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The potential of organic Rankine cycle (ORC) systems is acknowledged by both considerable research and development efforts and an increasing number of applications. Most research aims at improving ORC systems through technical performance optimization of various cycle architectures and working fluids. The assessment and optimization of technical feasibility is at the core of ORC development. Nonetheless, economic feasibility is often decisive when it comes down to considering practical instalments, and therefore an increasing number of publications include an estimate of the costs of the designed ORC system. Various methods are used to estimate ORC costs but the resulting values are rarely discussed with respect to accuracy and validity. The aim of this paper is to provide insight into the methods used to estimate these costs and open the discussion about the interpretation of these results. A review of cost engineering practices shows there has been a long tradition of industrial cost estimation. Several techniques have been developed, but the expected accuracy range of the best techniques used in research varies between 10% and 30%. The quality of the estimates could be improved by establishing up-to-date correlations for the ORC industry in particular. Secondly, the rapidly growing ORC cost literature is briefly reviewed. A graph summarizing the estimated ORC investment costs displays a pattern of decreasing costs for increasing power output. Knowledge on the actual costs of real ORC modules and projects remains scarce. Finally, the investment costs of a known heat recovery ORC system are discussed and the methodologies and accuracies of several approaches are demonstrated using this case as benchmark. The best results are obtained with factorial estimation techniques such as the module costing technique, but the accuracies may diverge by up to +30%. Development of correlations and multiplication factors for ORC technology in particular is likely to improve the quality of the estimates.

Fossil fuel shortage is a major challenge worldwide. Therefore, research is currently underway to investigate potential renewable energy sources. Biodiesel is one of the major renewable energy sources that can be obtained from oils and fats by transesterification. However, biodiesel obtained from vegetable oils as feedstock is expensive. Thus, an alternative and inexpensive feedstock such as waste cooking oil (WCO) can be used as feedstock for biodiesel production. In this project, techno-economic analyses were performed on the biodiesel production in Hong Kong using WCO as a feedstock. Three different catalysts such as acid, base, and lipase were evaluated for the biodiesel production from WCO. These economic analyses were then compared to determine the most cost-effective method for the biodiesel production. The internal rate of return (IRR) sensitivity analyses on the WCO price and biodiesel price variation are performed. Acid was found to be the most cost-effective catalyst for the biodiesel production; whereas, lipase was the most expensive catalyst for biodiesel production. In the IRR sensitivity analyses, the acid catalyst can also acquire acceptable IRR despite the variation of the WCO and biodiesel prices.

Based on an adapted monetary incentive delay task, this study involved 81 participants from China, including 40 from Experiment 1 and 41 from Experiment 2, we conducted behavioral experiments across two distinct gain conditions: one with an unknown expected return and the another with a fixed expected return. Our objective was to examine the effects of investment cost and reward-punishment cues on performance in a simple cognitive task. The findings from Experiment 1 revealed that reward cues significantly promoted individual behavioral performance.In Experiment 2, a significant main effect of investment cost was observed, with individuals facing lower investment costs showing better performance and higher payoffs under equivalent reward-punishment cue conditions.

In light of the growing demand for sustainability in the construction sector and real estate market, the European Community (EC) has recently begun incentivizing renovations of private and public European buildings. This aligns with the EC’s aim to reduce harmful emissions by 55% by 2030 compared to 1990 levels, and to achieve complete decarbonization of buildings, i.e., zero harmful emissions in this sector, by 2050. Given this framework, this study aims to verify the financial viability associated with the construction of “green” buildings, as well as the associated monetary benefits related to the efficient nature of these buildings and the resulting reduction in energy consumption. Lastly, an investigation is conducted to determine the economic feasibility of energy retrofit initiatives on existing building assets by comparing the required costs to retrofit against the potential increase in market value of a retrofitted residential unit. Along with assessing the undisputed environmental advantages for the community and all building users, this research aims to assess the financial and economic feasibility of sustainable construction initiatives, providing insight into how best to pursue the EC’s aims.

This paper presents an initial investment cost analysis of public transportation systems operating with wireless charging electric vehicles (EVs). There are three different types of wireless charging systems, namely, stationary wireless charging (SWC), in which charging happens only when the vehicle is parked or idle, quasi-dynamic wireless charging (QWC), in which power is transferred when a vehicle is moving slowly or in stop-and-go mode, and dynamic wireless charging (DWC), in which power can be supplied even when the vehicle is in motion. This analysis compares the initial investment costs for these three types of charging systems for a wireless charging-based public transportation system. In particular, this analysis is focused on the energy logistics cost in transportation, which is defined as the cost of transferring and storing the energy needed to operate the transportation system. Performing this initial investment analysis is complicated, because it involves considerable tradeoffs between the costs of batteries in the EV fleet and different kinds of battery-charging infrastructure. Mathematical optimization models for each type of EV and infrastructure system are used to analyze the initial costs. The optimization methods evaluate the minimum initial investment needed to deploy the public transportation system for each type of EV charging solution. To deal with the variable cost estimates for batteries and infrastructure equipment in the current market, a cost-sensitivity analysis is performed. The goal of this analysis is to identify the market cost conditions that are most favorable for each type of wireless charging solution. Furthermore, the cost analysis quantitatively verifies the qualitative comparison of the three different wireless charging types conducted in the previous research.

This paper investigates a two-stage supply chain consisting of a battery supplier (BS) and an electric vehicle manufacturer (EVM). Considering consumers’ sensitivity to the battery driving range, the BS can improve the driving range level by investing, and obtain subsidies if the driving range level exceeds the subsidy threshold set by the government. Meanwhile, the BS may misreport his private information of the investment cost to the EVM. We mainly examine the effect of cost information misreporting and how the BS determines the optimal improvement strategy when the subsidy threshold increases. Firstly, a low (high) subsidy threshold makes the BS raise the driving range level above (below) the subsidy threshold; when the subsidy threshold is moderately raised, the choice of the improvement strategy is controlled by both the degree of raising the subsidy threshold and the technical upper limit. Secondly, participants may show different preferences for the improvement strategy under certain conditions. Finally, cost information misreporting brings adverse effects to participants and reduces the driving range level.

This study is a product of the Africa Infrastructure Country Diagnostic (AICD), a project designed to expand the world's knowledge of physical infrastructure in Africa. The AICD provides a baseline against which future improvements in infrastructure services can be measured, making it possible to monitor the results achieved from donor support. It also offers a more solid empirical foundation for prioritizing investments and designing policy reforms in the infrastructure sectors in Africa. The book draws upon a number of background papers that were prepared by World Bank staff and consultants, under the auspices of the AICD. The main findings were synthesized in a flagship report titled Africa's infrastructure: A time for transformation, published in November 2009. Meant for policy makers, that report necessarily focused on the high-level conclusions. It attracted widespread media coverage feeding directly into discussions at the 2009 African union commission heads of state summit on infrastructure.

In light of recent advancements in energy storage technology, this paper introduces a sophisticated approach to planning the locations and sizes of HV/MV substations, utilizing battery energy storage systems (BESS) to optimize peak load management. Traditional substation planning, reliant on peak load forecasts, often results in substantial investment requirements, elevated operational risks, and diminished efficiency in asset utilization. To counter these issues, the proposed method integrates BESS, aiming to reduce not only the capital and operational expenditures associated with new substations and transmission lines but also the costs associated with implementing BESS for effective peak shaving. The methodology employs a combination of heuristic techniques and an advanced weighted Voronoi diagram to establish the most effective locations and capacities for substations, alongside optimal sites and sizing for BESS installations. This innovative planning tool is designed to enhance grid reliability and cost-efficiency by balancing energy supply with demand fluctuations more effectively. Furthermore, it provides a detailed operational schedule for the BESS, optimizing the charge and discharge cycles according to varying load levels, thereby maximizing the system's operational efficiency. A comprehensive case study illustrates the practical benefits of this approach. When compared to conventional methods, the proposed strategy significantly reduces both the capital investment and operational risks, while simultaneously improving utilization efficiency. The findings demonstrate the potential of integrating BESS in substation planning to facilitate a more resilient and efficient power grid, positioning this method as a forward-thinking solution in modern energy management.

Currently, with the quick increase in global population, the energetic crisis, the environmental problematic, and the development of the power electronic devices generated the need to include new technologies for supporting and potentiating electrical distributions systems; Distribution Static Compensators (D-STATCOMs) are highly used for this task due to the advantages that this technology presents: reduction in power loss, operation costs, and chargeability of branches, among others. The possibility to include this kind of technology within the electrical system has shown the need to develop efficient methodologies from the point of view of quality solution, repeatability and processing times by considering operation and investment costs as well as the technical conditions of the electrical grids under a scenario of variable power demand and then representing the real operation of the electrical grid. With the aim to propose a solution for this requirement, this paper presents a new Discrete-Continuous Particle Swarm Optimization (DCPSO) algorithm to solve the problem of the optimal integration of D-STATCOMs into Electrical Distribution Systems (EDSs). In this case, the objective function is the minimization of annual operating costs by using a weighted mono-objective function composed of the annual power loss and the investment cost and by including all constraints associated with the operation of an EDS in a distributed reactive compensation environmentinside the mathematical formulation. In order to evaluate the effectiveness and robustness of the proposed solution method, this study implemented two tests systems (i.e., 33- and 69-bus), as well as four comparison methods, and different considerations related to the inclusion of D-STATCOMs in the EDSs. Furthermore, for evaluating the repeatability of the solution obtained by each solution methods used, each algorithm was executed 100 times in Matlab software. The results obtained demonstrated that the proposed DCPSO/HSA methodology achieved the best trade-off between solution quality and processing time, with low standard deviation values for EDSs of any size.