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The number of public-private partnership (PPP) projects has gone up especially in developing countries. The risk assessment of PPP projects is essential in ensuring project success. The objective of this study is to develop an Analytic Network Process (ANP) based risk assessment model for hydropower investments, and a tool to facilitate quantification of risk ratings based on this model. The results show that the three most important risk factors that affect the overall risk rating of a PPP hydropower investment are legal risks, contractor/subcontractor risks, and operator risks. In addition, the three most important risk clusters were identified as stakeholders, government requirements, and resources, whereas market was the least important cluster. The tool that measures the risk rating of a PPP of hydropower project was tested on ten real cases, and satisfactory results were obtained in terms of its predictive capability. The contributions of this research include (1) identification of the risk factors and clusters of factors associated with PPP hydropower investments; (2) determination of the priority of each risk factor and cluster; (3) development a tool that guides the investors through the risk assessment of PPP hydropower investments. [ABSTRACT FROM AUTHOR]

During the feasibility study of BOT (Build-Operate-Transfer) hydropower investments, the selling price of energy is the most critical parameter that impacts the net present value (NPV) estimated by the investors. Investors usually consider the price of energy guaranteed by the government during their feasibility studies which is the worst case scenario. However, it is apparent that negotiations that take place between investor and broker determine the price of energy which is affected by various sources of uncertainty associated with the energy demand and country conditions. The objective of this study was to make a realistic estimate of the investor's selling price by modeling the negotiation process between investor and broker using a multi-agent system (MAS). Thus, the factors affecting the negotiation process were identified, a negotiation protocol between the parties was set up, negotiation scenarios were determined, and modelled by using a MAS. The model was tested on a hydropower investment in Turkey and generated more realistic results compared to the current practice. Investors and brokers may benefit from this study because it considers the potential changes in the market as well as the negotiating postures of parties under different scenarios. [ABSTRACT FROM AUTHOR]

Energy demand has been increasing, but traditional sources of energy are depletable. New investments are needed in renewable energy production. Hydroelectric power plants are often considered a feasible renewable source of energy and are often organized as a public private partnerships (PPP). However, risk factors stemming from the macro environment as well as project conditions should be considered in performing feasibility studies. The objective of this study was to develop a method that can be used to predict the profitability of hydropower investments considering the relevant risk factors. To that end, a cash flow that represents the construction and operation period is set up, the risk factors involved in such projects are identified, the impacts of these risk factors on the cash flow parameters are assessed, and Monte Carlo simulation is performed to estimate the net present value (NPV) of a hydropower investment. The proposed method was tested in a hydropower investment located in Turkey and generated credible results that could be of great benefit to potential investors operating in similar conditions. The primary contribution of this research is the creation of a method that allows investors to assess the profitability of a hydropower investment by using a stochastic approach. [ABSTRACT FROM PUBLISHER]

Accessing the required information in the supply chain of structural steel components is critical for minimizing costly reworks and delays. This paper identifies the information items generated in the different phases of the supply chain related to structural steel components and formalizes the process of producing and using this information. Precise details about different features of the components (e.g., their geometry and weight, connection details, cutting/bending/ punching requirements, and the type and grade of the material) are set in the various tasks performed in the different phases of the supply chain. Regardless of whether one uses paper-based systems or advanced technologies such as smart tags and radio-frequency identification (RFID), a better understanding is achieved of the processes through which a structural steel component passes. The results of this research can be used to streamline the information flow in the supply chain of structural steel components, regardless of the type of tracking technology used, hence reducing delays and reworks. [ABSTRACT FROM AUTHOR]

This paper presents a method that guides designers in obtaining the desired number of earned points in the "Optimize Energy Performance" credit of the "Energy and Atmosphere" category of LEED version 4 (v4) certification at minimum cost. The model creates different scenarios, identifies the LEED points and costs for each scenario. The energy analysis calculations are performed by Sefaira, the quantities of materials are received from Autodesk Revit, and the cost information comes from the RSMeans Database. A macro in Excel automates the process. An office building was used as a case study to illustrate the applicability of the proposed method. The minimum cost necessary to achieve any number of points in the "Optimize Energy Performance" credit were calculated, such as a minimum cost of $842,500 to obtain 16 points, and $476,684 for 5 points. The primary contributions of this research include (1) the development of a tool that allows designers to pick the most economical alternative for the desired points in the "Optimize Energy Performance" credit, and (2) the first time integrated use of an energy simulation software (Sefaira), a cost database (RSMeans), and a BIM software (Autodesk Revit). [ABSTRACT FROM AUTHOR]

Investments in renewable energy resources have become inevitable due to increasing energy demand and energy prices, diminishing non-renewable energy resources, and the outgrowth of carbon footprints. Photovoltaic (PV) systems offer high solar energy potential in sustainable energy production whereas their high initial costs necessitate critical strategic valuation of investments. Valuation with conventional methods has been challenging due to existence of uncertainties such as fluctuating PV panel prices, changing meteorological conditions with certain effects on power generation, and governmental policies on energy market regulations. This study aims to propose a real options approach to valuation of residential rooftop PV system investments considering these uncertainties and demonstrate benefits of this approach with an application on the residential PV investment decisions in Turkey. The proposed method, Real Options Valuation (ROV) with Least-Squares Monte Carlo Simulation (LSMC) considers the deferral option of the investor by utilizing stochastic simulations, the discounted cash flow method, linear regression, and backward dynamic programming and thus evaluates the effects of uncertainties on financial attractiveness of residential PV investments. The case study findings proved that ROV with LSMC having a 7-years deferral option supported the investment decision with realizable cost-effective options while "NPV method" resulted in an infeasible investment. Scenario analysis was also conducted to explore policy options that can be used to promote solar energy investments in Turkey. This study has a potential to have practical contributions for investors as well as implications for policy-makers. [Display omitted] • Considering strategic value of flexibility for investment time with deferral option. • Handling uncertainties using stochastic simulations. • ROV method can prevent faulty rejection of investments with classical methods. [ABSTRACT FROM AUTHOR]