Minimum System Cost Research Articles

The economic competitiveness of flexibility options: A model study of the European energy transition

The European energy transition towards renewable energy is increasingly ambitious and climate targets have been reinforced. Options to increase the flexibility of the energy system are expected to become more valuable with higher shares of variable renewable energy. This paper analyzes the economic benefits of key flexibility options in the Northern, Western and Central European energy system under six increasingly ambitious climate targets towards 2030. The value of and competition between flexibility options is investigated by comparing total system costs and profits. The BALMOREL energy system modeling framework is applied, minimizing system costs. The study results show the increasing value of all flexibility measures with increasingly ambitious climate targets. Demand side management has a large impact on system costs when climate targets are low, while sector coupling with the district heat sector and interconnector investments have an increasing impact with more ambitious climate targets. Biomass is essential for achieving deep decarbonization.

Failure Mode Structured Preventive Maintenance Scheduling With Changing Failure Rates in Industry 4.0 Environment

Critical manufacturing systems consist of many different components working together. Each component is important to the operation and performance of the whole system. It is crucial to have a sound maintenance plan to ensure continuous production output. The strategy of current research is to focus on the failure mode modelling of the manufacturing system and divide it according to failure mode in production blocks. The preventive replacement intervals are determined using mean time to failure values of components, with group replacements of components within the production blocks to prevent unexpected functional breakdown failures. With the implementation of Industry 4.0 infrastructure, the mean time to failure values of components are constantly updated. The multi-mode mathematical model developed in this paper is able to adjust the preventive replacement schedule dynamically to suit changing system conditions. The results indicate that preventive replacement of components can achieve consistent minimum system reliability of 90% while minimizing system costs due to maintenance and downtime. Novelty of the approach lies in developing a holistic preventive maintenance schedule using failure mode and effects analysis for a complete system. The approach not only improves maintainability and reliability, lowers the cost of maintenance, but also keeps continuity of production.

Technical and Economic Evaluation for Off-Grid Hybrid Renewable Energy System Using Novel Bonobo Optimizer

In this study, a novel bonobo optimizer (BO) technique is applied to find the optimal design for an off-grid hybrid renewable energy system (HRES) that contains a diesel generator, photovoltaics (PV), a wind turbine (WT), and batteries as a storage system. The proposed HRES aims to electrify a remote region in northern Saudi Arabia based on annualized system cost (ASC) minimization and power system reliability enhancement. To differentiate and evaluate the performance, the BO was compared to four recent metaheuristic algorithms, called big-bang–big-crunch (BBBC), crow search (CS), the genetic algorithm (GA), and the butterfly optimization algorithm (BOA), to find the optimal design for the proposed off-grid HRES in terms of optimal and worst solutions captured, mean, convergence rate, and standard deviation. The obtained results reveal the efficacy of BO compared to the other four metaheuristic algorithms where it achieved the optimal solution of the proposed off-grid HRES with the lowest ASC (USD 149,977.2), quick convergence time, and fewer oscillations, followed by BOA (USD 150,236.4). Both the BBBC and GA algorithms failed to capture the global solution and had high convergence time. In addition, they had high standard deviation, which revealed that their solutions were more dispersed with obvious oscillations. These simulation results proved the supremacy of BO in comparison to the other four metaheuristic algorithms.

Optimal allocation of intelligent parking lots in distribution system: A robust two‐stage optimization model

Electrification of transportation is one of the new ways to deal with environmental issues. In order to reach sustainable development, efficient integration of the plug-in electric vehicles (PEVs) into the distribution network (DN) plays a significant role. Establishing PEV intelligent parking lots (IPLs) is one of the important solutions to develop charging stations. However, one of the problems with IPLs is restrictions created by municipalities and DNs while the common parking lots are not faced with these constraints. The aim of this paper is to optimally allocate IPLs in order to minimize system costs through a two-stage mathematical model. The system cost involves installation cost, cost of exchanging the electrical power with an upstream grid, cost of the electrical power loss, network reliability cost, and emissions cost. In the first stage, the behaviour of IPLs is optimized taking into account the market interactions for enhancing the profit of the IPL owner. In the second stage, the optimal IPL allocation is performed taking various network constraints into consideration. The robust optimization is developed to hedge the DN operator and the IPL owner against risk imposed uncertain variables. The proposed model is a Robust Mix Integer Linear Problem, which is solved using the CPLEX solver in GAMS software. The effectiveness and the efficiency of the proposed model are evaluated on the IEEE 12-bus test network.

An optimal energy management among the electric vehicle charging stations and electricity distribution system using GPC-RERNN approach

This paper proposes a hybrid strategy to manage the energy in electric vehicle charging station (EVCS) and distribution system (DS). The proposed hybrid approach is the joint implementation of Giza Pyramids Construction (GPC) and recalling-enhanced recurrent neural network (RERNN) hence it is named as GPC-RERNN. The main purpose is to give maximum amount of energy as restrictions are irregular and volatile nature of renewable energy sources, stochastic nature of EV, and local meteorological conditions. Likewise, minimum system cost which incorporates land cost, station equipment, operating and preservation cost and also reduces the voltage deviation and power loss on distribution system. At first, RERNN is used to originate the quality-of-service constrained decision form for EVCSs. The utility of DS is increase when scheduling the charging plans of EVCSs. To analyze energy interaction, GPC approach is used. Here, every EVCS is considered as leader and DS is considered as follower. It is used to establish, an optimization issue by equilibrium restrictions. The proposed approach analysis the bidirectional trading of energy, effect of PV uncertainty under EMS operation, cost analysis based on selling energy. Finally the performance of the proposed approach is performed with the MATLAB/Simulink working platform and likened with several existing approaches.

The Selection of Optimal Structure for Stand-Alone Micro-Grid Based on Modeling and Optimization of Distributed Generators

The configuration programming of Distributed Generators (DGs) in a micro-grid (MG) through the achievement of multi-objective is an inevitable and primary issue ahead of micro-grid’s construction. The motivation of this paper is to select the most suitable catalog of MG from DC micro-grid (DC-MG), AC micro-grid (AC-MG), and hybrid MG by means of uncertainties’ models and corresponding DGs’ configurations. The DGs in all catalogs of MG are composed of wind turbine (WT), photovoltaic (PV), biomass generation (BG), and battery energy storage (BES) system. In terms of uncertainties’ models, the proposed mathematical models are combined with multifarious scenarios which are considered the uncertainties of variations in solar irradiance and wind speed, temperature, and load demand. Particularly, this paper also proposes differences in allocations and sizes of all the equipment based on the assumed specific structure for each catalog of MG. Then, the non-dominated sorting genetic algorithm III (NSGA-III) is utilized by MATLAB working platform to compute the multi-objective functions associating with the minimized system cost, the loss of power supply probability (LPSP), and the greenhouse gas (GHG) emissions for each catalog of MG. Finally, the results and comparisons demonstrate that the AC-MG is the optimal catalog for the case study, which has superiorities of economy and reliability. Although the DC-MG has lower GHG emissions, the AC-MG is the optimal choice after the comprehensive comparisons and analyses depended on three objectives.

The combined effect of rework, postponement, multiple shipments, and overtime producing common-component on a multiproduct vendor-client incorporated system

The study examines the combined effect of rework, multiple shipments, postponement, and overtime producing common-component on a multiproduct vendor-client incorporated system. Clients’ product demand trend turns to diversity, quality, and rapid response in the current supply-chain environment. Under such a stiff competitive environment, today’s manufacturers must effectively plan their multi-item fabrication to boost utilization and product quality, minimize total relevant costs, and meet short given order lead time. By considering the commonality of the finished goods, required quality, and completion lead time, this study presents an exact model featuring rework of defects, multiple shipments, postponement, overtime producing the mutual component, and satisfying the market needs. Through the techniques of explicitly modeling, formulating, and system cost minimization, this study simultaneously derives the optimal cycle-time and shipping frequency for the studied problem. A numerical example helps show how our model works for any given parameter values and how the variation in single and multiple factors of the problem affects the crucial system performances (e.g., total uptimes, each relevant cost, utilization, total cost, etc.) A wide variety of today’s industries (e.g., automotive, household goods, etc.) and their related supply chains can utilize our decisional model to reveal in-depth managerial insights for planning their fabrication and shipments.

MITIGATION OF WASTE TRANSPORTATION COSTS IN TAIF PROVINCE, SAUDI ARABIA

Municipal solid waste (MSW) management is a critical issue in the Kingdom of Saudi Arabia (KSA). Management of solid waste in crowded conditions, especially in main cities such as the Al-Taif Province. This paper deals with that case study and focuses on how to solve material handling problems by applying conventional methods and computer ability. One of the costliest operations in the civil sector is transportation for material handling. City mayors considered transportation and materials handling as a subclass of the linear programming technique, thus linear programming problems take into consideration that the main objective is to handle materials from the city centre site to the identified dumping area. There are many methods and tools used to solve the transportation problem (TP) to find out the maximum or minimum in order to reduce cost or maximize profit. The methods related to manual solutions and others used many different computer applications. Manual solutions include the northwest corner method, the minimum cost method, the row minimum cost method, the column minimum cost method, and Vogel's approximation method, and computer applications such as LINGO, Lingo, and solver function using Microsoft Excel. This case study focuses on applying the Microsoft Excel function to find the minimum cost of the transportation system in waste materials to reduce the transportation cost from the city centre to the final destination (dumping area) to be used. The results achieved with the model aided in obtaining optimal solutions and finally, the optimal solutions were checked.

Economic Dispatch Considering Hourly Capacity Allocation with a Variable Renewable and Hydro-Based Generation Portfolio

The objective of this paper is to assess an economic dispatch considering a power system portfolio, which includes predominant amount of hydro power and increasing quantities of intermittent renewables in relation to the total electric capacity. With growing importance of intermittent wind and solar generation taking part into power systems worldwide, there is need for greater chronological resolution to estimate the flexibility of the power system to offer firm capacity. In this way, a linear optimization model operating hourly is developed to calculate the minimum power system cost, while stablishing the capacity allocation to meet the projected load throughout one-year simulation, as an estimation of how the hourly economic dispatch impacts the scheduling of generators belonging to a power system with this portfolio composition. A central focus is how to operate the available hydro capacity to back up intermittent renewables, evaluating the physical hydro operating constraints, monthly energy balance and maximum power availability. A case study was simulated based on the Brazil’s power system configuration, showing that existing hydro capacity provide hourly flexibility to back-up intermittent renewables, potentially saving 1.2 Billion R$, about 3.6% of total system cost referred to 2019. It is worthwhile to realize that the developed methodology can be employed to other power systems with similar capacity portfolio structure for the purpose of calculating its optimum allocation for a specified region and target year.

Modelling the fluid mechanics in single-flow batteries with an adjacent channel for improved reactant transport

Redox flow batteries (RFBs) are an emerging electrochemical technology envisioned towards storage of renewable energy. A promising sub-class of RFBs utilizes single-flow membraneless architectures in an effort to minimize system cost and complexity. To support multiple functions, including reactant separation and fast reactant transport to electrode surfaces, electrolyte flow must be carefully designed and optimized. In this work, we propose adding a secondary channel adjacent to a permeable battery electrode, solving for the flow field and analysing the effects on the reactant concentration boundary layer at the electrode. We find that an adjacent channel with gradually changing thickness leads to a desired nearly uniform flow through the electrode to the adjacent channel. Consequently, the thickness of the concentration boundary layer is significantly reduced, increasing reactant transport to the electrode surface to 140% of the rate of a battery with a constant width adjacent channel, and 350% of the rate with no adjacent channel. Overall, this theory provides insight into the important role of flow physics for this promising sub-class of flow batteries, and can pave the way to improved energy efficiency of such flow batteries.

Optimal Sizing of Standalone PV-Wind Hybrid Energy System in Rural Area North Egypt

This paper studies the sizing of stand-alone renewable energy system applied in rural areas in the north of Egypt. The available renewable energy sources in these areas are investigated to be integrated to supply the different types of electrical loads. The quality and quantity of these sources over various weather and climate changes are studied to construct a robust energy system. The load demand in such areas is determined according to all activities require electrical energy. This study considers the different economic levels and technologies which affect the load demand value. The technique and economical indices required to obtain the optimal are investigated and applied in the various estimated cases. The genetic algorithm (GA) technique is applied to determine the size and number of photovoltaic panels and wind turbines. The obtained solution takes into account the loss of power supply probability and the minimization of system cost. This study presents an essential phase in the sustainable development of such rural areas.

Modeling a robust multi-objective locating-routing problem with bounded delivery time using meta-heuristic algorithms

Purpose This study aims to investigate a locating-routing-allocating problems and the supply chain, including factories distributor candidate locations and retailers. The purpose of this paper is to minimize system costs and delivery time to retailers so that routing is done and the location of the distributors is located. Design/methodology/approach The problem gets closer to reality by adding some special conditions and constraints. Retail service start times have hard and soft time windows, and each customer has a demand for simultaneous delivery and pickups. System costs include the cost of transportation, non-compliance with the soft time window, construction of a distributor, purchase or rental of a vehicle and production costs. The conceptual model of the problem is first defined and modeled and then solved in small dimensions by general algebraic modeling system (GAMS) software and non-dominated sorting genetic algorithm II (NSGAII) and multiple objective particle swarm optimization (MOPSO) algorithms. Findings According to the solution of the mathematical model, the average error of the two proposed algorithms in comparison with the exact solution is less than 0.7%. Also, the algorithms’ performance in terms of deviation from the GAMS exact solution, is quite acceptable and for the largest problem (N = 100) is 0.4%. Accordingly, it is concluded that NSGAII is superior to MOSPSO. Research limitations/implications In this study, since the model is bi-objective, the priorities of decision makers in choosing the optimal solution have not been considered and each of the objective functions has been given equal importance according to the weighting methods. Also, the model has not been compared and analyzed in deterministic and robust modes. This is because all variables, except the one that represents the uncertainty of traffic modes, are deterministic and the random nature of the demand in each graph is not considered. Practical implications The results of the proposed model are valuable for any group of decision makers who care optimizing the production pattern at any level. The use of a heterogeneous fleet of delivery vehicles and application of stochastic optimization methods in defining the time windows, show how effective the distribution networks are in reducing operating costs. Originality/value This study fills the gaps in the relationship between location and routing decisions in a practical way, considering the real constraints of a distribution network, based on a multi-objective model in a three-echelon supply chain. The model is able to optimize the uncertainty in the performance of vehicles to select the refueling strategy or different traffic situations and bring it closer to the state of certainty. Moreover, two modified algorithms of NSGA-II and multiple objective particle swarm optimization (MOPSO) are provided to solve the model while the results are compared with the exact general algebraic modeling system (GAMS) method for the small- and medium-sized problems.

A niching differential evolution algorithm for the large-scale combined heat and power economic dispatch problem

The combined heat and power economic dispatch (CHPED) problem, which aims at minimizing system cost to supply system demand under various constraints, is one of the most challenging problems in the power system. Numerous researches are concentrated on solving the CHPED problem with small size, ignoring implementation of the large-scale CHPED problem with lots of local optimal solutions. This paper proposes a niching differential evolution algorithm (NDE), which incorporates niching methods into differential evolution algorithm (DE), to solve the large-scale CHPED problem. In NDE, a modified DE/best/1 strategy inspired by the neighborhood concept of niching methods, called DE/best/1/nh, is developed to generate diverse solutions. A two-phase selection that utilizes the deterministic crowding of niching methods and greedy selection is designed to remain promising solutions. The two strategies help to balance the global and local search capabilities of the algorithm. The performance of NDE is evaluated by a comparative analysis with other existing approaches on the five CHPED systems with 7, 24, 84, 96 and 192 units. The numerical results demonstrate the feasibility and effectiveness of NDE in solving the large-scale CHPED problem.

Is total system cost minimization fair to all the actors of an energy system? Not according to game theory

A common approach to energy system optimization is to minimize overall costs at system level, regardless of the actors actually bearing those costs. This paper presents an approach inspired by Nash game theory concepts, in which the actors involved in an energy system determine their optimal strategies according to their own economic interests (profit functions) in a non-cooperative or in a cooperative way. A simple case study, considering an electric utility and individual heating consumers in the municipal energy system of a small town in northern Sweden, shows the differences between the two approaches. The game theory approach is able to represent more realistic interactions among the actors of an energy system, fair in fulfilling their conflicting economic interests, and, therefore, a more suitable tool for decision makers evaluating the impacts of policy instruments.

Task Scheduling Strategy Based on Queueing Model in Cloud Computing and Its Energy Consumption Optimization Analysis

In order to reduce the energy consumption of the cloud computing system while ensuring the response performance of the cloud users, a task scheduling strategy is proposed. In the strategy, the local processor of the mobile device continues to work, the virtual machines in a physical machine sleep synchronously, and the virtual machines in different physical machine sleep asynchronously. For the heterogeneous physical machines in cloud computing, a queueing model with a synchronous multiple vacation is established. By using the quasi-birth-death process and the matrix geometric solution, the steady-state distribution of the queueing model is given, and the expressions of the average response time of the tasks and the average power of the system are derived. The numerical results show that there is a power-performance trade-off for cloud computing when setting the assigning probability of the tasks to the local processor. Through the improved whale optimization algorithm, the task scheduling strategy is optimized with the minimum system cost.

Estimate the useful life for a heating, ventilation, and air conditioning system on a high-speed train using failure models

<p class="Abstract">Heating, ventilation, and air conditioning (HVAC) is a widely used system used to guarantee an acceptable level of occupancy comfort, to maintain good indoor air quality, and to minimize system costs and energy requirements. If failure data coming from company database are not available, then a reliability prediction based on failure rate model and handbook data must be carried out. Performing a reliability prediction provides an awareness of potential equipment degradation during the equipment life cycle. Otherwise, if field data regarding the component failures are available, then classical reliability assessment techniques such as Fault Tree Analysis and Reliability Block Diagram should be carried out. Reliability prediction of mechanical components is a challenging task that must be carefully assessed during the design of a system. For these reasons, this paper deals with the reliability assessment of an HVAC using both failure rate model for mechanical components and field data. The reliability obtained using the field data is compared to the one achieved using the failure rate models in order to assess a model which includes all the mechanical parts. The study highlights how it is fundamental to analyze the reliability of complex system integrating both field data and mathematical model.</p>

Fix-and-optimize approach for a healthcare facility location/network design problem considering equity and accessibility: A case study

This study addresses a healthcare facility location/network design problem considering equity and accessibility. Locating the healthcare facilities, planning the capacity of healthcare facilities, and designing the transfer network are the primary goals of the given problem. The presented model aims to minimize system costs, maximize accessibility, and minimize inequality among all demand nodes. A real-world case study is presented to elucidate the performance and applicability of the proposed model. Moreover, a fix-and-optimize (FO) approach is proposed for tackling the problem on a large scale. The obtained results from experiments on several test problems indicate the efficiency of the developed FO.

Optimum sizing and simulation of hybrid renewable energy system for remote area

In current scenario, people tend to move towards outskirts and like to settle in places that are close to nature. But, due to urban lifestyle and to fulfill the basic needs, demand of electricity remains the same as in urban areas. This demand of electricity can be only fulfilled by using hybrid renewable energy resources, which is easily available in outskirts. Renewable energy resources are unreliable and more expensive. Researchers are working to make, it more reliable and economic in terms of utilization. This article proposes a metaheuristic grasshopper optimization algorithm (GOA) for the optimal sizing of hybrid PV/wind/battery energy system located in remote areas. The proposed algorithm finds the optimal sizing and configuration of remote village load demand that includes house electricity and agriculture. The optimization problem is solved by minimization of total system cost at a desirable level of loss of power supply’s reliability index (LPSRI). The results of GOA are compared with particle swarm optimization (PSO), genetic algorithm (GA) and hybrid optimization of multiple energy resources (HOMER) software. In addition, results are also validated by modeling and simulation of the hybrid energy system and its configurations at different weather conditions-based results. Hybrid PV/wind/battery is found as an optimal system at remote areas and sizing are[Formula: see text] with cost of energy (COE) (0.3473$/kWh) and loss of power supplies reliability index (LPSRI) (0%). It is clear from the results that GOA based methods are more efficient for selection of optimal energy system configuration as compared to others algorithms.

Upholding labor productivity with intensified heat stress: Robust planning for adaptation to climate change under uncertainty

The intensification of heat stress in a changing climate poses great threats to both human health and labor productivity. It is of great practical importance to assess the impacts of climate-induced heat stress on labor productivity and to develop effective adaptation strategies. In this paper, an integrated optimization-based productivity restoration modeling framework is proposed for the first time to develop the optimal policies for adaptation to climate change. To address underlying uncertainties associated with climate and labor management systems, we take into account ensemble projections from five global climate models (GCMs) under two Representative Concentration Pathways (RCP2.6 and RCP8.5) and inexact system costs. The system costs, including direct and indirect costs such as management costs, energy costs, and labor costs, are presented as interval numbers due to inherent uncertainty caused by population growth, technology development, and other social-economic factors. Uncertain information can be effectively communicated into the optimization processes in this study to generate optimal and reliable decision alternatives. We find that the increased Wet-Bulb Globe Temperature (WBGT) will lead to a large reduction in labor capacities over China except for the Tibetan Plateau under both RCPs by the end of the 21st century. The less developed regions tend to achieve the minimum system cost by having labor productivity recovered through working overtime due to the relatively low cost of overtime. This could result in more heat-related work injuries in the less developed regions. Since the less developed regions are not heat-prone areas in China, the changing climate would be a more dangerous threat and cause more damages to these regions where the residents are less acclimatized to heat stress. Moreover, we obtain a range of minimum system costs from 1.86 to 8.97 billion dollars under RCP2.6 and from 9.42 to 32.31 billion dollars under RCP8.5 (about 0.2% of China's GDP in 2019, 0.01% of China's GDP projected in 2100 under a sustainable socio-economic development scenario) for the restoration of labor productivity in a warming climate. We argue that urgent actions are needed to mitigate global warming impacts on labor productivity.

Optimization of substation grounding grid design for horizontal and vertical multilayer and uniform soil condition using Simulated Annealing method.

Grounding systems are critical in safeguarding people and equipment from power system failures. A grounding system’s principal goal is to offer the lowest impedance path for undesired fault current. Optimization of the grounding grid designs is important in satisfying the minimum cost of the grounding system and safeguarding those people who work in the surrounding area of the grounded installations. Currently, there is no systematic guidance or standard for grounding grid designs that include two-layer soil and its effects on grounding grid systems, particularly vertically layered soil. Furthermore, while numerous studies have been conducted on optimization, relatively limited study has been done on the problem of optimizing the grounding grid in two-layer soil, particularly in vertical soil structures. This paper presents the results of optimization for substation grounding systems using the Simulated Annealing (SA) algorithm in different soil conditions which conforms to the safety requirements of the grounding system. Practical features of grounding grids in various soil conditions discussed in this paper (uniform soil, two-layer horizontal soil, and two-layer vertical soil) are considered during problem formulation and solution algorithm. The proposed algorithm’s results show that the number of grid conductors in the X and Y directions (Nx and Ny), as well as vertical rods (Nr), can be optimized from initial numbers of 35% for uniform soil, 57% for horizontal two-layer soil for ρ1> ρ2, and 33% for horizontal two-layer soil for ρ1< ρ2, and 29% for vertical two-layer soil structure. In other words, the proposed technique would be able to utilize square and rectangle-shaped grounding grids with a number of grid conductors and vertical rods to be implemented in uniform, two-layer horizontal and vertical soil structure, depending on the resistivity of the soil layer.