Relevant bibliographies by topics / Optimisation; renewable energy; computational complexity

Academic literature on the topic 'Optimisation; renewable energy; computational complexity'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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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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Dissertations / Theses on the topic "Optimisation; renewable energy; computational complexity"

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Fujdiak, Radek. "Analýza a optimalizace datové komunikace pro telemetrické systémy v energetice." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-358408.

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Telemetry system, Optimisation, Sensoric networks, Smart Grid, Internet of Things, Sensors, Information security, Cryptography, Cryptography algorithms, Cryptosystem, Confidentiality, Integrity, Authentication, Data freshness, Non-Repudiation.
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Wagner, Markus. "Theory and applications of bio-inspired algorithms." Thesis, 2013. http://hdl.handle.net/2440/82319.

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Evolutionary algorithms, which form a sub-class of bio-inspired algorithms, mimic some fundamental aspects of the neo-Darwinian evolutionary process. They simultaneously search with a population of candidate solutions and associate an objective score as a fitness value for each one. The algorithms then select among the population to favour those solutions that are more fit. The next generation (i.e. a new population) consists of replicates of the fitter solutions that have been genetically mutated and crossed over in a biological metaphor: the decision variables were perturbed such that they inherit characters of their parents, as well as change in random ways. For the past decades, the algorithms’ success has led to strongly practical-oriented interests. Although the theory of them is far behind the knowledge gained from experiments, there are theoretical investigations about some of their properties. This thesis spans theoretical investigations, theory-motivated algorithm engineering, and also the real-world application of evolutionary algorithms. First, we analyse different algorithms that work with solutions of variable length. We show theoretically and experimentally that certain design choices can have drastic impacts on the ability of an algorithm to find optimal solutions. Second, motivated by recent theoretical investigations, we design a framework for solving problems with conflicting objectives. We demonstrate that it can efficiently handle problems with many such objectives, which most existing algorithms have difficulties dealing with. Finally, we consider the problem of maximising the energy yield of wind farms. Our problem-specific algorithm achieves higher quality results than existing approaches, and it allows for an optimisation within minutes or hours instead of days or weeks.
Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2013
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Book chapters on the topic "Optimisation; renewable energy; computational complexity"

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"Computational Intelligence in Energy Generation." In Multi-Objective Optimization of Industrial Power Generation Systems, 1–62. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1710-9.ch001.

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As economies become increasingly complex, so do their associated energy generation systems. Therefore, engineers and decision makers in this sector are spurred to seek out state-of-the-art approaches to deal with this rapid increase in system complexity. An effective strategy to deal with this scenario is to employ computational intelligence (CI) methods. CI supplements the heuristics used by the engineer—enhancing the cumulative analytic capacity to effectively resolve complicated scenarios. CI could be split to two classes: predictive modeling and optimization. In this chapter, past applications of CI in energy generation are discussed. The sectors presented here are renewable energy systems, distributed generation, nuclear power plants, coal power, and gas-fueled plants.
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Deo, Ravinesh C., Sujan Ghimire, Nathan J. Downs, and Nawin Raj. "Optimization of Windspeed Prediction Using an Artificial Neural Network Compared With a Genetic Programming Model." In Advances in Computational Intelligence and Robotics, 328–59. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-4766-2.ch015.

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The precise prediction of windspeed is essential in order to improve and optimize wind power prediction. However, due to the sporadic and inherent complexity of weather parameters, the prediction of windspeed data using different patterns is difficult. Machine learning (ML) is a powerful tool to deal with uncertainty and has been widely discussed and applied in renewable energy forecasting. In this chapter, the authors present and compare an artificial neural network (ANN) and genetic programming (GP) model as a tool to predict windspeed of 15 locations in Queensland, Australia. After performing feature selection using neighborhood component analysis (NCA) from 11 different metrological parameters, seven of the most important predictor variables were chosen for 85 Queensland locations, 60 of which were used for training the model, 10 locations for model validation, and 15 locations for the model testing. For all 15 target sites, the testing performance of ANN was significantly superior to the GP model.
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Ouadi, Abderrahmane, and Abdelkader Zitouni. "Phasor Measurement Improvement Using Digital Filter in a Smart Grid." In Advances in Computer and Electrical Engineering, 100–117. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4027-5.ch005.

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During a transient operation condition of power smart grid, line current may include unwanted components that may cause unnecessary tripping of protection system. The disturbance mainly appears in a form of harmonics and sub-harmonics. In this case of signal waveforms including harmonics, the low pass filter may be used. However, this type of filter does not provide the ability to reject sub-harmonics. This chapter presents the digital filtering design issue based on optimization approach for removing sub-harmonics and hence improving the measurement. The first point of view is to reach an unified accurate phasor measurement algorithm that is immune to nearly all disturbances (sub-harmonics) in power grid including FACT devices and renewable energy sources, simultaneously with required speed of convergence. The second point focuses on reducing the computational requirement and algorithm complexity through designing recursive digital filter with reduced order.
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Conference papers on the topic "Optimisation; renewable energy; computational complexity"

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Igbinovia, Famous O., and Jiri Krupka. "Computational Complexity of Algorithms for Optimization of Multi-Hybrid Renewable Energy Systems." In 2018 International Conference on Power System Technology (POWERCON). IEEE, 2018. http://dx.doi.org/10.1109/powercon.2018.8601619.

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Hajimirza, Shima. "A Dimensionality-Reduced First Order Method for Industrial Optimization: A Case Study in Renewable Energy Technologies." In ASME 2015 9th International Conference on Energy Sustainability collocated with the ASME 2015 Power Conference, the ASME 2015 13th International Conference on Fuel Cell Science, Engineering and Technology, and the ASME 2015 Nuclear Forum. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/es2015-49058.

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We propose and study a dimensionality-reduced first order method for solving complex optimization problems of high-dimensional search space. We demonstrate that the proposed method is very efficient in design problems where the computational bottleneck is mostly due to the time-consuming nature of the forward problem in contrast to the complexity of the function behavior in the search space or other computational overheads. Many industrial problems are of this nature including design problems based on back testing or simulation of an evolutionary equation or a dynamic system in time, frequency or other (hybrid) domains, such as Electromagnetic, Quantum equations, Navier-Stokes PDEs, etc. The premise of efficiency improvement in the proposed framework is a better modelling and utilization of the complexity distribution among the components of an inverse design problem. As a particular case study, we list some of the existing optimization problems related to energy production, distribution and utilization at the industrial level. We briefly overview the different complexity components of these problems at a high level, and make suggestions as what industrial problems can be facilitated through the proposed framework.
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Gaitanis, Aggelos, Francesco Contino, and Ward De Paepe. "Real Time mGT Performance Assessment Tool: Comprehensive Transient Behaviour Prediction With Computationally Effective Techniques." In ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/gt2022-81577.

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Abstract Conventional centralized power generation is increasingly transforming into a more distributed structure due to the transition towards more renewable energy sources. The periodic power production that is created by the renewable production unit generate the need for small-scale heat and power units. One of the promising technologies which can assist a flexible and renewable power grid is micro Gas Turbines (mGTs). Such engines are generally considered to have an electrical power output less than 500kWe and are competent candidates for small-scale Combined Heat and Power (CHP). mGTs, as compensators for the demand fluctuations, are required to work on transient and part-load conditions. Therefore, the connection of such unit to a non-dispatchable energy system creates new research challenges. A complete characterization of their dynamic behaviour through a real-time simulation tool is necessary to establish effective and suitable control systems. This model should predict all the crucial parameters of the engine, such as the surge margin and combustion stability, which assist in the effective performance diagnosis. Moreover, the energy transition requires the conversion of conventional mGTs to more sophisticated high-efficient cycles with the addition of extra components (saturation unit, aftercooler, etc). Consequently, a modular and computationally fast real-time tool offers an asset in the development of future advanced cycles based on the mGT concept. This paper presents the complete development of a numerical in-house tool implemented in the Python open-source programming language for the behaviour prediction of the mGT. The fundamental target of our work is to achieve high fidelity of the simulated dynamic responses by adopting modern coding techniques that decrease the computational time. The model is validated with experimental results from the VUB Turbec T100 test rig and with additional data published in the literature. The component modeling methods are also compared to other techniques to confirm the practicality of the current code. Key benefits of this tool are the low complexity highly efficient component modules. This code reproduces the experimental results well during transient operation as the important cycle parameters present a deviation from the measurements within the range of 1.5%.
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