasting, demand response management and price forecasting.

In this Special Issue, we want to focus on the application of mathematical programming methods to the area of energy optimization, because they provide a modelling framework within which multi-objective optimization, uncertainty handling, process synthesis, constraint satisfaction, stochasticity, nonlinearity, and so on, all features present in energy optimization problems, can be fruitfully integrated and explored.

The purpose of this Special Issue is to stimulate the exchange of current research ideas and serve as a reference point that will include recent advancements in this critical area so as to be of use to researchers in diverse disciplines.

Dr. Aspassia Daskalopulu
Prof. Dr. Dimitrios Bargiotas
Guest Editors

Manuscript Submission Information

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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

  • energy optimization
  • sustainability
  • mathematical programming
  • linear programming
  • nonlinear programming
  • uncertainty
  • stochastic programming
  • genetic algorithms
  • load forecasting
  • demand response management
  • price forecasting

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Published Papers (4 papers)

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21 pages, 2821 KiB  
Article
Enhanced Automated Deep Learning Application for Short-Term Load Forecasting
by Vasileios Laitsos, Georgios Vontzos, Dimitrios Bargiotas, Aspassia Daskalopulu and Lefteri H. Tsoukalas
Mathematics 2023, 11(13), 2912; https://doi.org/10.3390/math11132912 - 28 Jun 2023
Cited by 7 | Viewed by 2286
Abstract
In recent times, the power sector has become a focal point of extensive scientific interest, driven by a convergence of factors, such as mounting global concerns surrounding climate change, the persistent increase in electricity prices within the wholesale energy market, and the surge [...] Read more.
In recent times, the power sector has become a focal point of extensive scientific interest, driven by a convergence of factors, such as mounting global concerns surrounding climate change, the persistent increase in electricity prices within the wholesale energy market, and the surge in investments catalyzed by technological advancements across diverse sectors. These evolving challenges have necessitated the emergence of new imperatives aimed at effectively managing energy resources, ensuring grid stability, bolstering reliability, and making informed decisions. One area that has garnered particular attention is the accurate prediction of end-user electricity load, which has emerged as a critical facet in the pursuit of efficient energy management. To tackle this challenge, machine and deep learning models have emerged as popular and promising approaches, owing to their having remarkable effectiveness in handling complex time series data. In this paper, the development of an algorithmic model that leverages an automated process to provide highly accurate predictions of electricity load, specifically tailored for the island of Thira in Greece, is introduced. Through the implementation of an automated application, an array of deep learning forecasting models were meticulously crafted, encompassing the Multilayer Perceptron, Long Short-Term Memory (LSTM), One Dimensional Convolutional Neural Network (CNN-1D), hybrid CNN–LSTM, Temporal Convolutional Network (TCN), and an innovative hybrid model called the Convolutional LSTM Encoder–Decoder. Through evaluation of prediction accuracy, satisfactory performance across all the models considered was observed, with the proposed hybrid model showcasing the highest level of accuracy. These findings underscore the profound significance of employing deep learning techniques for precise forecasting of electricity demand, thereby offering valuable insights with which to tackle the multifaceted challenges encountered within the power sector. By adopting advanced forecasting methodologies, the electricity sector moves towards greater efficiency, resilience and sustainability. Full article
(This article belongs to the Special Issue Mathematical Programming Methods in Energy Optimization)
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22 pages, 5927 KiB  
Article
Grey Wolf Optimizer for RES Capacity Factor Maximization at the Placement Planning Stage
by Andrey M. Bramm, Stanislav A. Eroshenko, Alexandra I. Khalyasmaa and Pavel V. Matrenin
Mathematics 2023, 11(11), 2545; https://doi.org/10.3390/math11112545 - 1 Jun 2023
Cited by 7 | Viewed by 1598
Abstract
At the current stage of the integration of renewable energy sources into the power systems of many countries, requirements for compliance with established technical characteristics are being applied to power generation. One such requirement is the installed capacity utilization factor, which is extremely [...] Read more.
At the current stage of the integration of renewable energy sources into the power systems of many countries, requirements for compliance with established technical characteristics are being applied to power generation. One such requirement is the installed capacity utilization factor, which is extremely important for optimally placing power facilities based on renewable energy sources and for the successful development of renewable energy. Efficient placement maximizes the installed capacity utilization factor of a power facility, increasing energy efficiency and the payback period. The installed capacity utilization factor depends on the assumed meteorological factors relating to geographical location and the technical characteristics of power generation. However, the installed capacity utilization factor cannot be accurately predicted, since it is necessary to know the volume of electricity produced by the power facility. A novel approach to the optimization of placement of renewable energy source power plants and their capacity factor forecasting was proposed in this article. This approach combines a machine learning forecasting algorithm (random forest regressor) with a metaheuristic optimization algorithm (grey wolf optimizer). Although the proposed approach assumes the use of only open-source data, the simulations show better results than commonly used algorithms, such as random search, particle swarm optimizer, and firefly algorithm. Full article
(This article belongs to the Special Issue Mathematical Programming Methods in Energy Optimization)
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23 pages, 2972 KiB  
Article
Improved Golden Jackal Optimization for Optimal Allocation and Scheduling of Wind Turbine and Electric Vehicles Parking Lots in Electrical Distribution Network Using Rosenbrock’s Direct Rotation Strategy
by Jing Yang, Jiale Xiong, Yen-Lin Chen, Por Lip Yee, Chin Soon Ku and Manoochehr Babanezhad
Mathematics 2023, 11(6), 1415; https://doi.org/10.3390/math11061415 - 15 Mar 2023
Cited by 15 | Viewed by 2119
Abstract
In this paper, a multi-objective allocation and scheduling of wind turbines and electric vehicle parking lots are performed in an IEEE 33-bus radial distribution network to reach the minimum annual costs of power loss, purchased grid energy, wind energy, PHEV energy, battery degradation [...] Read more.
In this paper, a multi-objective allocation and scheduling of wind turbines and electric vehicle parking lots are performed in an IEEE 33-bus radial distribution network to reach the minimum annual costs of power loss, purchased grid energy, wind energy, PHEV energy, battery degradation cost, and network voltage deviations. Decision variables, such as the site and size of wind turbines and electric parking lots in the distribution system, are found using an improved golden jackal optimization (IGJO) algorithm based on Rosenbrock’s direct rotational (RDR) strategy. The results showed that the IGJO finds the optimal solution with a lower convergence tolerance and a better (lower) objective function value compared to conventional GJO, the artificial electric field algorithm (AEFA), particle swarm optimization (PSO), and manta ray foraging optimization (MRFO) methods. The results showed that using the proposed method based on the IGJO, the energy loss cost, grid energy cost, and network voltage deviations were reduced by 29.76%, 65.86%, and 18.63%, respectively, compared to the base network. Moreover, the statistical analysis results proved their superiority compared to the conventional GJO, AEFA, PSO, and MRFO algorithms. Moreover, considering vehicles battery degradation costs, the losses cost, grid energy cost, and network voltage deviations have been reduced by 3.28%, 1.07%, and 4.32%, respectively, compared to the case without battery degradation costs. In addition, the results showed that the decrease in electric vehicle availability causes increasing losses for grid energy costs and weakens the network voltage profile, and vice versa. Full article
(This article belongs to the Special Issue Mathematical Programming Methods in Energy Optimization)
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19 pages, 899 KiB  
Article
Double Auction Offloading for Energy and Cost Efficient Wireless Networks
by Alexandra Bousia, Aspassia Daskalopulu and Elpiniki I. Papageorgiou
Mathematics 2022, 10(22), 4231; https://doi.org/10.3390/math10224231 - 12 Nov 2022
Cited by 1 | Viewed by 1393
Abstract
Network infrastructure sharing and mobile traffic offloading are promising technologies for Heterogeneous Networks (HetNets) to provide energy and cost effective services. In order to decrease the energy requirements and the capital and operational expenditures, Mobile Network Operators (MNOs) and third parties cooperate dynamically [...] Read more.
Network infrastructure sharing and mobile traffic offloading are promising technologies for Heterogeneous Networks (HetNets) to provide energy and cost effective services. In order to decrease the energy requirements and the capital and operational expenditures, Mobile Network Operators (MNOs) and third parties cooperate dynamically with changing roles leading to a novel market model, where innovative challenges are introduced. In this paper, a novel resource sharing and offloading algorithm is introduced based on a double auction mechanism where MNOs and third parties buy and sell capacity and roam their traffic among each other. For low traffic periods, Base Stations (BSs) and Small Cells (SCs) can even be switched off in order to gain even more in energy and cost. Due to the complexity of the scenario, we adopt the multi-objective optimization theory to capture the conflicting interests of the participating entities and we design an iterative double auction algorithm that ensures the efficient operation of the market. Additionally, the selection of the appropriate time periods to apply the proposed algorithm is of great importance. Thus, we propose a machine learning technique for traffic load prediction and for the selection of the most effective time periods to offload traffic and switch off the Base Stations. Analytical and experimental results are presented to assess the performance of the algorithm. Full article
(This article belongs to the Special Issue Mathematical Programming Methods in Energy Optimization)
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