Minimum Installation Cost Research Articles

Power system loading margin enhancement based on a multi-objective TCSC placement to mitigate the risk of blackouts

In the evolving power systems, loading margin (LM) enhancement has a significant effect on the mitigation of the risk of cascading line outages and thereby the occurrence of large blackouts. On the other hand, flexible alternating current transmission system (FACTS) devices, especially thyristor-controlled series compensated (TCSC) devices, are more effective than other mitigation strategies such as constructing new lines because of environmental and economic reasons. In this paper, a multi-objective framework is presented to maximize the LM with a minimum installation cost of TCSCs. The optimization problem is solved by using the ɛ-constraint method. The benefit of each Pareto solution is calculated by the probabilistic risk assessment, and the most preferred one is selected by a benefit/cost analysis. The proposed method is developed based on a modified alternating current (AC) version of the ORNL–Pserc–Alaska (OPA) model inspired by the self-organized criticality (SOC) theory in complex power grids. Using the benefit/cost criterion, it is revealed that by the proposed approach, a larger value for the most preferred Pareto solution is obtained in comparison with the defined transmission expansion planning (TEP) scenarios. It confirms the suitability of the proposed method to suppress the cascading outages instead of expensive and time-consuming policies such as building new lines.

Solar energy for low carbon buildings: choice of systems for minimal installation area, cost, and environmental impact

Solar application in buildings is limited by available installation areas. The performance of photovoltaic (PV) and solar collectors are compared in meeting the heating and cooling demand of a residential house using 100% solar energy through TRNSYS modelling of five systems that use air source heat pump and seasonal energy storage as optional assisting technologies. The results show that in a large scale, the PV working with air source heat pump is more efficient than the solar collector system. However, the photovoltaic/thermal (PV/T) is the most spatially efficient with an energy capacity of 551 kWh/m2, 10.6% higher than that of the PV. Compared with the air source heat pump heating system using grid power, using solar energy regardless of system formats can reduce emission by 72% in a lifetime of 20 years. Solar energy can become cost-effective if the utility price is increased to above 0.7 CNY/kWh. The results can help in renewable planning in the studied climate.

Investigation of optimum FSO communication link using different modulation techniques under fog conditions

The FSO communication system offers high data rate investigated over the last few decades because of extraordinary advantages like unlicensed frequency and bandwidth at low power consumption, simple design, hasty, and minimal installation cost, including no right of way. It is essential to investigate solutions against degrading factors like absorption and scattering caused by fog, dust, rain, smog, and uncertain temperature variation of environmental channels. In this work various modulation techniques (AM, CS-NRZ, CS-RZ, DB-NRZ, MDB-NRZ, MDB-RZ, RZ, NRZ) are simulated and used to mitigate the weather attenuation of the specific airfield of Lahore, Pakistan under fog conditions, to provide a reliable FSO communication link for high data rate up to 40 Gbps over a link distance from 1.2 to 1.8 km at transmitted power up to 34 dBm in congested region. The real-time visibility data was taken metrological department for the estimation of attenuation under fog conditions and simulated using Optisys software for further investigation. To choose an FSO communication link, analysis for data rate, link distance, SNR, BER and Q-factor are performed under fog conditions using eight different modulation techniques. An increase in signal channel loss has been observed under fog conditions and performance of the FSO communication system is degraded consequently. The 3 R’s (range, rate, and reliability) depend on each other if the link range is tarnished in a foggy condition that will also degrade the data rate and subsequently, reliability of the FSO system. It is observed that for maximum link distance, the performance parameters of AM modulation technique are prominent and more efficient, offering better Q-factor value at 6.08 dB, lower bit error rate at 7.03 × 10−10, and better SNR of 4.29 dB. The results also show that AM modulation technique offers better signal-to-noise power and has good SNR due to well-received signal power as compared to all other modulation techniques. This research will be helpful to design and implement an FSO communication system under foggy conditions in a metropolitan city to provide a high data communication link among different national institutions.

SMART IGNITION USING HELMET DETECTION

Over the past few decades, the percentage of individual transportation has increased from 39.2% to 77% globally, which shows rapid growth in the sales of Automotive Industries, as this increases, it also shows an increased rate of road accidents. The goal of this project is to use the Raspberry Pi, for the envisioned advanced system. The Raspberry Pi will serve as a command module. This project aims to introduce an efficient and feasible methodology by implementing Object Detection for improved safety. The project incorporates image processing where the authorized user’s wearing a helmet are recognized and verified with the database and allows users to turn on the Ignition System. Implementation of this system can be done on motorcycles with minimal installation cost which provides safety to the users. Keeping in mind the future perspectives, Smart Ignition Using Helmet Detection can surely provide a better system thereby ensuring passenger safety.

Simultaneous allocation ofEVcharging stations and renewable energy sources: An EliteRERNN‐m2MPAapproach

This article proposes a hybrid optimization technique for optimal location and sizing of electric vehicle fast charging stations (EVFCSs) and renewable energy sources (RESs). The proposed hybrid optimization technique is the consolidation of recalling-enhanced recurrent neural network (RERNN) and Marine Predators Algorithm (MPA), hence it is called RERNN-m2MPA technique. Here, an enhanced MPA (m2MPA) is proposed. The major objective of this article is to energy loss reduction, voltage deviation of the power system network and minimization of the land cost with maximum weightage to serve maximum EV with minimum installation cost. The lessening of voltage deviation is considered as objective function to maintain a stable voltage profile in every nodes. The land cost is diminished with weightage maximization to serve maximal electric vehicle with minimal installation cost of electric vehicle charging station (EVCS). In this proposal, optimal location with capacity of fast charging stations and RESs are determined concurrently, while determined the deviation paths and uncertainties of RESs. To this intention, RERNN is proposed to cover the charging demand for EVs on the transportation network. Moreover, an m2MPA is proposed to optimal allocation of RESs and fast charging stations with respect to the distribution and transportation networks. Likewise, the proposed technique minimizes the adverse effects of fast charging demand on the grid, particularly during peak periods reducing the need for larger capabilities of energy storage units to absorb fast charging fluctuations and reduces the need for separate FCS installation. By then, the proposed technique is carried out in MATLAB/Simulink platform, its performance is compared with various existing techniques.

A Techno-Economic Analysis of Energy Storage Components of Microgrids for Improving Energy Management Strategies

Microgrids are essential elements of the energy transition because they allow optimal use of renewable energy sources (photovoltaic panels, wind turbines) and storage devices (batteries, supercapacitors) by connecting them to consumption poles (e.g., buildings, charging stations of electric vehicles). Lithium-ion batteries and supercapacitors are the main electrical storage devices usually used by microgrids for energy and power transient management. In the present paper, microgrid simulations have been performed. Electrothermal and aging models of storage components are presented. Strategies and scenarios for the batteries are presented either based on the state of charge limitation or hybrid association with supercapacitors. The contribution of this study is to provide a management strategy which considers the aging of storage systems in the real-time management of the microgrid in order to extend their life, while minimizing installation costs. The first approach for a techno-economic study provided in that study enables us to improve the strategies by optimizing the use of the battery. The results obtained in this paper demonstrate the key role of the techno-economic approach and knowledge of the aging processes of storage devices in improving the energy management and global feedback costs of microgrids. The simulation results show that battery life can be improved by 2.2 years. The improvement in battery life leads to a reduction in the total cost of the installation by reducing the cost of the batteries.

A Comparative performance evaluation of a set of swarm intelligence based optimization algorithms for economic operation with FACTS devices in Power system

In this article an effective Reactive Power Management (RPM) method by using Flexible AC Transmission System (FACTS) has been proposed which minimizes the loss of energy, improve the power transfer capacity and reduce the overall cost of transmission network lines. The position of FACTS is optimized by two recently proposed heuristic optimization techniques, i.e., Gravitational Search Algorithm (GSA) and Teaching Learning Based Optimization (TLBO). The optimal positions of these FACTS are found by utilizing these GSA and TLBO algorithms. It is observed that by installing the FACTS in this optimal position improves the voltage profile with minimum installation cost. The overall performance of the proposed approaches is investigated by implementing it on IEEE 30-bus network.

Operation policy of multi-capacity logistic robots in hotel industry

PurposeThis paper aims to propose an operation policy of multi-capacity room service robots traveling within a hotel. As multi-capacity robots can serve many requests in a single trip, improved operation policy can reduce the investment cost of robots.Design/methodology/approachUsing a mathematical model-based optimization technique, an optimal set of robots with minimum installation cost is derived while serving the entire room service demands. Through testing a variety of scenarios by changing the price and function of robots to be installed, insights that consider the various situations are offered.FindingsThough the increase in capacity saves much time for room service at a lower capacity level, the amount of time saved gradually decreases as the capacity increases. Besides, the installation strategy is divided into two cases depending on the purchase cost of robots.Research limitations/implicationsCurrently, the studies focusing on the adoption of service robots from an operations view are rarely be found. To reduce the burden of investment cost, this study takes the unique approach to improve the operation policy of service robots by using the multi-capacity robots.Practical implicationsThis study guides the hotel to install an adequate set of robots. The result confirms that the optimal installation set of robots is affected by various factors, such as the room service information, the hotel structure and the unit execution cycle.Originality/valueAfter the outbreak of COVID-19, people avoid face-to-face contact and interest in non-contact service is growing. This paper deals with the efficient way to implement non-contact delivery through logistic robots, a timely and important topic.

Optimal Allocation of Synchrophasor Units in the Distribution Network Considering Maximum Redundancy

Phasor Measurement Unit (PMU) is a smart measuring device commonly used in wide-area monitoring systems. It provides the synchronized phasor values and the magnitudes of voltages and currents in real-time for the proper state calculation of the electrical network in a common time reference frame. But in order to avoid unnecessary placements, minimize installation cost, and due to the lack of communication facilities at the substations, the placing of PMUs at every location is not possible. Therefore several optimization techniques have been developed to solve the Optimal PMU Placement (OPP) problem. The OPP problem aims to reduce the number of PMUs by achieving a completely observable network. Many solutions to the OPP problem have been proposed for the transmission networks with the use of conventional and heuristic-based approaches, but very few for distribution networks. In this paper, the Binary Grey Wolf Optimization (BGWO) algorithm is proposed to solve the OPP problem considering the measurement redundancy (MR) to achieve complete observability of the distribution network. Finally, case studies have been done by implementing the proposed algorithm on different IEEE test feeders such as the IEEE -13, -33, -37, and -123 node feeder systems. The obtained results are compared with previous studies to verify the feasibility and efficiency of the proposed technique.

A novel approach for optimal allocation of series FACTS device for transmission line congestion management

AbstractThe reformed electricity market structure has forced the transmission network to operate near its operational limits. Due to competition in electricity market and with increasing demand, the probability of occurrence of congestion in transmission network has also increased. This resulted in insecure operation of power system and inefficient operation of the electricity market. The main aim of this article is to achieve efficient management of congestion in terms of minimum cost of electricity. This is accomplished by optimally allocating thyristor‐controlled series capacitor (TCSC) in the system. Since flexible alternating current transmission system (FATCS) devices involve heavy installation cost, its proper allocation plays a vital role in achieving the electricity market economics. Therefore, in this article, a new sensitivity index called line flow sensitivity factor is proposed to find the optimal location of TCSC while its control parameter setting is optimally obtained with particle swarm optimization algorithm. The obtained results are validated through implementation of TCSC based on its minimum installation cost. Further, two different penalty factors for violation of system constraints are also introduced in the problem formulation to effectively consider the competitive nature of the electricity market. The proposed method is tested on IEEE 30‐bus system, IEEE 118‐bus system, and 33‐bus Indian network and the results are found to be encouraging. The performance of the optimization algorithm on different systems is also analyzed in this article.

Secure Transmission Lines Monitoring and Efficient Electricity Management in Ultra-Reliable Low Latency Industrial Internet of Things

Smart Grid (SG) faces several challenges to efficiently transfer the power generated to power consumers. So, a robust monitoring tool is required to monitor the transmission lines in order to ensure security of the resource. This power transmission monitoring is a good example of ultra reliable low latency application of 5G with the aim to provide quality of service and quality of experience. The primary objective of this study is to design a wireless network for real-time monitoring of transmission lines to take the preventive measures. In this paper, we present an Internet of Things enabled real-time transmission line monitoring system comprising of wireless, wired, and cellular technologies. The objective is to minimize the time delay at minimum installation cost of the network. In our proposed model, all the sensors are powered by Renewable Energy Resources (RES) like wind and solar energy, etc. The placement problem is formulated to determine the location of cellular enabled transmission towers. Moreover, feasible regions are also calculated to show the relationship between time delay and energy consumption. Results show that proposed model provides efficient solutions and takes less time for data transmission and is more energy efficient.

A Game Approach for Charging Station Placement Based on User Preferences and Crowdedness

The placement of electric vehicle charging stations (EVCSs), which encourages the rapid development of electric vehicles (EVs), should be considered from not only operational perspective such as minimizing installation costs, but also user perspective so that their strategic and competitive charging behaviors can be reflected. This paper proposes a methodological framework to consider crowdedness and individual preferences of electric vehicle users (EVUs) in the selection of locations for fast-charging stations. The electric vehicle charging station placement problem (EVCSPP) is solved via a decentralized game theoretical decision-making algorithm and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means clustering algorithm. The proposed algorithm, referred to as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -GRAPE, determines the locations of charging stations to maximize the sum of utilities of EVUs. In particular, we analytically present that 50% of suboptimality of the solution can be at least guaranteed, which is about 17% better than the existing game theoretical based framework. We show a few variants to describe the utility functions that may capture the difference in preferences of EVUs. Finally, we demonstrate the viability of the decision framework via three real-world data-based experiments. The results of the experiments, including a comparison with a baseline method are then discussed.

Development of Low-Cost Natural Cooling Chamber for Preservation of Vegetables and Fruits in Rural Area

India is an agricultural-based country and this output creates a deep impact on the Indian economy and the rest of the factors. Hence upgrading the traditional farming techniques by the use of technology should be our priority. In the case of fruits and Vegetable farming, it is seen that most of the output gets decomposed or gets dried after harvesting because of various problems like no preserving units into the farms, irregular timings of transportation facilities, wrong methods of storage, etc. This directly affects the fruits and vegetables by lowering the physiological loss in weight as well as by damaging their quality. So it is very important to find out a good preserving unit to increase the shelf life and to maintain the freshness of fruits and vegetables for a longer time. As well as, it is very difficult to install the refrigerating systems into the farms. It would cost a lot too. Hence we have to make this possible by the use of natural things which would be easily available to the farmers and very easy to build. These conditions can be achieved by installing a Natural cooling chamber. It provides higher humidity and lower temperature on the inner side of the chamber which would not promote any physiological loss in weight as well as other metabolic processes, resulting increase of shelf life by maintaining 10°C–15°C temperature below the ambient temperature. Also, it is very easy to build, has less maintenance, requires no electricity, and has a minimum cost of installation. Farmers would have a really good advantage of this natural preserver. How to cite this article: Kulkarni S, Haswal S, Galatage S et al. Development of Low-Cost Natural Cooling Chamber for Preservation of Vegetables and Fruits in Rural Area. J Adv Res Mech Engi Tech 2020; 7(3): 16-25. DOI: https://doi.org/10.24321/2454.8650.202005

Development and Testing of a Test Level 4 Concrete Bridge Rail and Deck Overhang

A Manual for Assessing Safety Hardware (MASH)-compliant Test Level 4 (TL-4) concrete bridge rail was optimized to satisfy MASH TL-4 design loads, maximize vehicle stability, minimize installation costs, and mitigate the potential for deck damage by minimizing loads transfer to the deck. Additionally, the bridge rail was designed with a 39 in. installation height so that it would remain crashworthy after future roadway overlays up to 3 in. thick. The barrier had a front face with a 3-degree slope from vertical to promote vehicle stability during impacts while also providing some slope to allow for slipforming installations. Yield line theory was utilized to design both interior and end regions of the barrier. Further, minimum deck strengths were determined and a deck overhang design procedure was provided for users desiring to modify their existing deck details. Finally, MASH Test 4-12 was conducted on the new bridge rail to evaluate its safety performance criteria, damage to the barrier and a critical deck configuration, and its working width. In test 4CBR-1, the 22,198 lb single-unit truck impacted the concrete bridge rail at a speed of 57.6 mph and an angle of 16 degrees. The single-unit truck was successfully contained and redirected, and all safety performance criteria were within acceptable limits as defined in MASH. Therefore, test 4CBR-1 was determined to be acceptable according to MASH Test 4-12. Conclusions and recommendations for implementation are provided.

RETRACTED ARTICLE: Estimation of loadability limit with N-1 and N-2 outages using evolutionary computation techniques

Voltage stability primarily depends on the voltage magnitude, phase angle, real and reactive power constraint of the electric power system. Even during emergencies like contingency (outages), the stability of the electric power structure will be enhanced by improving the Loadability Limit (LL) of the transmission sector. Flexibility in the real and reactive power flow in the transmission system is achieved by the Flexible AC Transmission System (FACTS) devices. These devices can be placed anywhere in the transmission sector. To get effective control over the power flow through the transmission lines and to achieve the maximum loadability with the minimal installation cost, optimal choice and placement of FACTS devices are essential. In this manuscript, efforts had been taken to analyze the LL with outages for hybrid electric power structures. The proposed method is simulated and tested with the hybrid version of standard IEEE 30 bus system. Three types of FACTS devices like Thyristor Controlled Series Capacitor (TCSC), Static VAr Compensator (SVC) and Unified Power Flow Controller (UPFC) are efficiently selected and placed in the transmission lines. For the optimal positioning and placement of these devices, the Contingency Severity Index (CSI) and Fast Voltage Stability Index (FVSI) have been used. Differential Evolution (DE) and Modified Differential Evolution (MDE) algorithms are applied to optimize the obtained results. DE is an evolutionary search based soft computing algorithm popularly handed to resolve multifarious problems. MDE is the enhanced version of DE that embraces a prior knowledge about the solution space at every stage of the search. The main focus of this work is (i) to identify the weak branches and buses in the system using CSI and FVSI. (ii) to optimize the number, location and settings of FACTS devices using various soft computing techniques like DE and MDE. (iii) to evaluate ML of a transmission system with FACTS devices under normal and contingency conditions using DE and MDE. (iv) to calculate the cost required for the installation of FACTS devices. (v) to enhance ML of pool and hybrid model of deregulated electric power market with contingency using the optimal number, rating and positioning of FACTS devices.

A Novel Framework for Cost Optimization of Renewable Energy Installations: A Case Study of Nigeria

The merits of utilizing renewable energy sources (RESs) in electricity generation, especially in the developing countries, are to improve the electricity access, economic development and energy sustainability. Nigeria is a developing country with an acute electricity problem. The country is blessed with rich renewable energy (RE) resources. However, most of these resources are yet to be exploited. A look at the energy sector in Nigeria suggests that for the country to be energy sufficient, it must embrace domestic RESs in its energy mix. However, RE technologies are capital intensive. Hence, by using Nigeria as a motivation, this study aims at developing a general framework that can be used for any country or region in determining the optimal total capacities of RESs to be installed in different locations, to increase the use of RE in a cost-effective manner. The designed optimization problem aims to minimize the total cost of installing RE technologies while satisfying some predetermined constraints that include demand and supply, RE potential, area and system reserve requirement. To this end, three different scenarios, namely prospective off-grid, on-grid, and all-off-grid are introduced. The first scenario aims at finding the optimal cost of installing RESs in order to improve electricity access at each off-grid installation location (a distribution company (DISCO) location with electricity access level below 50%). The second scenario optimizes cost of installing RESs for on-grid installation locations (DISCO locations (DLs) with electricity access level of 50% and above). The third scenario finds the optimal cost of installing off-grid RESs for all the DLs. Linear optimization technique is used to solve the problem. The results show that the total installation costs of the first and second scenarios (which means having off-grid installations for some DLs and having on-grid installations for other DLs) are $97.46 billion and $114.03 billion respectively, with a total cost of $211.49 billion. On the other hand, the result of third installation scenario (only off-grid installations for all DLs), is found to be $244.33 billion. These results reveal that the combination of off-grid and on-grid installations (first and second scenarios) has the minimum installation cost, for the case study of Nigeria. As the framework presented in this study is used to provide the minimum RE related total installation cost and related RE planning in Nigeria, it can also be useful for other countries or regions considering RE planning.

Optimization model for the installation of SAMU bases: application in Natal-RN

A Medical Emergency Service is responsible for providing pre-hospital acute care and has the key role of providing good services to people, especially in urban areas. In Brazil, the Emergency Medical Assistance Service (SAMU) operates in several cities and aims to reach the victim early after an injury to his health has occurred, by sending vehicles manned by trained personnel. The objective of this study was the application of a mathematical model aiming to designate neighborhoods for the installation of new SAMU bases that minimize the distance traveled by the ambulances in the city of Natal / RN. After data collection, it was found that the average service response time of the SAMU was over 40 minutes for calls classified as red code. The average number of calls in 2015 was 1,930 per month, in this city with a population of 800,000. The application of the model allowed for the simulation of scenarios with the installation of 3 to 8 fixed bases. There was a significant reduction in the distance traveled by the ambulances which reached 48%, after the installation of eight bases. In other words, there was a reduction of 6,560 kilometers traveled per month by ambulances. The new SAMU bases are being installed in containers to minimize installation costs and easy relocation in the near future.

Improvement of Power System Loading Margin with Reducing Network Investment Cost Using SVC

Under emergency conditions to reduce the harms from environmental deterioration, one of the recently focused developments in the power industry is to make the existing transmission networks sufficiently utilize their capability in power transfer. From the detailed analysis and many studies, voltage instability was found to be the main factor responsible for several blackout events. As an index to indicate the level of static voltage stability of a transmission system, the Loading Margin (LM) or Voltage Stability Margin (VSM), represents the maximum power that can be transferred between generators and loads before voltage collapse point achieved is generally measured in system planning. In this paper, under each contingency with high Risk Index (RI) value, the Modal Analysis (MA) technique is used to determine which buses need Static VAR Compensator (SVC) installation, and with maximum LM and minimum SVC installation cost composed into the multi-objective function. The optimal LM enhancement problem is formulated as a multi-objective optimization problem (MOP) and solved by using the fitness sharing multi-objective particle swarm optimization (MOPSO) algorithm for a Pareto front set. The proposed method may be tested on the IEEE 24-bus reliability test system (RTS) and IEEE 14-bus system.

Dependency Structure Matrix and Hierarchical Clustering based algorithm for optimum module identification in MEP systems

Abstract Modular prefabrication of mechanical, electrical and plumbing (MEP) systems has become more prevalent during the last decade with the growth of the prefabricated construction industry. However, it is currently accomplished only for smaller systems where integrated packaged units are applied in heating, ventilation and air conditioning (HVAC) and other building services installations. The term ‘optimum modularity’ is rarely accurately used in the industry due to lack of an efficient modularisation method. In this investigation, an automated efficient modularisation algorithm has been developed using an approach which combines fuzzy logic, Dependency Structure Matrix (DSM) and Hierarchical Clustering (HC) methods. The developed algorithm achieves minimum total installation cost by identifying the optimum number modules and module division points based on assembly cost and the handling cost of each module. It is shown that the optimum modularity for a system is highly dependent on the module dimensions and the module division point.

Hybrid heuristic for the optimal design of photovoltaic installations considering mismatch loss effects

We consider the Photovoltaic Installation Design Problem (PIDP). In this problem, photovoltaic modules must be connected in “strings” and wired to a set of electronic components. The aim is to minimize installation costs and maximize power production, which is affected by “mismatch losses” caused by non-uniform irradiation (shading) and is also directly related to design decisions. We relate the problem to the known class of location routing problems and we design a route-first/cluster-second heuristic. We propose an efficient machine learning approach to evaluate Photovoltaic (PV) string performances accounting for mismatch losses. We prove that our approach is effective in real-world instances provided by our industrial partner.