Development Of Optimization Model Research Articles

OTIMIZAÇÃO DE MISTURAS DE CONCRETO PARA REDUÇÃO DE CUSTOS COM USO DE POZOLANA

Concrete is the primary source for the development of civil engineering projects. In Brazil, its use reaches approximately 40 million cubic meters per year. As a result, concrete production is a central element in civil engineering, being used in a wide range of applications, from foundations to large structures. However, the costs associated with concrete production represent a significant portion of the total costs of a project. Therefore, the search for methods that can reduce these costs without compromising the quality and durability of structures is of utmost importance for the sector. The main objective of this work is to explore effective methods for optimizing concrete mixtures using additives or applying alternative materials in order to guarantee the quality of the product, with the aim of improving its physical-mechanical and economic characteristics. To achieve this goal, the following specific objectives were defined: to analyze the physical and mechanical properties of different concrete formulations, to identify the main factors that influence concrete production costs, to develop optimization models for concrete mixtures based on economic and performance criteria, and to evaluate the technical and economic feasibility of the proposed solutions in case studies. During the development of the research, comprehensive literature reviews were carried out to understand the latest trends and practices related to concrete production. In addition, laboratory experiments were conducted to analyze the physical and mechanical properties of different concrete formulations, taking into account the influence of variables such as type of aggregate and the use of pozzolan as an additive to improve concrete. Based on the data researched and the tests developed, it was possible to present pozzolan as a satisfactory additive for addition to concrete, since it reduces production costs while maintaining the essential properties of concrete, such as compressive strength, durability and workability. And because it is a more sustainable material, it reduces environmental damage.

Investigation of mechanical properties in FFF- produced PLA samples using the Erichsen test: application of definitive screening and RSM

Purpose This study aims to investigate how printing parameters affect the mechanical properties of specimens produced through fused filament fabrication, using the Erichsen test to assess deformation characteristics and material durability under stress. Design/methodology/approach Polylactic acid (PLA) specimens were printed and tested in accordance with the ISO 20482 standard. Definitive screening was conducted to identify the most influential process parameters. This study examined the effects of four key process parameters – number of layers, layer height, crossing angle and nozzle diameter – on force, distension, peak energy and energy to break. Each parameter was assessed at three levels and a large number of required experiments was managed by using response surface methodology (RSM). Findings This study revealed that the number of layers, layer height and crossing angle are the most significant factors that influence the mechanical properties of 3D-printed materials. The number of layers had the greatest impact on the peak force, contributing 44.25%, with thicker layers typically enhancing material strength. The layer height has a significant effect on energy absorption and deformation, with greater layer heights generally improving these properties. Nozzle diameter contributed only 1.10%, making it the least influential factor; however, its impact became more pronounced in interactions with other parameters. Originality/value This paper presents a comprehensive experimental investigation into the effects of process parameters on the crack strength and behavior of 3D-printed PLA specimens using the RSM method. The documented results can be used to develop optimization models aimed at achieving desired mechanical properties with reduced variation and uncertainty in the final product.

Interplay Between Servicizing and Remanufacturing: Economic and Environmental Implications

Problem definition: To investigate the interplay between servicizing and remanufacturing, two widely used strategies in the circular economy, and its impact on firms’ economic and environmental performance, we consider a profit-maximizing manufacturer who explores the possibility of jointly adopting the two strategies. Methodology and results: We develop optimization models that capture the main attributes for servicizing (pay-per-use, demand pooling) and remanufacturing (lower production costs, higher operating costs). We show that the presence of remanufacturing always makes servicizing less attractive to adopt, and having servicizing may discourage the adoption of remanufacturing if the remanufacturing cost reduction effect is not sufficiently large. The joint adoption of servicizing and remanufacturing is preferred when the remanufacturing cost is neither too high nor too low and the range of the remanufacturing cost that favors the joint adoption is further moderated by the firm’s pooling level and the operating cost of remanufactured products. Lastly, we find that when the firm adopts servicizing in the presence of remanufacturing with relatively high remanufacturing costs, it can reduce the number of remanufactured products with low environmental impact in the market and, thus, harm the environment, resulting in a misalignment between economic and environmental goals. Managerial implications: Our findings suggest that managers considering jointly adopting servicizing and remanufacturing must balance their competition on the low-usage end of the market with the potential complementarity due to remanufacturing cost reduction. Further, by showing the win-win conditions under which the joint adoption of servicizing and remanufacturing improves firms’ economic and environmental performance, we demonstrate that there are no one-size-fits-all circular economy strategies and call for caution when promoting these strategies across industries. Supplemental Material: The electronic companion is available at https://doi.org/10.1287/msom.2022.0379 .

Models of forming logistics infrastructure for complex equipment recycling

Subject matter: models for forming the logistics infrastructure for complex equipment recycling. The work aims to create optimization models that consider key factors in forming recycling infrastructure and combine centralized and decentralized elements to enhance the efficiency of this process. Tasks: analyze modern approaches to organizing complex equipment recycling; identify criteria and factors for optimizing recycling infrastructure; develop a multi-level recycling infrastructure; formulate optimization models for recycling infrastructure, taking into account enterprise production capacities, logistical costs, environmental impact, and other factors. Methods: a systems approach, structural modeling, optimization models. Research results: approaches to organizing recycling infrastructure were analyzed: centralized, distributed, and combined; the selection of a combined approach for creating logistics infrastructure for complex equipment recycling was justified; a structural multi-level model of recycling infrastructure was developed with a lower level (sorting enterprises), middle level (processing enterprises), and upper level (organizational management); mathematical models for optimizing recycling infrastructure were formulated, considering enterprise production capacities, logistical costs, and environmental impact. The conclusions: proposed structural multi-level model of complex equipment recycling infrastructure combines the advantages of centralized and decentralized management, ensuring flexibility, system reliability against external influences, cost minimization, and sustainable process control. The application of the developed optimization models allows for consideration of enterprise production capacities, logistical costs, environmental impact, and other factors to find the most effective configuration of recycling infrastructure. Implementation of the proposed recycling infrastructure will ensure the formation of coherent interaction between government bodies, enterprises, public organizations, and international partners.

EXPERIMENTAL STUDY AND OPTIMIZATION OF SUBMERGED ARC WELDING PARAMETERS FOR AISI 1055 STEEL USING RESPONSE SURFACE METHODOLOGY

In order to forecast and regulate the quality of the weld with improved mechanical qualities while automating the welding process for an increased production rate, a relationship between the process parameters must be developed. The relationship between the welding parameters and the mechanical properties can be easily tracked using the response surface methodology. The aim of this study is to optimize the parameters in the submerged arc welding of AISI 1055 steel using Box–Behnken design in response surface methodology. The metal deposition rate is more influenced by the current, which simultaneously improves the mechanical properties of the weld. The optimized process parameters are 349.94[Formula: see text]A for current, 52.90[Formula: see text]V for voltage, and 0.6[Formula: see text]m/min for carrier speed. These optimized performance parameters are 461.72[Formula: see text]N/mm2 for ultimate tensile strength, 35.26[Formula: see text]J for impact strength, and 41.95[Formula: see text]HRC for hardness. The desirability of 0.718 is obtained for the developed optimization model, which proves the reliability of the model in the actual experimentation.

Optimizing waste management strategies through artificial intelligence and machine learning - An economic and environmental impact study

Applying artificial intelligence (AI) and machine learning (ML) techniques to optimize waste management strategies, focusing on enhancing economic efficiency and reducing environmental impact, is vital. The study utilized ML models to analyze and forecast waste generation trends, assess the viability of various waste management methods, and develop optimization models for resource allocation and operational efficiency. The research employs the World Bank’s comprehensive waste management dataset. After rigorous data preprocessing, including cleaning and feature selection, a variety of ML techniques, such as regression models, classification algorithms like Support Vector Machines (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and optimization algorithms, including linear programming, are applied. Unlike other research, this study achieved 85 % accuracy on predictive analytics models for forecasting waste generation trends, primarily attributed to integrating more diverse data sets, including socio-economic factors. Also, the optimization resource allocation achieved a 15 % increase in operational efficiency. These findings provide significant insights for policymakers and urban planners, suggesting that integrating ML in waste management can lead to more sustainable and cost-effective practices. This paper demonstrates the transformative potential of ML in optimizing waste management strategies, offering a pathway towards more sustainable and economically viable waste management solutions globally.

Blockchain design for optimal joint production and maintenance over multiple periods for oil-filling production lines

Joint maintenance and production planning result in enhanced system efficiency and a significant reduction in expensive costs. Therefore, this research proposed an integrated blockchain design of two optimization models for the joint scheduling and sequencing of production orders and maintenance jobs over multiple periods with probabilistic and stochastic parameters for the oil-filling process. In the scheduling model, the objective function was to minimize total production and maintenance costs. Further, the sequencing model aimed to minimize the total overtime cost and the sum start times of jobs and orders. The proposed models were implemented on five production lines of five-liter lube oil bottles. Results showed that the developed optimization models effectively achieved concurrent production and maintenance planning at minimal total cost. In practice, this results in effective resource utilization, and saving costly production and maintenance costs. In conclusion, the developed blockchain design is valuable in developing, managing, and controlling optimal joint production and maintenance planning of oil-filling production lines.

DeepDispatch: Deep Reinforcement Learning-Based Vehicle Dispatch Algorithm for Advanced Air Mobility

Near-future air taxi operations with electric vertical takeoff and landing aircraft will be constrained by the need for frequent recharging and limited takeoff and landing pads in vertiports and will be subject to time-varying demand and electricity prices, making the dispatch problem unique and particularly challenging to solve. Previously, the authors have developed optimization models to address this problem. Such optimization models, however, suffer from prohibitively high computational run times when the scale of the problem increases, making them less practical for real-world implementation. To overcome this issue, the authors have developed two deep reinforcement learning-based dispatch algorithms, namely, single-agent and multi-agent double dueling deep Q-network dispatch algorithms, where the objective is to maximize operating profit. A passenger transportation simulation environment was built to assess the performance of these algorithms across 36 numerical cases with varying numbers of vehicles and vertiports and amounts of demand. The results indicate that the multi-agent dispatch algorithm can closely approximate the optimal dispatch policy with significantly less computational expenses compared to the benchmark optimization model. The multi-agent algorithm was found to outperform the single-agent counterpart with respect to both profits generated and training time. Additionally, we implemented a heuristic-based algorithm, faster but less effective in generating profits compared to our two deep reinforcement learning-based algorithms.

Metro timetable optimization for improving regenerative braking energy utilization efficiency

The train generates regenerative braking energy (RBE) during braking. When the RBE generated by the braking train is too large to be absorbed and utilized, the OCS voltage will rise rapidly. It may even exceed the rated voltage threshold, resulting in hidden dangers for driving safety. How to use RBE efficiently and reasonably has become a hot issue in recent years. This paper presents a metro timetable optimization method for multi-train operation to improve the RBE utilization efficiency of trains. Firstly, an energy-saving multi-objective model for multi-train operation is established, which takes the total energy consumption and travel time as the objective functions and the dwell time and departure interval as the constraints. Then, the genetic algorithm (GA) is used to generate the composition sequence of dwell time and departure interval, and the multi-node nonlinear model is used to calculate the total energy consumption of the metro line. Finally, Guangzhou metro line 7 is selected as an example to illustrate the effectiveness of the developed optimization model. The results show that the energy consumption of the whole line can be effectively reduced by adjusting the dwell time and departure interval. Meanwhile, the timetable optimization model with GA has better energy-saving effects by comparative analysis.

Minimizing the environmental impacts of waste valorization systems using multi-criteria life cycle optimization

Unsustainable waste management practices impose severe environmental hazards, health risks, and financial burdens. Waste valorization systems have emerged to mitigate the negative impacts of landfilling while recovering finite materials and renewable energy. This study aims to develop a life cycle assessment (LCA)-based multi-criteria framework using linear programming to optimize municipal waste allocation to multiple valorization facilities. The developed optimization model minimizes the life cycle impacts of integrated waste management on several life cycle impact categories. A multi-criteria decision-making analysis is implemented to account for the relative importance of the examined environmental categories, and a sensitivity analysis is conducted to study the effect of varying the importance weights. The variation between different LCA methods, IMPACT, CML, and ReCiPe, is also investigated. The proposed framework is applied to Egypt based on local conditions and strategic priorities. The LCA findings reveal that processing recyclables in material recovery facilities enhances the environmental performance in all impact categories except ozone layer depletion. To achieve minimum environmental effects on the assessed impact categories, single objective models suggest the diversion of recyclables to material recovery facilities and non-recyclables to gasifiers. The Pareto front of IMPACT involves anaerobic digesters and incinerators, CML comprises composting facilities, and ReCiPe includes incinerators. Overall, this study provides a lifecycle-based decision-making guide for the planning and optimization of sustainable waste management systems, and highlights the potential variations in the outcomes of different LCA methods adopted.

Multi-objective optimization of geosynthetic reinforced soil structures

Optimization models for reinforced earth structures such as foundation pads, bridge abutments, and embankments based on the Eurocode standard are presented. The developed optimization models, which take into account construction costs and environmental footprint, are used to determine an optimal design for each earth structure. The optimization model uses discrete variables, making the results more suitable for actual construction practice and fully exploiting the geotechnical and structural capacity of earth structures with geosynthetic reinforcement. The multi-objective optimization was performed to find a set of solutions that represent the best trade-off between construction cost and environmental footprint. The results show that the correct selection of geosynthetics leads to a significant reduction in costs and environmental impact. The general observation that emerges from the multi-objective optimization is that when designing the earth structures using geosynthetic reinforcements, due to the discrete set of variables, there are not so many optimal solutions that the designer can choose from. The entire optimization process is illustrated with the help of a numerical example. This study can help engineers to select earth structure and geosynthetic reinforcements that are economical and sustainable.

Smart-Contract-Based Agricultural Service Platform for Drone Plant Protection Operation Optimization

The platform-based agricultural service is receiving popularity in small-scale farming and shows significant advantages in gathering dispersed service requests and matching supply and demand. However, it also generates new challenges including service traceability, denial and fraud, information security and privacy issues. Blockchain is an emerging technology that provides a secure and trusted environment to track and manage the service process. In this study, we propose a blockchain-based service platform for efficient agricultural service operations with the support of Internet of Things technology. Following the smart platform, we use the drone plant protection service as an example and develop a new execution procedure for smart contract-based agricultural services. In the proposed procedure, we focus on integrating optimization methods to deal with multiple service requests as well as potential disruption events and establishing detailed interactions among service plans, smart contracts, and physical services. Moreover, we formulate the drone plant protection issue using a mixedinteger linear programming model to obtain the optimal service plan and develop a recovery model to deal with potential disruptions of new order arrival. Finally, we design detailed smart contract terms for drone plant protection services. Results of numerical experiments demonstrate the effectiveness of the developed optimization model in obtaining the optimal service plan before and after disruptions. Also, we verify the applicability and security of the smart contract on the Ethereum platform based on a three-phase functional test and a comprehensive security test.

Determining the playing 11 based on opposition squad: An IPL illustration

Indian Premier League (IPL) is the most popular T20 domestic sporting league globally. Player selection is crucial in winning the competitive IPL tournament. Thus, team management select 11 players for each match from a team’s squad of 15 to 25 players. Different player statistics are analysed to select the best playing 11 for each match. This study attempts an approach where the on-field player performance is used to determine the playing-11. A player’s on-field performance in a match is computed as a single metric considering a player’s attributes against every player present in the opposition squad. For this computation, past ball-by-ball data is cleaned and mined to generate data containing player-vs-player performance attributes. Next, the various performance attributes for a player-vs-player combination is converted into a player’s performance rating by computing a weighted score of the performance attributes. Finally, an optimisation model is proposed and developed to determine the best playing-11 using the computed performance ratings. The developed optimisation model suggests the playing-11 that maximises the possibility of winning against a given opponent. The proposed procedure to determine the playing-11 for an IPL match is demonstrated using past data from 2008-20. The demonstration indicates that for matches in the league stage, the suggested playing-11 by model and the actual playing-11 have a ∼7% similarity across all teams. The remaining ∼3% are different from those selected in the actual team. Nevertheless, this difference approximately yields a ∼ Indian Premier League (IPL) is the most popular T20 domestic sporting league globally. Player selection is crucial in winning the competitive IPL tournament. Thus, team management select 11 players for each match from a team’s squad of 15 to 25 players. Different player statistics are analysed to select the best playing 11 for each match. This study attempts an approach where the on-field player performance is used to determine the playing-11. A player’s on-field performance in a match is computed as a single metric considering a player’s attributes against every player present in the opposition squad. For this computation, past ball-by-ball data is cleaned and mined to generate data containing player-vs-player performance attributes. Next, the various performance attributes for a player-vs-player combination is converted into a player’s performance rating by computing a weighted score of the performance attributes. Finally, an optimisation model is proposed and developed to determine the best playing-11 using the computed performance ratings. The developed optimisation model suggests the playing-11 that maximises the possibility of winning against a given opponent. The proposed procedure to determine the playing-11 for an IPL match is demonstrated using past data from 2008-20. The demonstration indicates that for matches in the league stage, the suggested playing-11 by model and the actual playing-11 have a ∼7% similarity across all teams. The remaining ∼3% are different from those selected in the actual team. Nevertheless, this difference approximately yields a ∼13.32% increase in performance rating compared to the existing team.3.32% increase in performance rating compared to the existing team.

Pareto multi-objective optimization of metro train energy-saving operation using improved NSGA-II algorithms

Quantitative analysis of the factors affecting the energy consumption of metro trains and finding out the breakthrough points of energy conservation is of great practical significance for reducing transport costs and improving energy utilization. Train operation curve optimization is one of the main methods to reduce the energy consumption of train operation. This paper introduces the metro train energy-saving operation optimization model with fixed travel time. Firstly, taking the energy consumption index and punctuality index as the objectives, a train energy-saving operation optimization model is established. Then, the solution method based on the improved NSGA-II algorithm is developed. Finally, the developed optimization model is applied to Guangzhou Metro Line 7 to verify the performance. The results show that the energy consumption of the developed operation strategy is 24.4 % less than that of the minimum travel time strategy, and the total operation time meets the punctuality requirements. Meanwhile, the improved NSGA-II algorithm has less energy consumption than GA and PSO algorithms in the application of Guangzhou Metro Line 7.

Optimal decarbonization strategies for existing districts considering energy systems and retrofits

Integrated energy system planning at the district level can contribute towards the sustainable transformation of the building sector by unlocking solutions beyond individual buildings. This is particularly true for existing districts, whose older buildings have a low energy performance and for which measures to reduce the energy demand and ensure a low-emission energy supply must be implemented. In the urban context, district heating networks (DHN) are a promising way of doing the latter, especially with carbon capture and storage (CCS) on the horizon. However, investment decisions for both types of measures – energy supply and demand reduction – must consider individual buildings as part of district-scale considerations as building-level demand-side interventions affect energy demand patterns and densities. These can in turn affect energy supply decisions at the district-level.This study presents a comprehensive methodology for determining optimal decarbonization strategies for existing districts while considering building-level energy supply and retrofitting investment decisions and the expansion of existing DHN. We do so by extending the MANGOret (Multi-stAge eNerGy Optimization - retrofitting) optimization framework for the long-term investment planning of building-level multi-energy systems and envelope retrofits. In addition, the study presents an approach for modeling CCS as an emission-reduction technology for the waste incinerator supplying the marginal expansion of the district heating network.The developed optimization model considers a long-term time horizon with multiple investment stages, allowing it to represent economic and technological developments over the model time horizon. It incorporates a multi-objective perspective, capturing the trade-offs between the total system costs and emissions. The model is applied to two existing case study neighborhoods in the city of Chur, Switzerland. The long-term energy system design and operation for the two neighborhood typologies – mixed-use and residential – are analyzed as part of the DHN expansion investment decision.The results show that retrofitting is the main cost driver of any decarbonization strategy. Therefore the choice and the size of the energy systems offer better leverage for reducing emissions with a moderate increase in costs. In dense inner-city neighborhoods with high heating demands, district heating is the cost-optimal heating choice. Together with a low-emission district heating source such as a waste incinerator with CCS or biomass, it also becomes the CO2-optimal choice. Furthermore, the case studies demonstrate that a combination of heat pumps, hot water thermal storage, and solar PV is not only the CO2-optimal but also the cost-optimal decentralized heating option if no DHN is available. This highlights the attractiveness of energy-efficient heating systems already today.

A Novel Chaotic Optimization‐Oriented Model for Bridge Maintenance and Rehabilitation Planning

AbstractThe rise in percentage of deteriorated bridges coupled with the escalation of maintenance costs call for systematic bridge management systems. Therefore, this research paper introduces an exponential chaotic differential evolution (ECDE) model for optimizing bridge maintenance plans at both project and network levels. The developed optimization model is envisioned on addressing the performance, economic, social, and environmental aspects of bridge maintenance management. In addition, it exploits the use of chaotic sequences to circumvent the critical shortcomings of local minima entrapment and premature convergence encountered by classical optimization metaheuristics. The validity of the developed model is experimented using a comparative analysis of a wide set bridge elements and over a varying multi‐year maintenance plan. Comparison results reveal that ECDE‐based Sinusoidal algorithm improve the performance diagnostics of classical meta‐heuristics by values ranging from 49.2% to 73.1% over the multi‐year maintenance plans. It can be argued that the developed model could benefit transportation agencies in formulating flexible, sustainable, and cost‐effective maintenance strategies in addition to preserving the structural condition of designated bridges in the network.

Sustainable Design of Circular Reinforced Concrete Column Sections via Multi-Objective Optimization

An optimization model for reinforced concrete circular columns based on the Eurocodes is presented. With the developed optimization model, which takes into account the exact distribution of the steel reinforcement, which is not the case when designing with conventional column design charts, an optimal design for the reinforced concrete cross section is determined. The optimization model uses discrete variables, which makes the results more suitable for actual construction practice and fully exploits the structural capacity of the structure. A parametric study of the applied axial load and bending moment was performed for material cost and CO2 emissions. The results based on a single objective function show that the optimal design of the reinforced concrete column cross section obtained for the material cost objective function contains a larger cross-sectional area of concrete and a smaller area of steel compared with the optimization results when CO2 emissions are determined as the objective function. However, the optimal solution in the case where the material cost was assigned as the objective function has much more reserve in axial load capacity than in the optimal design where CO2 was chosen as the objective function. In addition, the multi-objective optimization was performed to find a set of solutions that provide the best trade-offs between the material cost and CO2 emission objectives.

Risk-Based Design Optimization of Contamination Detection Sensors in Water Distribution Systems: Application of an Improved Whale Optimization Algorithm

In the present study, the optimal placement contamination warning systems (CWSs) in water distribution systems (WDSs) was investigated. To this end, we developed a novel optimization model called WOA-SCSO, which is based on a hybrid nature-inspired algorithm that combines the whale optimization algorithm (WOA) and sand cat swarm optimization (SCSO). In the proposed hybrid algorithm, the SCSO operators help to find the global optimum solution by preventing the WOA from becoming stuck at a local optimum point. The effectiveness of the WOA-SCSO algorithm was evaluated using the CEC′20 benchmark functions, and the results showed that it outperformed other algorithms, demonstrating its competitiveness. The WOA-SCSO algorithm was finally applied to optimize the locations of CWSs in both a benchmark and a real-world WDS, in order to reduce the risk of contamination. The statistically obtained results of the model implementations on the benchmark WDS showed that the WOA-SCSO had the lowest average and standard deviation of the objective functions in 10 runs, 131,754 m3 and 0, respectively, outperforming the other algorithms. In conclusion, the results of applying the developed optimization model for the optimal placement of CWSs in the Dortmund WDS showed that the worst-case impact risk could be mitigated by 49% with the optimal placement of at least one sensor in the network. These findings suggest that the WOA-SCSO algorithm can serve as an effective optimization tool, particularly for determining the optimal placements of CWSs in WDSs.

Optimization models for the cost-effective design and operation of renewable-integrated energy systems

Novel and simple optimization methods have been developed for the cost-effective design and operation of domestic and commercial energy systems when energy consumers have access to renewable energy. The new optimization framework is formulated such that time-dependent variations in energy demand and renewable energy availability are accommodated, while unnecessary non-linearity and computational complexities are avoided. The simplicity in the optimization model with a proposed solution strategy enables the use of a spreadsheet-based solver that adequately encapsulates the impact of renewable energy integration on fossil fuel-based energy generation and predicts economic sizes of energy storage units. The developed optimization model also allows economic trade-offs between capital investment and operation costs, which determines the cost-effective operational profiles of energy supplies and its exchange through energy storage. To demonstrate the applicability and effectiveness of the proposed optimization models, case studies are conducted to identify the most cost-effective energy generation and utilization of renewable energy through a storage unit for different levels of renewable energy use; for example, up to 40% and 20% wind and solar energy contributions, respectively, are considered for summer and winter subject to constraints on the capacity or heat-to-power ratio of conventional energy generators.

Roadmap to modernization of line protection in active distribution systems

The nature of distribution systems is changing from passive to active networks due to the integration of Distributed Energy Resources, especially Inverter-Based Distributed Generation (IBDG). This change presents challenges for existing fuse-based unidirectional protection schemes, as active networks experience bidirectional flow of power and small fault current contribution from IBDGs. Such fault behavior can impact the protection scheme’s sensitivity and selectivity, compromising the reliability of active distribution networks and leading to customer outages due to improper fault isolation. The problem is exacerbated in networks that allow the islanded mode of operation. Upgrading the entire protection system to microprocessor-based relays is expensive. Instead, this work proposes a planning strategy to optimally upgrade protective devices. The proposed framework is able to incorporate IBDG integration plans, distribution system reliability targets, protection requirements, and economic viability throughout the planning horizon. The obtained strategy determines the optimal number, location, and deployment time of protective devices in consideration of network characteristics. The developed optimization model is formulated as a Mixed-Integer Linear Programming problem. The results verify the proposed framework’s ability to economically upgrade the protection scheme while enhancing the reliability of the active distribution network with IBDGs.