High Demand Scenarios Research Articles

The Decontamination of N95 Masks as an Occupational Safety Strategy

Objective: This study aimed to monitor the use of a short-wave ultraviolet (UV-C) decontamination device for N95 masks at the João de Barros Barreto University Hospital, Belém, Pará, Brazil. The research investigated the reuse of masks, storage methods, and the perception of healthcare professionals regarding the decontamination protocol using UV-C after each work shift. Theoretical Framework: The research is based on theories related to biosafety and occupational health, focusing on the importance of decontamination processes in hospital settings to enhance the protection of healthcare workers and reduce infection risks. Method: A cross-sectional study was conducted with 295 nursing professionals, including nurses, technicians, and assistants. Data were collected through a questionnaire addressing the reuse of N95 masks, storage between uses, and opinions on the use of the UV-C device for decontamination. Results and Discussion: All participants (n=295) agreed that UV-C decontamination improves biosafety by reducing the handling of potentially contaminated masks. The acceptance of the device was unanimous, confirming its feasibility both in high-demand scenarios, such as the Covid-19 pandemic, and in routine hospital practices. Research Implications: This study demonstrates that implementing UV-C decontamination systems is a practical and effective solution for hospitals, with the potential to increase occupational safety and reduce infectious waste generation. Originality/Value: The study contributes to the literature by highlighting the feasibility and benefits of reusing N95 masks with UV-C decontamination, promoting greater safety in hospital environments.

Electricity system optimisation based on python model for renewable electricity generation portfolio

100% renewable electricity scenarios have received great attention from industry and policy makers, this is due to its widespread need for technical, economical, and renewable integration action plans. In this research, a python model named WIBSS, is developed and tested for adequacy using sensitivity analysis and then used to simulate the Irish electricity system.In this investigation, the optimal cost was identified with the installed capacities needed for the electricity system for high electricity demand scenario, and the generation portfolios for the electricity system in Ireland is proposed.The electricity system parameters are determined and the levelised cost of electricity is optimised and tabulated. In this investigation, the optimal solution is identified to decrease curtailment and minimize the total cost. Overlay graphs are plotted, and the optimal renewable sources combinations are presented.This research of determining generation portfolios toward 100% renewable electricity systems demonstrates simulations of two different pathways for 2030 scenarios to meet electricity demand in Republic of Ireland. The renewable energy target appeared in the government climate action plan and the authors renewable energy target determined based on the simulation of the python model, with more than doubled photovoltaic installation, a significant reduction in offshore wind, and a substantial integration of battery capacity, the authors renewable energy target performs better than renewable energy target, the levelised cost of electricity were 57.22 €/mWh and 51.11 €/mWh respectively. The least cost pathway is extended to balance between electricity generation and electricity demand in 2040 scenario, and keys target were proposed for generation portfolios.

Explicit coordinated signal control using soft actor–critic for cycle length determination

AbstractExplicit signal coordination carries prior knowledge of traffic engineering and is widely accepted for global implementation. With the recent popularity of reinforcement learning, numerous researchers have turned to implicit signal coordination. However, these methods inevitably require learning coordination from scratch. To maximize the use of prior knowledge, this study proposes an explicit coordinated signal control (ECSC) method using a soft actor–critic for cycle length determination. This method can fundamentally solve the challenges encountered by traditional methods in determining the cycle length. Soft actor–critic was selected among various reinforcement learning methods. A single agent was administered to the arterials. An action is defined as the selection of a cycle length from among the candidates. The state is represented as a feature vector, including the cycle length and features of each leg at every intersection. The reward is defined as departures that indirectly minimize system vehicle delays. Simulation results indicate that ECSC significantly outperforms the baseline methods, as evident in system vehicle delay across nearly all demand scenarios and throughput in high demand scenarios. The ECSC revitalizes explicit signal coordination and introduces new perspectives on the application of reinforcement learning methods in signal coordination.

Long-Term Forecasting: A MAED Application for Sierra Leone’s Electricity Demand (2023–2050)

Sierra Leone is an electricity-poor country with one of the lowest electricity consumption per capita rates across sub-Saharan Africa. Yet, with ambitious targets to transform and stimulate its economy in the coming decades, energy demand forecasting becomes an integral component of successful energy planning. Through applying the MAED-D (version 2.0.0) demand software, this research study aims to generate Sierra Leone’s electricity demand forecasts from 2023 to 2050. Three novel scenarios (baseline-, high-, and low-demand) are developed based on socio-economic and technical parameters. The baseline scenario considers the current electricity sector as business-as-usual; the high-demand scenario examines an ambitious development future with increased economic diversification and mechanisation, and the low-demand scenario examines more reserved future development. The modelled scenario results project an increase in electricity demand ranging from 7.32 PJ and 12.23 PJ to 5.53 PJ for the baseline-, high-, and low-demand scenarios, respectively, by 2050. This paper provides a base set of best-available data needed to produce an electricity demand model for Sierra Leone which can be used as a capacity-building tool for in-country energy planning alongside further integration into data modelling pipelines.

Real-time energy management strategy with dynamically updating equivalence factor for through-the-road (TTR) hybrid vehicles

Through-the-road–coupled hybrid electric vehicles, comprising a forward-driving internal combustion engine (ICE) and two rear-mounted hub motors, have been attracting increasing attention for their energy efficiency and excellent road passability. However, real-time energy allocation in the ICE and motors is challenging because of complex road scenarios, nonlinear characteristics of components, and the requirement of real-time executability of energy management strategies (EMSs). This study developed an online EMS with an equivalent factor (EF) which is timely updated through the offline rule extraction and online parameter feedback based on real-time driving scenarios. Initially, the utilization of a global dynamic programming approach aims at delineating the potential switching boundary, thus substantially mitigating the computational burden on the controller. Subsequently, an innovative Model Predictive Control-based Equivalent Fuel Consumption Strategy (MPC-ECMS) is introduced. This method integrates real-time velocity prediction with State of Charge (SOC) feedback to enhance fuel efficiency. Notably, the efficiency factor (EF) within ECMS undergoes optimization through a genetic algorithm, with adaptive corrections based on driving condition recognition outcomes. Consequently, the EF is continuously adjusted to ensure convergence of actual SOC towards the reference SOC within the MPC time frame. Through rigorous Hardware in Loop experimentation, the efficacy of the proposed EMS is validated. The outcomes demonstrate its superiority over traditional charge-depleting mode–charge sustaining mode strategies, yielding a notable 13.3 % reduction in total fuel consumption during high power demand scenarios. Furthermore, the method's feasibility within embedded systems is convincingly affirmed.

Workplace Buoyancy and Servant Leadership as Catalysts for Sustainable Disaster Management: Mitigating Emotional Exhaustion in Disaster Response Teams

Amid global crises like natural disasters and climate change, the emotional well-being of disaster response workers (DRWs) is a key factor in sustainable public health and disaster management. The study highlights the issue of emotional exhaustion among DRWs, which can impact organizational effectiveness and the health and well-being of affected communities. The study examines the roles of servant leadership (SL) and workplace buoyancy (WB) in addressing these issues. Using data from 336 DRWs involved in the 2023 Kahramanmaraş earthquake, JD-R Theory is employed to study how these factors interact in high-demand scenarios. Analysis with the Smart PLS 4 program reveals that SL and WB play key roles in reducing emotional exhaustion (EE). Notably, WB partially mediates the relationship between SL and EE. These insights are vital for creating sustainable public health and disaster management strategies in times of growing conflicts and climate crises. By extending the JD-R Theory to encompass these elements, the study provides valuable suggestions for policymakers and managers to enhance the resilience and well-being of DRWs. The study deepens our understanding of public health challenges in disaster settings and highlights the need for integrated, sustainable responses to support those on the front lines of disaster relief efforts.

Freeway congestion management on multiple consecutive bottlenecks with RL‐based headway control of autonomous vehicles

AbstractAdaptive cruise control (ACC) is the core building block of future full autonomous driving. Numerous recent research demonstrated that Autonomous Vehicles (AVs) adopting shorter headways generally increase road capacity and may relieve congestion at bottlenecks for moderate demand scenarios. However, with high demand scenarios, bottlenecks can still be activated causing capacity breakdown. Therefore, extra control measures as dynamic traffic control near bottlenecks is necessary. The challenge is harder on urban freeways with consecutive bottlenecks which affect each other. This paper aims to improve the performance of ACC systems in a high demand scenario. A multi‐bottleneck dynamic headway control strategy based on deep reinforcement learning (DRL) that adapts headways to optimize traffic flow and minimize delay is proposed. The controller dynamically assigns an optimal headway for each controlled section, based on state measurement representing the current traffic conditions. The case study is a freeway stretch with three consecutive bottlenecks which is then extended to include eight bottlenecks. Three different RL agent configurations are presented and compared. It is quantitatively demonstrated that the proposed control strategy improves traffic and enhances the system delay by up to 22.30%, and 18.87% compared to shortest headway setting for the three‐bottleneck and the eight‐bottleneck networks, respectively.

Advanced E-Voting System

Advancements in technology have led to the evolution of voting systems, paving the way for sophisticated e-voting solutions. This abstract delves into the design and critical components of an advanced e-voting system. The proposed system integrates cutting-edge security measures, utilizing encryption, blockchain technology, and robust authentication protocols to ensure the integrity and confidentiality of votes cast. Accessibility remains a paramount consideration, with provisions made for inclusivity through user-friendly interfaces and support for diverse voter groups, including those with disabilities. Scalability and reliability underpin the system's architecture, ensuring seamless operation during high-demand scenarios without compromising performance. Privacy preservation is addressed through cryptographic techniques that dissociate voter identity from ballots

Reducing passenger waiting time and energy consumption for metro timetabling: A robust optimization approach

Urban subway scheduling is a quite complicated task as any small fluctuation could disturb the entire system. Subway companies need an efficient scheduling system that best serves passengers and optimizes the utilization of resources. They need, therefore, to include real-world problems in their schedule through non-deterministic modeling. A robust optimization approach is applied in real-world data to propose an optimum train schedule that minimizes passenger waiting time and maximizes electrical energy converted from kinetic energy. Three high, normal, and low travel demand scenarios are considered to optimize train running-time duration in all three interstation movement categories, namely acceleration, coasting, and braking. To find the optimal solution, a genetic algorithm (GA) model has been applied to real data from a subway line in Tehran Metro.

Joint Optimal Train Rescheduling and Passenger Flow Control for Speed Limit and High-Demand Scenarios of Urban Rail Transits

In the operation of urban rail transits, train delays occur frequently, and emergency management is one of the key factors to ensure the service quality. For speed limit scenarios with high demand, this paper takes the train running in the restricted manual (RM) mode in the speed limit zone as an example and discusses a coping method by jointing train rescheduling and passenger flow control. With the goal of maximizing the number of passengers served and minimizing total train delay, a nonlinear optimization model is constructed by taking the train operation-related requirements and passenger flow control-related indicators as constraints, and the model is reformulated to a mixed-integer programming (MIP) model with quadratic constraints, which can be solved by the Gurobi solver. In order to obtain effective solutions faster, a two-stage approach is discussed, which first obtains a rescheduling timetable and then dynamically adjusts the requirement of boarding equalization to obtain the passenger flow control scheme. The validity of the model and the solution approach are discussed with the help of numerical experiments. The results suggest that the model and solution approach are feasible. When the number of trains is fixed, the reasonable implementation of passenger flow control will help to increase the number of passengers served, and the pursuit of a higher equalization of boarding is not conducive to the number of passengers served and the full utilization of transport capacity. The two-stage approach has certain advantages over the direct computing method. The methods in this paper have a guiding value for emergency decision-making in similar delay scenarios.

Chance-constrained multi-stage stochastic energy system expansion planning with demand satisfaction flexibility

A classic multi-period stochastic energy system expansion planning (ESEP) model aims to address demand uncertainty by requiring immediate demand satisfaction for all scenarios. However, this approach may result in an expensive system that deviates from the planner’s long-term goals, especially when facing unexpectedly high demand scenarios. To address this issue, we propose a chance-constrained stochastic multi-stage ESEP model that allows for a portion of demand to remain unmet in specific periods while still ensuring complete demand satisfaction during most of the planning horizon, including the final period. This approach provides more time flexibility to build infrastructure and assess needs, ultimately reducing costs and allowing for a broader view of infrastructure planning options. To solve the chance-constrained stochastic model, we introduce a binary-search-based progressive hedging algorithm heuristic, which is particularly useful for large-scale models. We demonstrate the effectiveness and benefits of implementing the chance-constrained model through a case study of Rwanda using real-world data.

Quadrant Roadway Intersections: Tradeoffs between Control Delay Savings and Extra Travel Time

A quadrant roadway intersection (QRI) reduces congestion relative to a four-phase intersection. (Note: this study relates to traffic systems where vehicles drive on the right-hand side of the road.) It does this by removing left-turn traffic from the main intersection, resulting in a two-phase signal. Nevertheless, there is a lack of clear understanding of the tradeoffs between savings in control delay versus extra travel time experienced by the rerouted movements. This research compared the operational performance of five QRI designs with the counterpart conventional intersection (CI) under various traffic demand scenarios via TransModeler microsimulation modeling. Three measures-of-effectiveness (MOEs) were employed: time-in-system (TIS), control delay, and intersection capacity utilization. Simulation results show that all QRI designs outperform CI design for all three MOEs under all demand scenarios. QRIs with direct left-turn design have a smaller average TIS than those with loop left-turn design, indicating that savings in control delays did not offset the extra travel times. Under a relatively low demand condition, a single QRI design can generally balance the tradeoffs between control delay and extra travel time. Under a high demand scenario, a dual or full QRI with direct left-turns is preferred, since it reroutes or partially reroutes left- and right-turn traffic to secondary intersections, thus the main intersection has a lower capacity utilization and can accommodate more through-traffic demands than CI, single QRI, and dual or full QRIs with loop left-turns.

Revealing the hidden potentials of Internet of Things (IoT) - An integrated approach using agent-based modelling and system dynamics to assess sustainable supply chain performance

The Internet of Things (IoT) brings new opportunities for creating intelligent and streamlined supply chains that have better environmental and cost performance as compared to conventional ones. In this paper, we quantify such improvements for a specific logistics chain case. To support the inventory of cost and emission data, we utilize system dynamics (SD) and agent-based modeling (AB) to define the structure of the two logistical systems, simulating and estimating differences in e.g., required storage levels, efficiency of transport, etc. In particular, we assess the difference in carbon emissions, cost, and market performance of a battery delivery chain in the delivery process between a two-tier IoT-supported supply chain (users are served by an IoT retailer directly connected to the producer) and a conventional three-tier supply chain (include an additional wholesaler to connect retailer and producer). The results demonstrate that IoT supply chains have significant advantages in minimizing average product storage and shipment fluctuations. IoT suppliers can estimate market demand to adjust production and transportation strategies for new orders. Consequently, the overall profitability of the IoT supply chain increases by more than 30%. Heating and lighting emissions in the storage process and direct emissions in transportation per functional unit (one unit of a Li-ion cell module) are reduced by 60%–70% under middle- and low-demand scenarios, and by at least 50% under high-demand scenario. However, the increasing use and higher loading rates of heavy trucks will weaken the advantages of IoT. Moreover, IoT products occupies a 10% lower market share compared to conventional ones under the same pricing strategy but achieves similar market share under the same value-added strategy.

Synergistic Paths of Reduced Pollution and Carbon Emissions Based on Different Power Demands in China

Nowadays, China is faced with two strategic tasks:improving ecological environmental quality and realizing carbon neutrality and carbon peaking. Synergy to reduce pollution and carbon emissions has become an inevitable choice for the comprehensive green transition of economic and social development in China. The electric power sector will play an important role in the transition process. Based on different power demand scenarios, a multi-objective model was constructed to achieve carbon peaking and carbon neutrality at a low cost, and the optimal path scheme of carbon emission reduction synergy was obtained. The results showed that under the premise of achieving carbon peaking and carbon neutrality as scheduled, pollution reduction and carbon reduction had good synergies, and their synergistic control could effectively facilitate the realization of the low-carbon transition. Optimizing the power generation structure of the electric power sector was the key measure to achieving the synergistic effect of pollution reduction and carbon reduction. During the study period, the proportion of thermal power decreased continuously, and the proportion of clean power exceeded 92.5%. The emissions of carbon dioxide and major air pollutants were significantly different under different power demands. Carbon dioxide emissions were most affected by power demand. The peak carbon dioxide emissions under low power demand, medium power demand, and high power demand were 9.416 billion, 10.409 billion, and 10.746 billion t, respectively. The emissions of sulfur dioxide, nitrogen oxide, and particulate matter also showed an increasing trend in the low power demand, medium power demand, and high power demand scenarios. The increase in power demand only increased the pressure of power generation structure adjustment within the electric power sector, without affecting the output and activity level of other sectors, that is, the pressure of emission reduction in the electric power sector caused by power demand did not show the trend of transmission between sectors.

Reduction in water consumption in premise plumbing systems: Impacts on lead concentration under different water qualities

Water conservation measures are increasing in response to regulatory requirements addressing the need for lower environmental footprint and in response to water shortages. In households with lead service lines (LSLs), lowering consumption can adversely impact lead release as it will increase stagnation. Using a lead dissolution model and data from extensive pilot studies on excavated LSLs, the impact of adaptation to different water conservation strategies on dissolved lead contamination at the kitchen tap is assessed under three water qualities and three LSL lengths (3, 14 and 30 m) using hydraulic and water quality modelling. Consumers' behavioural variability is also assessed based on integration of EPANET and results of the stochastic water demand model SIMDEUM. Demand reduction increased the dissolved lead concentrations (Pbdiss) at the end of the LSL with mean values ranging from 28.4 to 63.3 μg/L (without corrosion control) and from 4.6 to 9.9 μg/L with corrosion control (addition of orthophosphate and pH adjustment). Adding orthophosphate (1 mg P/L) to the water reduces the mean Pbdiss values at the kitchen tap from 7.1 μg/L to 1.2 μg/L for a high water demand scenario and from 31.2 to 4.9 μg/L for a low water demand scenario. Finally, the Integrated Exposure Uptake Biokinetic (IEUBK) model is used to predict the potential blood lead levels (BLLs) for children aged 0–84 months. Results showed that the orthophosphate addition of only 1 mg P/L can significantly decrease the proportion of children with a BLL >5 μg/dL, from 82 % to 17 %, under the most extreme water conservation scenario studied, using the 90th percentile of Pbdiss concentrations during usage at kitchen tap. Wide variations of Pbdiss concentrations at the kitchen tap were calculated at times of use over a week (up to 155 μg/L in lower demand scenarios, without corrosion control) showing evident limitations of single random daytime sampling.

Cost-benefit analysis of mitigating subsidence damage in Semarang and Demak, Indonesia

Since the early 1990s, the coastal Semarang City in Indonesia has been undergoing rapid industrialization and population expansion. To meet growing water demand, groundwater is abstracted with an ever-increasing number of abstraction wells. This has led to lower groundwater tables in the largely unconsolidated substrate and this, in turn, causes land subsidence in the area. This has led to significant direct and indirect economic damage. In the context of limited public resources, this study aims to analyze the economic rationale of alternative (public) investment strategies to reduce subsidence impact in the Semarang-Demak region. With Cost-Benefit Analysis (CBA), three alternative strategies to replace groundwater abstraction wells are analyzed: (1) Installment of a piped clean water supply system with 1,000 L/s capacity, (2) a new 2 km2 coastal freshwater reservoir in West Semarang, (3) installment of a piped clean water supply system with 2,000 L/s capacity. All strategies have a positive (>1) benefit-cost ratio, which indicates that there is an economic rationale for investment to mitigate subsidence in Semarang and Demak. Under a low water demand scenario, the best strategy is strategy 1; under a high water demand scenario, the best strategy is strategy 3. As strategy 3 gives the highest economic benefit in the worst-case scenario, this is the most robust strategy.

Corporate sustainability in entrepreneurial settings: green management and operational fairness as joint drivers of venture competitiveness

PurposeConsidering the characteristics and barriers faced by small and medium enterprises (SMEs) and the need to be competitive in the current scenario of high demand for sustainability, this paper aims to propose drivers for developing corporate sustainability in SMEs based on the most influential theoretical and empirical studies and the authors' experience.Design/methodology/approachThe scientific method used in this study was content analysis of recent literature (2015–2021), specifically the 30 most cited articles on sustainability in SMEs from the Scopus database. This methodological approach has already been tested and validity by other scientific studies. From this content analysis, elements for the development of sustainability in SMEs were identified. Subsequently, in light of the authors' experience, these elements were grouped by affinity, giving rise to the drivers that aim to guide owners and managers of SMEs in the transition to sustainability.FindingsThe major finding of this paper is the proposition of drivers for the development of sustainability in SMEs. They were proposed considering the reality of these companies to be adequate to their resources and structure and to be simple to apply. Thus, these drivers guide the owners and managers of SMEs in actions that make them more prosperous, fair and green and, consequently, more competitive.Originality/valueThis work contributes theoretically to deepening the recent body of knowledge on sustainability in SMEs and fills the gap that indicates the need for accessible and flexible management solutions oriented to the development of sustainability in SMEs.

An optimisation tool for minimising fuel consumption, costs and emissions from Diesel-PV-Battery hybrid microgrids

Diesel generators (diesel gensets) are widely used within microgrid (MG) and off-grid systems for rural electrification, particularly in developing countries. The sizing and selection techniques during the MG planning stage are a key for maximising cost-effectiveness and minimising environmental impacts. This becomes more important for hybrid mini-grid systems when photovoltaic (PV) electricity generation and other renewable energies are included in the system as special attention is needed to limit the genset’s output power, to keep it within the recommended operating range. This paper presents a cost optimisation model, centred on the generatoŕs performance, within diesel/PV/battery MGs, for minimising the MG’s operating costs and environmental impact. The model considers fuel consumption equations adapted for castor oil-diesel (COD) blends and two major pollutant emissions (NOx and PM2.5), which are not considered in other optimisation models. The optimisation was implemented for high, medium, and low electricity demand scenarios, with eight possible system configurations, for the Lindi Region of Tanzania as it belongs to one of the five countries with the lowest electricity access in sub-Saharan Africa (SSA). An economic assessment was done to compare the Levelized Cost of Energy (LCOE) of the system configurations. The impact of the fuel price and pollutant emission costs on the fuel selection was investigated using sensitivity analysis. The results confirmed that for specific electricity demand each scenario requires a unique set of diesel generators and the selection is affected by the PV share and the battery energy storage (BES) units included. The best LCOE for the high, medium, and low electricity demand scenarios were 0.43£/kWh, 0.42£/kWh, and 0.45£/kWh, respectively. The sensitivity analysis revealed that the pollutant emission costs have a significant impact on LCOE for the different fuel choices whereas the variation of fuel prices has a minimal effect unless the diesel price increased by 100%.

Acuity-Based Allocation of ICU-Downstream Beds with Flexible Staffing

Intensive care units (ICUs) are crucial resources within hospitals, caring for the most critically ill patients. We propose a novel modeling framework that improves the outflow of ICU patients by anticipating unit interactions and resource sharing within the system. Across an arbitrary bipartite network of units, we consider two types of downstream staffing (baseline and flexible) and a two-stage decision process. In the first stage, we determine the level of flexible bed staffing using existing physical beds at downstream units in anticipation of incoming transfers from the ICUs. In the second stage, we determine the allocation of ICU patients to downstream beds. The goal of the model is to reduce inefficiencies and transfer delays causing ICU bed block due to lack of space in downstream units. We formulate a dynamic multiperiod model and analyze the dual of its (relaxed) stationary counterpart. Decomposing the relaxed stationary model into an ICU and downstream subproblems, we calculate the relative values of downstream beds and derive a practical acuity-based policy for the daily operational decisions. Using a large-scale simulation calibrated with historic hospital data, we demonstrate that our acuity-based policy reduces the number of long-run diverted ICU arrivals, particularly in high-demand scenarios, thus improving ICU throughput, when compared with a deterministic, a generalized randomized-most-idle, and static policies. History: Accepted by J. Paul Brooks, Area Editor for Applications in Biology, Medicine, & Healthcare. Funding: This work was partially supported by National Science Foundation [Grants CMMI-1635301/1635410/1635642]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.1267 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0133 ) at ( http://dx.doi.org/10.5281/zenodo.7194693 ).

Transmission Network Loss Reduction and Voltage Profile Improvement Using Network Restructuring and Optimal DG Placement

This paper introduced a method using hybrid combination of network restructuring and optimal placement of optimally sized distributed generators (DG) to reduce loss and improve voltage profile in a practical transmission network for scenario of high load demand for a period of ten years. A study is performed for four study cases which includes the test transmission network without considering optimal DG placement and network restructuring, considering network restructuring, optimal placement of DG units using proposed grid parameter oriented harmony search algorithm (GPOHSA) and considering hybrid combination of network restructuring and DG placement using GPOHSA. Network restructuring is achieved by addition of a new 400 kV Grid-substation (GSS) and a 220 kV GSS along with associated transmission system. GPOHSA is obtained by a modification in the conventional harmony search algorithm (HSA) where grid coordinates are used for locating the individuals in an objective space. Performance Improvement Indicators such as real power loss reduction indicator (SPLRI), reactive power loss reduction indicator (SQLRI) and summation of node voltage deviation reduction indicator (SNVDRI) are proposed to evaluate performance of each case of study. The period of investment return is assessed to evaluate the pay back period of the investments incurred in network restructuring and DG units. It is established that hybrid combination of network restructuring and DG units placement using GPOHSA is effective to meet the increased load demand for time period of ten years with reduced losses and improved voltage profile. Investment incurred on the network restructuring and DG units placement will be recovered in a time period of 4 years. Effectiveness of the GPOHSA is better relative to the conventional genetic algorithm (GA) for DG unit placement. The study is performed using the MATLAB software on a practical transmission network in India.