Journal articles on the topic 'Optimisation; renewable energy; computational complexity'
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Dkhili, Nouha, David Salas, Julien Eynard, Stéphane Thil, and Stéphane Grieu. "Innovative Application of Model-Based Predictive Control for Low-Voltage Power Distribution Grids with Significant Distributed Generation." Energies 14, no. 6 (March 23, 2021): 1773. http://dx.doi.org/10.3390/en14061773.
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In past decades, the deployment of renewable-energy-based power generators, namely solar photovoltaic (PV) power generators, has been projected to cause a number of new difficulties in planning, monitoring, and control of power distribution grids. In this paper, a control scheme for flexible asset management is proposed with the aim of closing the gap between power supply and demand in a suburban low-voltage power distribution grid with significant penetration of solar PV power generation while respecting the different systems’ operational constraints, in addition to the voltage constraints prescribed by the French distribution grid operator (ENEDIS). The premise of the proposed strategy is the use of a model-based predictive control (MPC) scheme. The flexible assets used in the case study are a biogas plant and a water tower. The mixed-integer nonlinear programming (MINLP) setting due to the water tower ON/OFF controller greatly increases the computational complexity of the optimisation problem. Thus, one of the contributions of the paper is a new formulation that solves the MINLP problem as a smooth continuous one without having recourse to relaxation. To determine the most adequate size for the proposed scheme’s sliding window, a sensitivity analysis is carried out. Then, results given by the scheme using the previously determined window size are analysed and compared to two reference strategies based on a relaxed problem formulation: a single optimisation yielding a weekly operation planning and a MPC scheme. The proposed problem formulation proves effective in terms of performance and maintenance of acceptable computational complexity. For the chosen sliding window, the control scheme drives the power supply/demand gap down from the initial one up to 38%.
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Et.al, Samuel Jonas Yeboah. "Gravitational Search Algorithm Based Automatic Load Frequency Control for Multi-Area Interconnected Power System." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 10, 2021): 4548–68. http://dx.doi.org/10.17762/turcomat.v12i3.1845.
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Demand and frequency deviation is gaining more popularity in power system research especially with multiple power systems interconnections and operations as a result of the complexity of power system network, network upgrade and renewable energy sources integration. However, stability of the power system with respect to momentarily fault of Load Frequency Control (LFC) models, in terms of time taken for the fault to settle, magnitude of overshoot and Steady-State Error (SSE) margin, still remain a challenge to the various proposed LFC designs for power system stability. This paper proposes an intelligent demand and frequency variations controller for a four-area interconnected power system using Gravitational Search Algorithm (GSA) optimisation technique. Proportional Integral Derivative (PID) controller and Gravitational Search Algorithm (GSA) were integrated and implemented on the interconnected power system. The optimised GSA-PID controller demonstrated robustness and superiority with time taken for the instability to settle and maximum overshoot in all the four areas as compared to results with Particle Swarm Optimisation (PSO) PID controller and conventional PID controller under 1% and 5% load perturbation. The settling time in all the areas produced tremendous results with GSA-PID controller compared to the results of PSO-PID and conventional PID, the performance of GSA-PID controller shows better dynamic responses with superior damping, less overshoot, minimum oscillations and shorter transient duration.
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Anastasiadis, Eleftherios, Panagiotis Angeloudis, Daniel Ainalis, Qiming Ye, Pei-Yuan Hsu, Renos Karamanis, Jose Escribano Macias, and Marc Stettler. "On the Selection of Charging Facility Locations for EV-Based Ride-Hailing Services: A Computational Case Study." Sustainability 13, no. 1 (December 26, 2020): 168. http://dx.doi.org/10.3390/su13010168.
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The uptake of Electric Vehicles (EVs) is rapidly changing the landscape of urban mobility services. Transportation Network Companies (TNCs) have been following this trend by increasing the number of EVs in their fleets. Recently, major TNCs have explored the prospect of establishing privately owned charging facilities that will enable faster and more economic charging. Given the scale and complexity of TNC operations, such decisions need to consider both the requirements of TNCs and local planning regulations. Therefore, an optimisation approach is presented to model the placement of CSs with the objective of minimising the empty time travelled to the nearest CS for recharging as well as the installation cost. An agent based simulation model has been set in the area of Chicago to derive the recharging spots of the TNC vehicles, and in turn derive the charging demand. A mathematical formulation for the resulting optimisation problem is provided alongside a genetic algorithm that can produce solutions for large problem instances. Our results refer to a representative set of the total data for Chicago and indicate that nearly 180 CSs need to be installed to handle the demand of a TNC fleet of 3000 vehicles.
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Adegbenro, Akinkunmi, Michael Short, and Claudio Angione. "An Integrated Approach to Adaptive Control and Supervisory Optimisation of HVAC Control Systems for Demand Response Applications." Energies 14, no. 8 (April 8, 2021): 2078. http://dx.doi.org/10.3390/en14082078.
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Heating, ventilating, and air-conditioning (HVAC) systems account for a large percentage of energy consumption in buildings. Implementation of efficient optimisation and control mechanisms has been identified as one crucial way to help reduce and shift HVAC systems’ energy consumption to both save economic costs and foster improved integration with renewables. This has led to the development of various control techniques, some of which have produced promising results. However, very few of these control mechanisms have fully considered important factors such as electricity time of use (TOU) price information, occupant thermal comfort, computational complexity, and nonlinear HVAC dynamics to design a demand response schema. In this paper, a novel two-stage integrated approach for such is proposed and evaluated. A model predictive control (MPC)-based optimiser for supervisory setpoint control is integrated with a digital parameter-adaptive controller for use in a demand response/demand management environment. The optimiser is designed to shift the heating load (and hence electrical load) to off-peak periods by minimising a trade-off between thermal comfort and electricity costs, generating a setpoint trajectory for the inner loop HVAC tracking controller. The tracking controller provides HVAC model information to the outer loop for calibration purposes. By way of calibrated simulations, it was found that significant energy saving and cost reduction could be achieved in comparison to a traditional on/off or variable HVAC control system with a fixed setpoint temperature.
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Liu, Bing, Bowen Xu, Tong He, Wei Yu, and Fanghong Guo. "Hybrid Deep Reinforcement Learning Considering Discrete-Continuous Action Spaces for Real-Time Energy Management in More Electric Aircraft." Energies 15, no. 17 (August 30, 2022): 6323. http://dx.doi.org/10.3390/en15176323.
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The increasing number and functional complexity of power electronics in more electric aircraft (MEA) power systems have led to a high degree of complexity in modelling and computation, making real-time energy management a formidable challenge, and the discrete-continuous action space of the MEA system under consideration also poses a challenge to existing DRL algorithms. Therefore, this paper proposes an optimisation strategy for real-time energy management based on hybrid deep reinforcement learning (HDRL). An energy management model of the MEA power system is constructed for the analysis of generators, buses, loads and energy storage system (ESS) characteristics, and the problem is described as a multi-objective optimisation problem with integer and continuous variables. The problem is solved by combining a duelling double deep Q network (D3QN) algorithm with a deep deterministic policy gradient (DDPG) algorithm, where the D3QN algorithm deals with the discrete action space and the DDPG algorithm with the continuous action space. These two algorithms are alternately trained and interact with each other to maximize the long-term payoff of MEA. Finally, the simulation results show that the effectiveness of the method is verified under different generator operating conditions. For different time lengths T, the method always obtains smaller objective function values compared to previous DRL algorithms, is several orders of magnitude faster than commercial solvers, and is always less than 0.2 s, despite a slight shortfall in solution accuracy. In addition, the method has been validated on a hardware-in-the-loop simulation platform.
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Torres, César, and Antonio Valero. "The Exergy Cost Theory Revisited." Energies 14, no. 6 (March 13, 2021): 1594. http://dx.doi.org/10.3390/en14061594.
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This paper reviews the fundamentals of the Exergy Cost Theory, an energy cost accounting methodology to evaluate the physical costs of products of energy systems and their associated waste. Besides, a mathematical and computationally approach is presented, which will allow the practitioner to carry out studies on production systems regardless of their structural complexity. The exergy cost theory was proposed in 1986 by Valero et al. in their “General theory of exergy savings”. It has been recognized as a powerful tool in the analysis of energy systems and has been applied to the evaluation of energy saving alternatives, local optimisation, thermoeconomic diagnosis, or industrial symbiosis. The waste cost formation process is presented from a thermodynamic perspective rather than the economist’s approach. It is proposed to consider waste as external irreversibilities occurring in plant processes. A new concept, called irreversibility carrier, is introduced, which will allow the identification of the origin, transfer, partial recovery, and disposal of waste.
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Amini, Erfan, Danial Golbaz, Rojin Asadi, Mahdieh Nasiri, Oğuzhan Ceylan, Meysam Majidi Nezhad, and Mehdi Neshat. "A Comparative Study of Metaheuristic Algorithms for Wave Energy Converter Power Take-Off Optimisation: A Case Study for Eastern Australia." Journal of Marine Science and Engineering 9, no. 5 (May 1, 2021): 490. http://dx.doi.org/10.3390/jmse9050490.
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One of the most encouraging sorts of renewable energy is ocean wave energy. In spite of a large number of investigations in this field during the last decade, wave energy technologies are recognised as neither mature nor broadly commercialised compared to other renewable energy technologies. In this paper, we develop and optimise Power Take-off (PTO) configurations of a well-known wave energy converter (WEC) called a point absorber. This WEC is a fully submerged buoy with three tethers, which was proposed and developed by Carnegie Clean Energy Company in Australia. Optimising the WEC’s PTO parameters is a challenging engineering problem due to the high dimensionality and complexity of the search space. This research compares the performance of five state-of-the-art metaheuristics (including Covariance Matrix Adaptation Evolution Strategy, Gray Wolf optimiser, Harris Hawks optimisation, and Grasshopper Optimisation Algorithm) based on the real wave scenario in Sydney sea state. The experimental achievements show that the Multiverse optimisation (MVO) algorithm performs better than the other metaheuristics applied in this work.
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Evins, Ralph. "A review of computational optimisation methods applied to sustainable building design." Renewable and Sustainable Energy Reviews 22 (June 2013): 230–45. http://dx.doi.org/10.1016/j.rser.2013.02.004.
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van der Heijde, Annelies Vandermeulen, Salenbien, and Helsen. "Integrated Optimal Design and Control of Fourth Generation District Heating Networks with Thermal Energy Storage." Energies 12, no. 14 (July 18, 2019): 2766. http://dx.doi.org/10.3390/en12142766.
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In the quest to increase the share of renewable and residual energy sources in our energy system, and to reduce its greenhouse gas emissions, district heating networks and seasonal thermal energy storage have the potential to play a key role. Different studies prove the techno-economic potential of these technologies but, due to the added complexity, it is challenging to design and control such systems. This paper describes an integrated optimal design and control algorithm, which is applied to the design of a district heating network with solar thermal collectors, seasonal thermal energy storage and excess heat injection. The focus is mostly on the choice of the size and location of these technologies and less on the network layout optimisation. The algorithm uses a two-layer program, namely with a design optimisation layer implemented as a genetic algorithm and an optimal control evaluation layer implemented using the Python optimal control problem toolbox called modesto. This optimisation strategy is applied to the fictional district energy system case of the city of Genk in Belgium. We show that this algorithm can find optimal designs with respect to multiple objective functions and that even in the cheaper, less renewable solutions, seasonal thermal energy storage systems are installed in large quantities.
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Fazeres-Ferradosa, Tiago, João Chambel, Francisco Taveira-Pinto, Paulo Rosa-Santos, Francisco V. C. Taveira-Pinto, Gianmaria Giannini, and Piet Haerens. "Scour Protections for Offshore Foundations of Marine Energy Harvesting Technologies: A Review." Journal of Marine Science and Engineering 9, no. 3 (March 8, 2021): 297. http://dx.doi.org/10.3390/jmse9030297.
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The offshore wind is the sector of marine renewable energy with the highest commercial development at present. The margin to optimise offshore wind foundations is considerable, thus attracting both the scientific and the industrial community. Due to the complexity of the marine environment, the foundation of an offshore wind turbine represents a considerable portion of the overall investment. An important part of the foundation’s costs relates to the scour protections, which prevent scour effects that can lead the structure to reach the ultimate and service limit states. Presently, the advances in scour protections design and its optimisation for marine environments face many challenges, and the latest findings are often bounded by stakeholder’s strict confidential policies. Therefore, this paper provides a broad overview of the latest improvements acquired on this topic, which would otherwise be difficult to obtain by the scientific and general professional community. In addition, this paper summarises the key challenges and recent advances related to offshore wind turbine scour protections. Knowledge gaps, recent findings and prospective research goals are critically analysed, including the study of potential synergies with other marine renewable energy technologies, as wave and tidal energy. This research shows that scour protections are a field of study quite challenging and still with numerous questions to be answered. Thus, optimisation of scour protections in the marine environment represents a meaningful opportunity to further increase the competitiveness of marine renewable energies.
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Cooper, M. C., and S. Zivny. "Tractable Triangles and Cross-Free Convexity in Discrete Optimisation." Journal of Artificial Intelligence Research 44 (July 27, 2012): 455–90. http://dx.doi.org/10.1613/jair.3598.
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The minimisation problem of a sum of unary and pairwise functions of discrete variables is a general NP-hard problem with wide applications such as computing MAP configurations in Markov Random Fields (MRF), minimising Gibbs energy, or solving binary Valued Constraint Satisfaction Problems (VCSPs). We study the computational complexity of classes of discrete optimisation problems given by allowing only certain types of costs in every triangle of variable-value assignments to three distinct variables. We show that for several computational problems, the only non- trivial tractable classes are the well known maximum matching problem and the recently discovered joint-winner property. Our results, apart from giving complete classifications in the studied cases, provide guidance in the search for hybrid tractable classes; that is, classes of problems that are not captured by restrictions on the functions (such as submodularity) or the structure of the problem graph (such as bounded treewidth). Furthermore, we introduce a class of problems with convex cardinality functions on cross-free sets of assignments. We prove that while imposing only one of the two conditions renders the problem NP-hard, the conjunction of the two gives rise to a novel tractable class satisfying the cross-free convexity property, which generalises the joint-winner property to problems of unbounded arity.
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Daniels, S. J., A. A. M. Rahat, G. R. Tabor, J. E. Fieldsend, and R. M. Everson. "Shape optimisation of the sharp-heeled Kaplan draft tube: Performance evaluation using Computational Fluid Dynamics." Renewable Energy 160 (November 2020): 112–26. http://dx.doi.org/10.1016/j.renene.2020.05.164.
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Adamson, Duncan, Argyrios Deligkas, Vladimir Gusev, and Igor Potapov. "On the Hardness of Energy Minimisation for Crystal Structure Prediction*." Fundamenta Informaticae 184, no. 3 (February 15, 2022): 181–203. http://dx.doi.org/10.3233/fi-2021-2096.
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Crystal Structure Prediction (CSP) is one of the central and most challenging problems in materials science and computational chemistry. In CSP, the goal is to find a configuration of ions in 3D space that yields the lowest potential energy. Finding an efficient procedure to solve this complex optimisation question is a well known open problem. Due to the exponentially large search space, the problem has been referred in several materials-science papers as “NP-Hard and very challenging” without a formal proof. This paper fills a gap in the literature providing the first set of formally proven NP-Hardness results for a variant of CSP with various realistic constraints. In particular, we focus on the problem of removal: the goal is to find a substructure with minimal potential energy, by removing a subset of the ions. Our main contributions are NP-Hardness results for the CSP removal problem, new embeddings of combinatorial graph problems into geometrical settings, and a more systematic exploration of the energy function to reveal the complexity of CSP. In a wider context, our results contribute to the analysis of computational problems for weighted graphs embedded into the three-dimensional Euclidean space.
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Drikakis, Dimitris, and Talib Dbouk. "The Role of Computational Science in Wind and Solar Energy: A Critical Review." Energies 15, no. 24 (December 18, 2022): 9609. http://dx.doi.org/10.3390/en15249609.
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This paper concerns technology challenges for the wind and solar sectors and the role of computational science in addressing the above. Wind energy challenges include understanding the atmospheric flow physics, complex wakes and their interaction with wind turbines, aeroelastic effects and the associated impact on materials, and optimisation of wind farms. Concentrated solar power technologies require an optimal configuration of solar dish technology and porous absorber in the volumetric solar receiver for efficiency and durability and to minimise the convective heat losses in the receiver. Computational fluid dynamics and heat transfer have advanced in terms of numerical methods and physics-based models and their implementation in high-performance computing facilities. Despite this progress, computational science requires further advancement to address the technological challenges of designing complex systems accurately and efficiently, as well as forecasting the system’s performance. Machine Learning models and optimisation techniques can maximise the performance of simulations and quantify uncertainties in the wind and solar energy technologies. However, in a similar vein, these methods require further development to reduce their computational uncertainties. The need to address the global energy challenges requires further investment in developing and validating computational science methods and physics-based models for accurate and numerically efficient predictions at different scales.
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Ullah, Zahid, Nayyar Hussain Mirjat, and Muhammad Baseer. "Optimisation and Management of Virtual Power Plants Energy Mix Trading Model." International Journal of Renewable Energy Development 11, no. 1 (September 30, 2021): 83–94. http://dx.doi.org/10.14710/ijred.2022.39295.
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. In this study, a robust optimisation method (ROM) is proposed with aim to achieve optimal scheduling of virtual power plants (VPPs) in the day-ahead electricity markets where electricity prices are highly uncertain. Our VPP is a collection of various distributed energy resources (DERs), flexible loads, and energy storage systems that are coordinated and operated as a single entity. In this study, an offer and bid-based energy trading mechanism is proposed where participating members in the VPP setting can sell or buy to/from the day-ahead electricity market to maximise social welfare (SW). SW is defined as the maximisation of end-users benefits and minimisation of energy costs. The optimisation problem is solved as a mixed-integer linear programming model taking the informed decisions at various levels of uncertainty of the market prices. The benefits of the proposed approach are consistency in solution accuracy and traceability due to less computational burden and this would be beneficial for the VPP operators. The robustness of the proposed mathematical model and method is confirmed in a case study approach using a distribution system with 18-buses. Simulation results illustrate that in the highest robustness scenario, profit is reduced marginally, however, the VPP showed robustness towards the day-ahead market (DAM) price uncertainty
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Zaghwan, Ashraf, and Indra Gunawan. "Energy Loss Impact in Electrical Smart Grid Systems in Australia." Sustainability 13, no. 13 (June 28, 2021): 7221. http://dx.doi.org/10.3390/su13137221.
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This research draws attention to the potential and contextual influences on energy loss in Australia’s electricity market and smart grid systems. It further examines barriers in the transition toward optimising the benefit opportunities between electricity demand and electricity supply. The main contribution of this study highlights the impact of individual end-users by controlling and automating individual home electricity profiles within the objective function set (AV) of optimum demand ranges. Three stages of analysis were accomplished to achieve this goal. Firstly, we focused on feasibility analysis using ‘weight of evidence’ (WOE) and ‘information value’ (IV) techniques to check sample data segmentation and possible variable reduction. Stage two of sensitivity analysis (SA) used a generalised reduced gradient algorithm (GRG) to detect and compare a nonlinear optimisation issue caused by end-user demand. Stage three of analysis used two methods adopted from the machine learning toolbox, piecewise linear distribution (PLD) and the empirical cumulative distribution function (ECDF), to test the normality of time series data and measure the discrepancy between them. It used PLD and ECDF to derive a nonparametric representation of the overall cumulative distribution function (CDF). These analytical methods were all found to be relevant and provided a clue to the sustainability approach. This study provides insights into the design of sustainable homes, which must go beyond the concept of increasing the capacity of renewable energy. In addition to this, this study examines the interplay between the variance estimation of the problematic levels and the perception of energy loss to introduce a novel realistic model of cost–benefit incentives. This optimisation goal contrasted with uncertainties that remain as to what constitutes the demand impact and individual house effects in diverse clustering patterns in a specific grid system. While ongoing effort is still needed to look for strategic solutions for this class of complex problems, this research shows significant contextual opportunities to manage the complexity of the problem according to the nature of the case, representing dense and significant changes in the situational complexity.
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Tajdaran, Sadjad, Christopher Kendrick, Edward Hopkins, and Fabrizio Bonatesta. "Geometrical optimisation of Transpired Solar Collectors using design of experiments and computational fluid dynamics." Solar Energy 197 (February 2020): 527–37. http://dx.doi.org/10.1016/j.solener.2020.01.018.
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Pastellides, Stavros, Stiaan Gerber, Rong-Jie Wang, and Maarten Kamper. "Evaluation of Drive Cycle-Based Traction Motor Design Strategies Using Gradient Optimisation." Energies 15, no. 3 (February 1, 2022): 1095. http://dx.doi.org/10.3390/en15031095.
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In this paper, two design optimisation methods are evaluated using gradient-based optimisation for electric vehicle traction applications. A driving cycle-based approach is used to evaluate specific operational points for the design optimisation procedure. To determine the operational points, an energy centre of gravity (ECG) approach is used. Both optimisation methods are described, namely the point based method and the flux mapping method, with a focus on the flux mapping procedure. Within the flux mapping approach, an inner optimisation loop is defined in order to maintain the stability of gradient calculation for the gradient-based optimisation. An emphasis is placed on the importance of how the optimisation problem is defined, in terms of the objective function and constraints, and how it affects a gradient based optimisation. Based on a design case study conducted in the paper, it is found that the point-based strategy realised motor designs with a slightly lower overall cost (5.66% lower than that of the flux mapping strategy with 8 ECG points), whereas the flux mapping strategy found motor designs with a lower input energy (1.48% lower than that of the point-based strategy with 8 ECG points). This may be attributed to the difference in the definition and interpretation of constraints between these two methods. It is also shown that including more operational points from the driving cycle in the design optimisation leads to designs with reduced total input energy and thus better drive-cycle energy efficiency. This paper further illustrates the significant computational advantages of a gradient-based optimisation over a global optimisation method as it can be completed within a fraction of the time while still finding a global optimum, as long as the problem definition is correctly determined.
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Cekic, Yalcin. "Bearing fault detection by four-band wavelet packet decomposition." Thermal Science 23, Suppl. 1 (2019): 91–98. http://dx.doi.org/10.2298/tsci180927333c.
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Bearing problems are by far the biggest cause of induction motor failures in the industry. Since induction machines are used heavily by the industry, their unexpected failure may disturb the production process. Motor condition monitoring is employed widely to avoid such unexpected failures. The data that can be obtained from induction machines are non-stationary by nature since the loading may vary during their operation. Wavelet packet decomposition seems to better handle non-stationary nature of induction machines, the use of this method in monitoring applications is limited, since the computational complexity is higher than other methods. In this work four-band wavelet packet decomposition of motor vibration data is proposed to reduce the computational complexity without compromising accuracy. The proposed method is very suitable for parallel computational processing by its nature, and as a result it is predicted that the calculation time will be shortened further if field-progammable gate array is used in design.
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De Mel, Ishanki, Oleksiy V. Klymenko, and Michael Short. "Balancing accuracy and complexity in optimisation models of distributed energy systems and microgrids with optimal power flow: A review." Sustainable Energy Technologies and Assessments 52 (August 2022): 102066. http://dx.doi.org/10.1016/j.seta.2022.102066.
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Neshat, Mehdi, Nataliia Y. Sergiienko, Erfan Amini, Meysam Majidi Nezhad, Davide Astiaso Garcia, Bradley Alexander, and Markus Wagner. "A New Bi-Level Optimisation Framework for Optimising a Multi-Mode Wave Energy Converter Design: A Case Study for the Marettimo Island, Mediterranean Sea." Energies 13, no. 20 (October 20, 2020): 5498. http://dx.doi.org/10.3390/en13205498.
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To advance commercialisation of ocean wave energy and for the technology to become competitive with other sources of renewable energy, the cost of wave energy harvesting should be significantly reduced. The Mediterranean Sea is a region with a relatively low wave energy potential, but due to the absence of extreme waves, can be considered at the initial stage of the prototype development as a proof of concept. In this study, we focus on the optimisation of a multi-mode wave energy converter inspired by the CETO system to be tested in the west of Sicily, Italy. We develop a computationally efficient spectral-domain model that fully captures the nonlinear dynamics of a wave energy converter (WEC). We consider two different objective functions for the purpose of optimising a WEC: (1) maximise the annual average power output (with no concern for WEC cost), and (2) minimise the levelised cost of energy (LCoE). We develop a new bi-level optimisation framework to simultaneously optimise the WEC geometry, tether angles and power take-off (PTO) parameters. In the upper-level of this bi-level process, all WEC parameters are optimised using a state-of-the-art self-adaptive differential evolution method as a global optimisation technique. At the lower-level, we apply a local downhill search method to optimise the geometry and tether angles settings in two independent steps. We evaluate and compare the performance of the new bi-level optimisation framework with seven well-known evolutionary and swarm optimisation methods using the same computational budget. The simulation results demonstrate that the bi-level method converges faster than other methods to a better configuration in terms of both absorbed power and the levelised cost of energy. The optimisation results confirm that if we focus on minimising the produced energy cost at the given location, the best-found WEC dimension is that of a small WEC with a radius of 5 m and height of 2 m.
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Iacobucci, Riccardo, Raffaele Bruno, and Jan-Dirk Schmöcker. "An Integrated Optimisation-Simulation Framework for Scalable Smart Charging and Relocation of Shared Autonomous Electric Vehicles." Energies 14, no. 12 (June 18, 2021): 3633. http://dx.doi.org/10.3390/en14123633.
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Ride-hailing with autonomous electric vehicles and shared autonomous electric vehicle (SAEV) systems are expected to become widely used within this decade. These electrified vehicles can be key enablers of the shift to intermittent renewable energy by providing electricity storage to the grid and offering demand flexibility. In order to accomplish this goal, practical smart charging strategies for fleets of SAEVs must be developed. In this work, we present a scalable, flexible, and practical approach to optimise the operation of SAEVs including smart charging based on dynamic electricity prices. Our approach integrates independent optimisation modules with a simulation model to overcome the complexity and scalability limitations of previous works. We tested our solution on real transport and electricity data over four weeks using a publicly available dataset of taxi trips from New York City. Our approach can significantly lower charging costs and carbon emissions when compared to an uncoordinated charging strategy, and can lead to beneficial synergies for fleet operators, passengers, and the power grid.
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Bożek, Andrzej. "Energy Cost-Efficient Task Positioning in Manufacturing Systems." Energies 13, no. 19 (September 24, 2020): 5034. http://dx.doi.org/10.3390/en13195034.
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A problem to determine a production schedule which minimises the cost of energy used for manufacturing is studied. The scenario assumes that each production task has assigned constant power consumption, price of power from conventional electrical grid system is defined by time-of-use tariffs, and a component of free of charge renewable energy is available for the manufacturing system. The objective is to find the most cost-efficient production plan, subject to constraints involving predefined precedence relationships between the tasks and a bounded makespan. Two independent optimisation approaches have been developed, based on significantly different paradigms, namely mixed-integer linear programming and tabu search metaheuristic. Both of them have been verified and compared in extensive computational experiments. The tabu search-based approach has turned out to be generally more efficient in the sense of the obtained objective function values, but advantages of the use of linear programming have also been identified. The results confirm that it is possible to develop efficient computational methods to optimise energy cost under circumstances typical of manufacturing companies. The set of numerous benchmark instances and their solutions have been archived and it can be reused in further research.
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Peric, Bojan, Aleksandar Simonovic, and Milos Vorkapic. "Comparative analysis of numerical computational techniques for determination of the wind turbine aerodynamic performances." Thermal Science, no. 00 (2020): 175. http://dx.doi.org/10.2298/tsci200216175p.
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The purpose of this paper is to explore and define an adequate numerical setting for the computation of aerodynamic performances of wind turbines of various shapes and sizes, which offers the possibility of choosing a suitable approach of minimal complexity for the future research. Here, mechanical power, thrust, power coefficient, thrust coefficient, pressure coefficient, pressure distribution along the blade, relative velocity contoure at different wind speeds and streamlines were considered by two different methods: the blade element momentum (BEM) and computational fluid dynamics (CFD), within which three different turbulence models were analyzed. The estimation of the mentioned aerodynamic performances was carried out on two different wind turbine blades. The obtained solutions were compared with the experimental and nominal (up-scaled) values, available in the literature. Although the flow was considered as steady, a satisfactory correlation between numerical and experimental results was achieved. The comparison between results also showed, the significance of selection, regarding the complexity and geometry of the analyzed wind turbine blade, the most appropriate numerical approach for computation of aerodynamic performances.
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Goddard, C. D., Y. B. Yang, J. Goodfellow, V. N. Sharifi, J. Swithenbank, J. Chartier, D. Mouquet, R. Kirkman, D. Barlow, and S. Moseley. "Optimisation study of a large waste-to-energy plant using computational modelling and experimental measurements." Journal of the Energy Institute 78, no. 3 (August 1, 2005): 106–16. http://dx.doi.org/10.1179/014426005x50850.
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Yang, Xiao-Jun, Zhi-Zhen Zhang, Tenreiro Machado, and Dumitru Baleanu. "On local fractional operators View of computational complexity: Diffusion and relaxation defined on cantor sets." Thermal Science 20, suppl. 3 (2016): 755–67. http://dx.doi.org/10.2298/tsci16s3755y.
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This paper treats the description of non-differentiable dynamics occurring in complex systems governed by local fractional partial differential equations. The exact solutions of diffusion and relaxation equations with Mittag-Leffler and exponential decay defined on Cantor sets are calculated. Comparative results with other versions of the local fractional derivatives are discussed.
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Zuo, Jing, Sui Peng, Yan Yang, Zuohong Li, Zhengmin Zuo, Hao Yu, and Yong Lin. "A Modified Multiparameter Linear Programming Method for Efficient Power System Reliability Assessment." Processes 10, no. 11 (October 25, 2022): 2188. http://dx.doi.org/10.3390/pr10112188.
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Power systems face adequacy risks because of the high integration of renewable energy. It is urgent to develop efficient methods for power system operational reliability assessment. Conventional power system reliability assessment methods cannot achieve real-time assessment of system risk because of the high computational complexity and long calculation time. The high computational complexity is mainly caused by a large number of optimal power flow (OPF) calculations. To reduce the computational complexity, this paper transfers the optimal power flow model as a multiparameter linear programming model. Then, the optimal power flow can be obtained by linear calculations. Furthermore, this paper proposes a state reduction method considering the importance index of transmission lines for further improving the calculation efficiency. Case studies are carried out on IEEE standard systems and a provincial power grid in China. Compared with the conventional reliability assessment method, the reliability assessment efficiency of the proposed method increases by 10–40 times, and the assessment error is less than 1%.
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Kannengießer, Timo, Maximilian Hoffmann, Leander Kotzur, Peter Stenzel, Fabian Schuetz, Klaus Peters, Stefan Nykamp, Detlef Stolten, and Martin Robinius. "Reducing Computational Load for Mixed Integer Linear Programming: An Example for a District and an Island Energy System." Energies 12, no. 14 (July 22, 2019): 2825. http://dx.doi.org/10.3390/en12142825.
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The complexity of Mixed-Integer Linear Programs (MILPs) increases with the number of nodes in energy system models. An increasing complexity constitutes a high computational load that can limit the scale of the energy system model. Hence, methods are sought to reduce this complexity. In this paper, we present a new 2-Level Approach to MILP energy system models that determines the system design through a combination of continuous and discrete decisions. On the first level, data reduction methods are used to determine the discrete design decisions in a simplified solution space. Those decisions are then fixed, and on the second level the full dataset is used to ex-tract the exact scaling of the chosen technologies. The performance of the new 2-Level Approach is evaluated for a case study of an urban energy system with six buildings and an island system based on a high share of renewable energy technologies. The results of the studies show a high accuracy with respect to the total annual costs, chosen system structure, installed capacities and peak load with the 2-Level Approach compared to the results of a single level optimization. The computational load is thereby reduced by more than one order of magnitude.
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Zhang, Can, Stephan C. Kramer, Athanasios Angeloudis, Jisheng Zhang, Xiangfeng Lin, and Matthew D. Piggott. "Improving tidal turbine array performance through the optimisation of layout and yaw angles." International Marine Energy Journal 5, no. 3 (December 19, 2022): 273–80. http://dx.doi.org/10.36688/imej.5.273-280.
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Tidal stream currents change in magnitude and direction during flood and ebb tides. Setting the most appropriate yaw angles for a tidal turbine is not only important to account for the performance of a single turbine, but can also be significant for the interactions between the turbines within an array. In this paper, a partial differentiation equation (PDE) constrained optimisation approach is established based on the Thetis coastal ocean modelling framework. The PDE constraint takes the form here of the two-dimensional, depth-averaged shallow water equations which are used to simulate tidal elevations and currents in the presence of tidal stream turbine arrays. The Sequential Least Squares Programming (SLSQP) algorithm is applied with a gradient obtained via the adjoint method in order to perform array design optimisation. An idealised rectangular channel test case is studied to demonstrate this optimisation framework. Located in the centre of the computational domain, arrays comprised of 12 turbines are tested in aligned and staggered layouts. The setups are initially optimised based on their yaw angles alone. In turn, turbine coordinates and yaw angles are also optimized simultaneously. Results indicate that for an aligned turbine array case under steady state conditions, the energy output can be increased by approximately 80\% when considering yaw angle optimisation alone. For the staggered turbine array, the increase is approximately 30\%. The yaw optimised staggered array is able to outperform the yaw optimised aligned array by approximately 8\%. If both layout and the yaw angles of the turbines are considered within the optimisation then the increase is more significant compared with optimising yaw angle alone.
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Bliek, Laurens. "A Survey on Sustainable Surrogate-Based Optimisation." Sustainability 14, no. 7 (March 24, 2022): 3867. http://dx.doi.org/10.3390/su14073867.
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Surrogate-based optimisation (SBO) algorithms are a powerful technique that combine machine learning and optimisation to solve expensive optimisation problems. This type of problem appears when dealing with computationally expensive simulators or algorithms. By approximating the expensive part of the optimisation problem with a surrogate, the number of expensive function evaluations can be reduced. This paper defines sustainable SBO, which consists of three aspects: applying SBO to a sustainable application, reducing the number of expensive function evaluations, and considering the computational effort of the machine learning and optimisation parts of SBO. The paper reviews sustainable applications that have successfully applied SBO over the past years, and analyses the used framework, type of surrogate used, sustainable SBO aspects, and open questions. This leads to recommendations for researchers working on sustainability-related applications who want to apply SBO, as well as recommendations for SBO researchers. It is argued that transparency of the computation resources used in the SBO framework, as well as developing SBO techniques that can deal with a large number of variables and objectives, can lead to more sustainable SBO.
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S.W. Chai, M.R. Kamaluddin, and Mohd Fadzil Faisae Ab. Rashid. "Optimisation of vehicle routing problem with time windows using Harris Hawks optimiser." Journal of Mechanical Engineering and Sciences 16, no. 3 (September 28, 2022): 9056–65. http://dx.doi.org/10.15282/jmes.16.3.2022.08.0717.
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Vehicle routing problem is one of the combinatorial optimisation problems that have gained attraction for studies because of its complexity and significant impact to service providers and passengers. Vehicle routing problem with time windows (VRPTW) is a variant where vehicles need to visit the predetermined stop points within the given time frame. This problem has been widely studied and optimised using different methods. Since the performance of algorithms in different problems is dissimilar, the study to optimise the VRPTW is ongoing. This paper presents a VRPTW study for a public transportation network in Kuantan and Pekan districts, located in East Pahang, Malaysia. There were 52 stop points to be visited within two hours. The main objective of the study is to minimise the number of vehicles to be assigned for the routing problem subjected to the given time windows. The problem was optimised using a new algorithm known as Harris Hawks Optimiser (HHO). To the best of authors’ knowledge, this is the first attempt to build HHO algorithm for VRPTW problem. Computational experiment indicated that the HHO came up with the best average fitness compared with other comparison algorithms in this study including Artificial Bee Colony (ABC), Particle Swarm Optimisation (PSO), Moth Flame Optimiser (MFO), and Whale Optimisation Algorithm (WOA). The optimisation results also indicated that all the stop points can be visited within the given time frames by using three vehicles.
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Gebauer, Marek, Tomáš Blejchař, Tomáš Brzobohatý, Tomáš Karásek, and Miroslav Nevřela. "Determination of Aerodynamic Losses of Electric Motors." Symmetry 14, no. 11 (November 13, 2022): 2399. http://dx.doi.org/10.3390/sym14112399.
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The energy efficiency of machines is nowadays an intensively studied problem. The efficiency of the induction motor is dominantly influenced by the rotor’s and stator winding’s temperature. The main goal of the research presented in this paper is to develop a methodology based on Computational Fluid Dynamics (CFD) analysis of internal and external aerodynamics, which is necessary for the optimisation of cooling of the induction motors. In this paper, the theoretical, as well as the numerical study of the internal and external aerodynamics of the induction motor, is described and verified by the experimental measurements. In the CFD-based numerical study, the Reynolds-averaged Navier–Stokes (RANS) turbulence modelling approach was applied to the flow field simulations inside and outside the induction motor. The complexity of the solved problem is increased not only by the geometric asymmetry but also by the flow’s asymmetric character caused by the fan’s rotation to cool the motor casing. This increases demand, especially on computational resources, as it is impossible to create a simplified numerical model incorporating symmetry. The volume flow of the cooling air and velocity between ribs was measured for the experimental study. Comparing the results of the Computational Fluid Dynamics (CFD) simulations and data obtained from the experimental measurement, we concluded that the results of CFD simulations are in good relationship with the results of experimental measurement and analytical approximations. An experimentally validated CFD model of the induction motor, the so-called digital twin, will be in the future used for virtual optimisation of the new designs concerning minimising losses and maximising efficiency, respectively.
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Colak, Ilhami, Mehmet Demirtas, and Ersan Kabalci. "Design, optimisation and application of a resonant DC link inverter for solar energy systems." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 33, no. 5 (August 26, 2014): 1761–76. http://dx.doi.org/10.1108/compel-06-2013-0200.
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Purpose – The purpose of this paper is to examine diminish switching losses in a solar energy conversion system in order to utilise the full efficiency of a solar panel. Design/methodology/approach – In this paper, a boost converter and a resonant DC link (RDCL) inverter are controlled by a microcontroller. The maximum power point tracker (MPPT) algorithm implemented for boost converter supplies to track maximum power point of solar panel. The Class D full-bridge resonant inverter (RI) that is considered to be supplied by boost converter is modeled and zero voltage switching operation is performed by controlling the inverter with sinusoidal pulse width modulation (SPWM) control scheme. The control algorithm is managed with a feedback detecting the current of the boost converter and the zero voltage levels of capacitor voltage in the resonant circuit. Findings – There are several control techniques have been proposed to reduce switching losses and harmonic contents in conventional or RDCL inverters. Solar panels are used in low power applications among other renewable energy sources. By considering that the efficiency parameter of an actual solar panels is around 14∼17 per cent, the switching losses occurred in energy conversion systems causes the efficiency are reduced. Originality/value – The proposed approach has been decreased the switching power losses owing to resonant DC link inverter while the developed MPPT algorithm provides to generate maximum power. This paper introduces a novel soft switching technique in solar energy applications in order to maximise the possible efficiency.
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Abualigah, Laith, Raed Abu Zitar, Khaled H. Almotairi, Ahmad MohdAziz Hussein, Mohamed Abd Elaziz, Mohammad Reza Nikoo, and Amir H. Gandomi. "Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques." Energies 15, no. 2 (January 14, 2022): 578. http://dx.doi.org/10.3390/en15020578.
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Nowadays, learning-based modeling methods are utilized to build a precise forecast model for renewable power sources. Computational Intelligence (CI) techniques have been recognized as effective methods in generating and optimizing renewable tools. The complexity of this variety of energy depends on its coverage of large sizes of data and parameters, which have to be investigated thoroughly. This paper covered the most resent and important researchers in the domain of renewable problems using the learning-based methods. Various types of Deep Learning (DL) and Machine Learning (ML) algorithms employed in Solar and Wind energy supplies are given. The performance of the given methods in the literature is assessed by a new taxonomy. This paper focus on conducting comprehensive state-of-the-art methods heading to performance evaluation of the given techniques and discusses vital difficulties and possibilities for extensive research. Based on the results, variations in efficiency, robustness, accuracy values, and generalization capability are the most obvious difficulties for using the learning techniques. In the case of the big dataset, the effectiveness of the learning techniques is significantly better than the other computational methods. However, applying and producing hybrid learning techniques with other optimization methods to develop and optimize the construction of the techniques is optionally indicated. In all cases, hybrid learning methods have better achievement than a single method due to the fact that hybrid methods gain the benefit of two or more techniques for providing an accurate forecast. Therefore, it is suggested to utilize hybrid learning techniques in the future to deal with energy generation problems.
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Mohseni, Soheil, and Alan C. Brent. "A Metaheuristic-Based Micro-Grid Sizing Model with Integrated Arbitrage-Aware Multi-Day Battery Dispatching." Sustainability 14, no. 19 (October 10, 2022): 12941. http://dx.doi.org/10.3390/su141912941.
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Rule-based micro-grid dispatch strategies have received significant attention over the last two decades. However, a recent body of literature has conclusively shown the benefits of operational scheduling optimisation while optimally sizing micro-grids. This is commonly referred to as micro-grid design and dispatch co-optimisation (MGDCO). However, as far as can be ascertained, all the existing MGDCO models in the literature consider a 24-h-resolved day-ahead timeframe for the associated optimal energy scheduling processes. That is, intelligent, look-ahead energy dispatch strategies over multi-day timeframes are generally absent from the wider relevant literature. In response, this paper introduces a novel MGDCO modelling framework that integrates an arbitrage-aware linear programming-based multi-day energy dispatch strategy into the standard metaheuristic-based micro-grid investment planning processes. Importantly, the model effectively extends the mainstream energy scheduling optimisation timeframe in the micro-grid investment planning problems by producing optimal dispatch solutions that are aware of scenarios over three days. Based on the numeric simulation results obtained from a test-case micro-grid, the effectiveness of the proposed optimisation-based dispatch strategy in the micro-grid sizing processes is verified, while retaining the computational tractability. Specifically, comparing the proposed investment planning framework, which uses the formulated 72-h dispatch strategies, with the business-as-usual MGDCO methods has demonstrated that it can reduce the micro-grid’s whole-life cost by up to 8%. Much of the outperformance of the proposed method can be attributed to the effective use of the behind-the-meter Li-ion battery storage, which improves the overall system flexibility.
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Garcia-Teruel, Anna, Yvonne Scholz, Wolfgang Weimer-Jehle, Sigrid Prehofer, Karl-Kiên Cao, and Frieder Borggrefe. "Teaching Power-Sector Models Social and Political Awareness." Energies 15, no. 9 (April 29, 2022): 3275. http://dx.doi.org/10.3390/en15093275.
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Energy-system scenarios are widely used to relate the developments of the energy supply and the resulting carbon-emission pathways to political measures. To enable scenario analyses that adequately capture the variability of renewable-energy resources, a specialised type of power-sector model (PSM) has been developed since the beginning of this century, which uses input data with hourly resolution at the national or subnational levels. These models focus on techno-economic-system optimisation, which needs to be complemented with expert socioeconomic knowledge in order to prevent solutions that may be socially inacceptable or that oppose political goals. A way to integrate such knowledge into energy-system analysis is to use information from framework scenarios with a suitable geographical and technological focus. We propose a novel methodology to link framework scenarios to a PSM by applying complexity-management methods that enable a flexible choice of base scenarios that are tailored to suit different research questions. We explain the methodology, and we illustrate it in a case study that analyses the influence of the socioeconomic development on the European power-system transition until 2050 by linking the power-sector model, REMix (renewable-energy mix), to regional framework scenarios. The suggested approach proves suitable for this purpose, and it enables a clearer link between the impact of political measures and the power-system development.
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Iung, Anderson Mitterhofer, Fernando Luiz Cyrino Oliveira, and André Luís Marques Marcato. "A Review on Modeling Variable Renewable Energy: Complementarity and Spatial–Temporal Dependence." Energies 16, no. 3 (January 17, 2023): 1013. http://dx.doi.org/10.3390/en16031013.
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The generation from renewable sources has increased significantly worldwide, mainly driven by the need to reduce the global emissions of greenhouse gases, decelerate climate changes, and meet the environmental, social, and governance agenda (ESG). The main characteristics of variable renewable energy (VRE) are the stochastic nature, its seasonal aspects, spatial and time correlations, and the high variability in a short period, increasing the complexity of modeling, planning, operating, and the commercial aspects of the power systems. The research on the complementarity and dependence aspects of VREs is gaining importance, given the development of hybrid generation systems and an array of VREs generators spread over a large region, which could be compounded by different renewable sources, such as hydro, solar, and wind. This review is based on a systematic literature review, providing a comprehensive overview of studies that investigated applied methodologies and methods to address dependence and complementarity. It is a recent field of interest, as 60% of the articles were published in the last five years, a set of methods that have been employed to address this issue, from conventional statistics methods to artificial intelligence. The copulas technique appears as an important approach to modeling renewable energy interdependence. There is a gap in articles comparing the accuracy of the methods employed and the computational efforts.
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Cao, Yongsheng, Guanglin Zhang, Demin Li, Lin Wang, and Zongpeng Li. "Online Energy Management and Heterogeneous Task Scheduling for Smart Communities with Residential Cogeneration and Renewable Energy." Energies 11, no. 8 (August 13, 2018): 2104. http://dx.doi.org/10.3390/en11082104.
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With the development of renewable energy technology and communication technology in recent years, many residents now utilize renewable energy devices in their residences with energy storage systems. We have full confidence in the promising prospects of sharing idle energy with others in a community. However, it is a great challenge to share residents’ energy with others in a community to minimize the total cost of all residents. In this paper, we study the problem of energy management and task scheduling for a community with renewable energy and residential cogeneration, such as residential combined heat and power system (resCHP) to pay the least electricity bill. We take elastic and inelastic load demands into account which are delay intolerant and delay tolerant tasks in the community. The minimum cost problem of a non-cooperative community is extracted into a random non-convex optimization problem with some physical constraints. Our objective is to minimize the time-average cost for each resident in the community, including the cost of the external grid and natural gas. The Lyapunov optimization theory and a primal-dual gradient method are adopted to tackle this problem, which needs no future data and has low computational complexity. Furthermore, we design a cooperative renewable energy sharing algorithm based on State-action-reward-state-action (Sarsa) Algorithm, in the condition that each residence in the community is able to communicate with its neighbors by a central controller. Finally, extensive simulations are presented to validate the proposed algorithms by using practical data.
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Wu, Xie, Chen, and Tang. "Power Control and Link Selection for Wireless Relay Networks with Hybrid Energy Sources." Applied Sciences 9, no. 13 (July 6, 2019): 2744. http://dx.doi.org/10.3390/app9132744.
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The Hybrid energy supply (HES) wireless relay system is a new green network technology, where the source node is powered by the grid and relay is powered by harvested renewable energy. However, the network’s performance may degrade due to the intermittent nature of renewable energy. In this paper, our purpose is to minimize grid energy consumption and maximize throughput. However, improving the throughput requires increasing the transmission power of the source node, which will lead to a higher grid energy consumption. Linear weighted summation method is used to turn the two conflicting objectives into a single objective. Link assignment and a power control strategy are adopted to maximize the total reward of the network. The problem is formulated as a discrete Markov decision model. In addition, a backwards induction method based on state deletion is proposed to reduce the computational complexity. Simulation results show that the proposed algorithm can effectively alleviate performance degradation caused by the lack of renewable energy, and present the trade-off between energy consumption and throughput.
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Polo-Garzon, Felipe, and Zili Wu. "Acid–base catalysis over perovskites: a review." Journal of Materials Chemistry A 6, no. 7 (2018): 2877–94. http://dx.doi.org/10.1039/c7ta10591f.
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Kaur, Paramjeet, Krishna Teerth Chaturvedi, and Mohan Lal Kolhe. "Combined Heat and Power Economic Dispatching within Energy Network using Hybrid Metaheuristic Technique." Energies 16, no. 3 (January 22, 2023): 1221. http://dx.doi.org/10.3390/en16031221.
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Combined heat and power (CHP) plants have opportunities to work as distributed power generation for providing heat and power demand. Furthermore, CHP plants contribute effectively to overcoming the intermittence of renewable energy sources as well as load dynamics. CHP plants need optimal solution(s) for providing electrical and heat energy demand simultaneously within the smart network environment. CHP or cogeneration plant operations need appropriate techno-economic dispatching of combined heat and power with minimising produced energy cost. The interrelationship between heat and power development in a CHP unit, the valve point loading effect, and forbidden working regions of a thermal power plant make the CHP economic dispatch’s (CHPED) objective function discontinuous. It adds complexity in the CHPED optimisation process. The key objective of the CHPED is operating cost minimisation while meeting the desired power and heat demand. To optimise the dispatch operation, three different algorithms, like Jaya algorithm, Rao 3 algorithm, and hybrid CHPED algorithm (based on first two) are adopted containing different equality and inequality restrictions of generating units. The hybrid CHPED algorithm is developed by the authors, and it can handle all of the constraints. The success of the suggested algorithms is assessed on two test systems; 5-units and 24-unit power plants.
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Bañales, Santiago, Raquel Dormido, and Natividad Duro. "Smart Meters Time Series Clustering for Demand Response Applications in the Context of High Penetration of Renewable Energy Resources." Energies 14, no. 12 (June 11, 2021): 3458. http://dx.doi.org/10.3390/en14123458.
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The variability in generation introduced in the electrical system by an increasing share of renewable technologies must be addressed by balancing mechanisms, demand response being a prominent one. In parallel, the massive introduction of smart meters allows for the use of high frequency energy use time series data to segment electricity customers according to their demand response potential. This paper proposes a smart meter time series clustering methodology based on a two-stage k-medoids clustering of normalized load-shape time series organized around the day divided into 48 time points. Time complexity is drastically reduced by first applying the k-medoids on each customer separately, and second on the total set of customer representatives. Further time complexity reduction is achieved using time series representation with low computational needs. Customer segmentation is undertaken with only four easy-to-interpret features: average energy use, energy–temperature correlation, entropy of the load-shape representative vector, and distance to wind generation patterns. This last feature is computed using the dynamic time warping distance between load and expected wind generation shape representative medoids. The two-stage clustering proves to be computationally effective, scalable and performant according to both internal validity metrics, based on average silhouette, and external validation, based on the ground truth embedded in customer surveys.
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Albreem, Mahmoud A., Arun Kumar, Mohammed H. Alsharif, Imran Khan, and Bong Jun Choi. "Comparative Analysis of Data Detection Techniques for 5G Massive MIMO Systems." Sustainability 12, no. 21 (November 9, 2020): 9281. http://dx.doi.org/10.3390/su12219281.
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Massive multiple-input multiple-output (MIMO) is a backbone technology in the fifth-generation (5G) and beyond 5G (B5G) networks. It enhances performance gain, energy efficiency, and spectral efficiency. Unfortunately, a massive number of antennas need sophisticated processing to detect the transmitted signal. Although a detector based on the maximum likelihood (ML) is optimal, it incurs a high computational complexity, and hence, it is not hardware-friendly. In addition, the conventional linear detectors, such as the minimum mean square error (MMSE), include a matrix inversion, which causes a high computational complexity. As an alternative solution, approximate message passing (AMP) algorithm is proposed for data detection in massive MIMO uplink (UL) detectors. Although the AMP algorithm is converging extremely fast, the convergence is not guaranteed. A good initialization influences the convergence rate and affects the performance substantially together and the complexity. In this paper, we exploit several free-matrix-inversion methods, namely, the successive over-relaxation (SOR), the Gauss–Seidel (GS), and the Jacobi (JA), to initialize the AMP-based massive MIMO UL detector. In other words, hybrid detectors are proposed based on AMP, JA, SOR, and GS with an efficient initialization. Numerical results show that proposed detectors achieve a significant performance enhancement and a large reduction in the computational complexity.
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Eyre, Nick. "Barriers to Energy Efficiency: More Than Just Market Failure." Energy & Environment 8, no. 1 (March 1997): 25–43. http://dx.doi.org/10.1177/0958305x9700800103.
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The importance to environmental policy of improving energy efficiency is now widely agreed. It is also well established that levels of energy efficiency are below the optimum for economic efficiency, i.e. there are market barriers to energy efficiency. Neo-c1assical economic theory provides a taxonomy of the barriers in terms of market failure and can evaluate short term policy options to address them. However, this paradigm does not explain the underlying causes or why all the market failures act in the direction of lower energy efficiency. Economic analysis alone cannot identify long term, sustainable approaches to removing the barriers; input is needed from other disciplines. A review of the multi-disciplinary literature identifies some common elements in the nature of the barriers: a dichotomy between producers and consumers, centralisation in energy supply and planning, a commodity view of energy, and complexity of energy efficiency markets. It is concluded that these are fundamental characteristics of energy use in a modem economy. They constitute a meta-barrier - a framework in which the other barriers can be described. Barriers to energy efficiency therefore remain deeply entrenched and, in the short term, optimisation of energy efficiency is unlikely. However, future changes in technology, market structures and institutions may open new opportunities to address the fundamental problems in the longer term.
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Moghimi, Hamid, Majid Siavashi, Mohaddeseh Mousavi Nezhad, and Alberto Guadagnini. "Pore-scale computational analyses of non-Darcy flow through highly porous structures with various degrees of geometrical complexity." Sustainable Energy Technologies and Assessments 52 (August 2022): 102048. http://dx.doi.org/10.1016/j.seta.2022.102048.
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Singh, Abha, Abhishek Sharma, Shailendra Rajput, Amarnath Bose, and Xinghao Hu. "An Investigation on Hybrid Particle Swarm Optimization Algorithms for Parameter Optimization of PV Cells." Electronics 11, no. 6 (March 15, 2022): 909. http://dx.doi.org/10.3390/electronics11060909.
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The demands for renewable energy generation are progressively expanding because of environmental safety concerns. Renewable energy is power generated from sources that are constantly replenished. Solar energy is an important renewable energy source and clean energy initiative. Photovoltaic (PV) cells or modules are employed to harvest solar energy, but the accurate modeling of PV cells is confounded by nonlinearity, the presence of huge obscure model parameters, and the nonattendance of a novel strategy. The efficient modeling of PV cells and accurate parameter estimation is becoming more significant for the scientific community. Metaheuristic algorithms are successfully applied for the parameter valuation of PV systems. Particle swarm optimization (PSO) is a metaheuristic algorithm inspired by animal behavior. PSO and derivative algorithms are efficient methods to tackle different optimization issues. Hybrid PSO algorithms were developed to improve the performance of basic ones. This review presents a comprehensive investigation of hybrid PSO algorithms for the parameter assessment of PV cells. This paper presents how much work is conducted in this field, and how much work can additionally be performed to improve this strategy and create more ideal arrangements of an issue. Algorithms are compared on the basis of the used objective function, type of diode model, irradiation conditions, and types of panels. More importantly, the qualitative analysis of algorithms is performed on the basis of computational time, computational complexity, convergence rate, search technique, merits, and demerits.
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Stepanovic, Ksenija, Jichen Wu, Rob Everhardt, and Mathijs de Weerdt. "Unlocking the Flexibility of District Heating Pipeline Energy Storage with Reinforcement Learning." Energies 15, no. 9 (April 30, 2022): 3290. http://dx.doi.org/10.3390/en15093290.
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The integration of pipeline energy storage in the control of a district heating system can lead to profit gain, for example by adjusting the electricity production of a combined heat and power (CHP) unit to the fluctuating electricity price. The uncertainty from the environment, the computational complexity of an accurate model, and the scarcity of placed sensors in a district heating system make the operational use of pipeline energy storage challenging. A vast majority of previous works determined a control strategy by a decomposition of a mixed-integer nonlinear model and significant simplifications. To mitigate consequential stability, feasibility, and computational complexity challenges, we model CHP economic dispatch as a Markov decision process. We use a reinforcement learning (RL) algorithm to estimate the system’s dynamics through interactions with the simulation environment. The RL approach is compared with a detailed nonlinear mathematical optimizer on day-ahead and real-time electricity markets and two district heating grid models. The proposed method achieves moderate profit impacted by environment stochasticity. The advantages of the RL approach are reflected in three aspects: stability, feasibility, and time scale flexibility. From this, it can be concluded that RL is a promising alternative for real-time control of complex, nonlinear industrial systems.
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Fuksa, Dariusz. "Innovative Method for Calculating the Break-Even for Multi-Assortment Production." Energies 14, no. 14 (July 12, 2021): 4213. http://dx.doi.org/10.3390/en14144213.
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The subject of the article is a new method that I have developed for calculating a multi-asset break-even for multi-assortment production, extended by a percentage threshold and a current sales ratio (which was missing in previously published methods). The percentage threshold provides unambiguous information about the economic health of a company. As a result, it became possible to use it in practice to evaluate the activities of economic entities (mines) and to perform modelling and optimisation of production plans based on different variants of customer demand scenarios. The publication addresses the complexity of the problem of determining the break-even in multi-assortment production. Moreover, it discusses the practical limitations of previous methods and demonstrates the usefulness of the proposed method on the example of hard coal mines.
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Fattah, Salmah, Ismail Ahmedy, Mohd Yamani Idna Idris, and Abdullah Gani. "Hybrid multi-objective node deployment for energy-coverage problem in mobile underwater wireless sensor networks." International Journal of Distributed Sensor Networks 18, no. 9 (September 2022): 155013292211235. http://dx.doi.org/10.1177/15501329221123533.
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Underwater wireless sensor networks have grown considerably in recent years and now contribute substantially to ocean surveillance applications, marine monitoring and target detection. However, the existing deployment solutions struggle to address the deployment of mobile underwater sensor nodes as a stochastic system. The system faces internal and external environment problems that must be addressed for maximum coverage in the deployment region while minimizing energy consumption. In addition, the existing traditional approaches have limitations of improving simultaneously the objective function of network coverage and the dissipated energy in mobility, sensing and redundant coverage. The proposed solution introduced a hybrid adaptive multi-parent crossover genetic algorithm and fuzzy dominance-based decomposition approach by adapting the original non-dominated sorting genetic algorithm II. This study evaluated the solution to substantiate its efficacy, particularly regarding the nodes’ coverage rate, energy consumption and the system’s Pareto optimal metrics and execution time. The results and comparative analysis indicate that the Multi-Objective Optimisation Genetic Algorithm based on Adaptive Multi-Parent Crossover and Fuzzy Dominance (MOGA-AMPazy) is a better solution to the multi-objective sensor node deployment problem, outperforming the non-dominated sorting genetic algorithm II, SPEA2 and MOEA/D algorithms. Moreover, MOGA-AMPazy ensures maximum global convergence and has less computational complexity. Ultimately, the proposed solution enables the decision-maker or mission planners to monitor effectively the region of interest.
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Qi, Xiaoguang, Ying Wang, Mingfeng Yu, Zhengze Wei, Kailin Zhao, Yu Chen, and Xuan Li. "Model-and-data hybrid driven method for power system Operational reliability evaluation with high penetration of renewable energy." Journal of Physics: Conference Series 2378, no. 1 (December 1, 2022): 012007. http://dx.doi.org/10.1088/1742-6596/2378/1/012007.
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Abstract With the rising penetration of renewable energy, the fluctuation of operating conditions such as wind speed and light intensity affects the reliability of the power system. It leads to the repeated reliability evaluation of the power systems and increases the computational complexity. Based on the non-sequential Monte Carlo simulation (NMCS) and back propagation neural network (BPNN), a new model-and-data hybrid driven method for power system reliability evaluation is proposed. Firstly, NMCS is used to calculate the system reliability indices under different operating conditions to obtain the input data for neural network training. Then, the proposed Multiple BPNN (M-BPNN) is used to establish the highly nonlinear mapping relationship between operating conditions and system reliability indices, which can significantly reduce the calculation time of power system operational reliability evaluation with high penetration of renewable energy. The IEEE-RTS 79 system is used to verify the effectiveness and efficiency of the proposed method, and sensitivity analysis is performed.