Multi-criteria decision making monarch butterfly optimization for optimal distributed energy resources mix in distribution networks

The optimal integration of distributed energy resources (DERs) is a multiobjective and complex combinatorial optimization problem that conventional optimization methods cannot solve efficiently. This paper reviews the existing DER integration models, optimization and multi-criteria decision-making approaches. Further to that, a recently developed monarch butterfly optimization method is introduced to solve the problem of DER mix in distribution systems. A new multiobjective DER integration problem is formulated to find the optimal sites, sizes and mix (dispatchable and non-dispatchable) for DERs considering multiple key performance objectives. Besides, a hybrid method that combines the monarch butterfly optimization and the technique for order of preference by similarity to ideal solution (TOPSIS) is proposed to solve the formulated large-scale multi-criteria decision-making problem. Whilst the meta-heuristic optimization method generates non-dominated solutions (creating Pareto-front), the TOPSIS approach selects that with the most promising outcome from a large number of alternatives. The effectiveness of this approach is verified by solving single and multiobjective dispatchable DER integration problems over the benchmark 33-bus distribution system and the performance is compared with the existing optimization methods. The proposed model of DER mix and the optimization technique significantly improve the system performance in terms of average annual energy loss reduction by 78.36%, mean node voltage deviation improvement by 9.59% and average branches loadability limits enhancement by 50%, and minimized the power fluctuation induced by 48.39% renewable penetration. The proposed optimization techniques outperform the existing methods with promising exploration and exploitation abilities to solve engineering optimization problems

Optimization Methods Applied to Power Systems Ⅱ

Electrical power systems are complex networks that include a set of electrical components that allow distributing the electricity generated in the conventional and renewable power plants to distribution systems so it can be received by final consumers (businesses and homes). In practice, power system management requires solving different design, operation, and control problems. Bearing in mind that computers are used to solve these complex optimization problems, this book includes some recent contributions to this field that cover a large variety of problems. More specifically, the book includes contributions about topics such as controllers for the frequency response of microgrids, post-contingency overflow analysis, line overloads after line and generation contingences, power quality disturbances, earthing system touch voltages, security-constrained optimal power flow, voltage regulation planning, intermittent generation in power systems, location of partial discharge source in gas-insulated switchgear, electric vehicle charging stations, optimal power flow with photovoltaic generation, hydroelectric plant location selection, cold-thermal-electric integrated energy systems, high-efficiency resonant devices for microwave power generation, security-constrained unit commitment, and economic dispatch problems

Classification and Performance Study of Task Scheduling Algorithms in Cloud Computing Environment

Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms. Keywords: Cloud computing, Task scheduling, Load balancing, Makespan, Energy-aware, Turnaround time, Response time, Cost of task, QoS, Multi-objective. DOI: 10.7176/IKM/12-5-03 Publication date:September 30th 2022

Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes

Multi-Objective and Multi-Attribute Optimisation for Sustainable Development Decision Aiding

Optimization is considered as a decision-making process for getting the most out of available resources for the best attainable results. Many real-world problems are multi-objective or multi-attribute problems that naturally involve several competing objectives that need to be optimized simultaneously, while respecting some constraints or involving selection among feasible discrete alternatives. In this Reprint of the Special Issue, 19 research papers co-authored by 88 researchers from 14 different countries explore aspects of multi-objective or multi-attribute modeling and optimization in crisp or uncertain environments by suggesting multiple-attribute decision-making (MADM) and multi-objective decision-making (MODM) approaches. The papers elaborate upon the approaches of state-of-the-art case studies in selected areas of applications related to sustainable development decision aiding in engineering and management, including construction, transportation, infrastructure development, production, and organization management

Smart Energy and Intelligent Transportation Systems

With the Internet of Things and various information and communication technologies, a city can manage its assets in a smarter way, constituting the urban development vision of a smart city. This facilitates a more efficient use of physical infrastructure and encourages citizen participation. Smart energy and smart mobility are among the key aspects of the smart city, in which the electric vehicle (EV) is believed to take a key role. EVs are powered by various energy sources or the electricity grid. With proper scheduling, a large fleet of EVs can be charged from charging stations and parking infrastructures. Although the battery capacity of a single EV is small, an aggregation of EVs can perform as a significant power source or load, constituting a vehicle-to-grid (V2G) system. Besides acquiring energy from the grid, in V2G, EVs can also support the grid by providing various demand response and auxiliary services. Thanks to this, we can reduce our reliance on fossil fuels and utilize the renewable energy more effectively. This Special Issue “Smart Energy and Intelligent Transportation Systems” addresses existing knowledge gaps and advances smart energy and mobility. It consists of five peer-reviewed papers that cover a range of subjects and applications related to smart energy and transportation

Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems

The electrical power system is undergoing a revolution enabled by advances in telecommunications, computer hardware and software, measurement, metering systems, IoT, and power electronics. Furthermore, the increasing integration of intermittent renewable energy sources, energy storage devices, and electric vehicles and the drive for energy efficiency have pushed power systems to modernise and adopt new technologies. The resulting smart grid is characterised, in part, by a bi-directional flow of energy and information. The evolution of the power grid, as well as its interconnection with energy storage systems and renewable energy sources, has created new opportunities for optimising not only their techno-economic aspects at the planning stages but also their control and operation. However, new challenges emerge in the optimization of these systems due to their complexity and nonlinear dynamic behaviour as well as the uncertainties involved.This volume is a selection of 20 papers carefully made by the editors from the MDPI topic “Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems”, which was closed in April 2022. The selected papers address the above challenges and exemplify the significant benefits that optimisation and nonlinear control techniques can bring to modern power and energy systems

Development of emergency response systems by intelligent and integrated approaches for marine oil spill accidents

Oil products play a pervasive role in modern society as one of the dominant energy fuel sources. Marine activities related to oil extraction and transportation play a vital role in resource supply. However, marine oil spills occur due to such human activities or harsh environmental factors. The emergency accidents of spills cause negative impacts on the marine environment, human health, and economic loss. The responses to marine oil spills, especially large-scale spills, are relatively challenging and inefficient due to changing environmental conditions, limited response resources, various unknown or uncertain factors and complex resource allocation processes. The development of previous research mainly focused on single process simulation, prediction, or optimization (e.g., oil trajectory, weathering, or cleanup optimization). There is still a lack of research on comprehensive and integrated emergency responses considering multiple types of simulations, types of resource allocations, stages of accident occurrence to response, and criteria for system optimizations. Optimization algorithms are an important part of system optimization and decision-making. Their performance directly affacts the quality of emergency response systems and operations. Thus, how to improve efficiency of emergency response systems becomes urgent and essential for marine oil spill management. The power and potential of integrating intelligent-based modeling of dynamic processes and system optimization have been recognized to better support oil spill responders with more efficient response decisions and planning tools. Meanwhile, response decision-making combined with human factor analysis can help quantitatively evaluate the impacts of multiple causal factors on the overall processes and operational performance after an accident. To address the challenges and gaps, this dissertation research focused on the development and improvement of new emergency response systems and their applications for marine oil spill response in the following aspects: 1) Realization of coupling dynamic simulation and system optimization for marine oil spill responses - The developed Simulation-Based Multi-Agent Particle Swarm Optimization (SA-PSO) modeling investigated the capacity of agent-based modeling on dynamic simulation of spill fate and response, particle swarm optimization on response allocation with minimal time and multi-agent system on information sharing. 2) Investigation of multi-type resource allocation under a complex simulation condition and improvement of optimization performance - The improved emergency response system was achieved by dynamic resource transportation, oil weathering and response simulations and resource allocation optimization. The enhanced particle swarm optimization (ME-PSO) algorithm performed outstanding convergence performance and low computation cost characteristics integrating multi-agent theory (MA) and evolutionary population dynamics (EPD). 3) Analysis and evaluation of influencing factors of multiple stages of spill accidents based on human factors/errors and multi-criteria decision making - The developed human factors analysis and classification system for marine oil spill accidents (HFACS-OS) framework qualitatively evaluated the influence of various factors and errors associated with the multiple operational stages considered for oil spill preparedness and response (e.g., oil spill occurrence, spill monitoring, decision making/contingency planning, and spill response). The framework was further coupled with quantitative data analysis by Fuzzy-based Technique for Order Preference by Similarity to Idea Solution (Fuzzy-TOPSIS) to enhance decision-making during response operations under multiple criteria. 4) Development of a multi-criteria emergency response system with the enhanced optimization algorithm, multi-mode resource transportation and allocation and a more complex and realistic simulation modelling - The developed multi-criteria emergency response system (MC-ERS) system integrated dynamic process simulations and weighted multi-criteria system optimization. Total response time, response cost and environmental impacts were regarded as multiple optimization goals. An improved weighted sum optimization function was developed to unify the scaling and proportion of different goals. A comparative PSO was also developed with various algorithm-improving methods and the best-performing inertia weight function. The proposed emergency response approaches in studies were examined by oil spill case studies related to the North Atlantic Ocean and Canada circumstances to analyze the modelling performance and evaluate their practicality and applicability. The developed optimization algorithms were tested by benchmarked functions, other optimization algorithms, and an oil spill case. The developed emergency response systems and the contained simulation and optimization algorithms showed the strong capability for decision-making and emergency responses by recommending optimal resource management or evaluations of essential factors. This research was expected to provide time-efficient, and cost-saving emergency response management approaches for handling and managing marine oil spills. The research also improved our knowledge of the significance of human factors/errors to oil spill accidents and response operations and provided improved support tools for decision making. The dissertation research helped fill some important gaps in emergency response research and management practice, especially in marine oil spill response, through an innovative integration of dynamic simulation, resource optimization, human factor analysis, and artificial intelligence methods. The research outcomes can also provide methodological support and valuable references for other fields that require timely and effective decisions, system optimizations, process controls, planning and designs under complicated conditions, uncertainties, and interactions

Neuro-fuzzy resource forecast in site suitability assessment for wind and solar energy: a mini review

Abstract:Site suitability problems in renewable energy studies have taken a new turn since the advent of geographical information system (GIS). GIS has been used for site suitability analysis for renewable energy due to its prowess in processing and analyzing attributes with geospatial components. Multi-criteria decision making (MCDM) tools are further used for criteria ranking in the order of influence on the study. Upon location of most appropriate sites, the need for intelligent resource forecast to aid in strategic and operational planning becomes necessary if viability of the investment will be enhanced and resource variability will be better understood. One of such intelligent models is the adaptive neuro-fuzzy inference system (ANFIS) and its variants. This study presents a mini-review of GIS-based MCDM facility location problems in wind and solar resource site suitability analysis and resource forecast using ANFIS-based models. We further present a framework for the integration of the two concepts in wind and solar energy studies. Various MCDM techniques for decision making with their strengths and weaknesses were presented. Country specific studies which apply GIS-based method in site suitability were presented with criteria considered. Similarly, country-specific studies in ANFIS-based resource forecasts for wind and solar energy were also presented. From our findings, there has been no technically valid range of values for spatial criteria and the analytical hierarchical process (AHP) has been commonly used for criteria ranking leaving other techniques less explored. Also, hybrid ANFIS models are more effective compared to standalone ANFIS models in resource forecast, and ANFIS optimized with population-based models has been mostly used. Finally, we present a roadmap for integrating GIS-MCDM site suitability studies with ANFIS-based modeling for improved strategic and operational planning

Optimal sizing of a hybrid PV-WT-battery storage system: Effects of split-ST and combined ST + ORC back-ups in circuit charging and load following

This study explores the opportunities in deploying split Stirling and combined Stirling and organic Rankine cycle (ORC) in circuit charging and load following dispatch modes, respectively as the back-up of a hybrid renewable energy system. The optimal number of system components in each dispatch mode that simultaneously minimises the loss of power supply probability (LPSP), levelised cost of energy (LCOE) and dumped power have been found by implementing an evolutionary algorithm-based multi-objective optimisation approach. Then, a multi-criteria decision making tool is deployed to select the best configuration from the Pareto set. The optimal hybrid system configuration obtained have been compared to the traditional diesel generator back-up system base case, to demonstrate performance improvements with the deployment of the proposed back-ups. The results show deploying Stirling + ORC back-up in load following leads to 60.70% and 33.71% reductions in the LCOE and CO2 emissions, respectively compared to the base case but with slightly higher LPSP. While 61.4%, 33% and 24.47% reductions in the LCOE, CO2 emissions and LPSP have been observed with the deployment of split Stirling in circuit charging mode. Further results from the dynamic simulation highlight the energy cost, reliability, dumped power and battery performance of the optimal system respond to seasonal changes in the test location. Other observed results show the change in the market price and number of the photo-voltaic generator that generates 50% of the total power, strongly affect the performance of the optimal system. The proposed biomass powered Stirling based back-ups are promising alternatives to replace the traditional diesel generator back-ups in improving the green energy system's reliability