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The System Advisor Model SAM is a performance and financial model designed to facilitate decision making for people involved in the renewable energy industry:. Download a published description of SAM SAM makes performance predictions and cost of energy simulator kostenlos binare optionen template for grid-connected power projects based on installation and operating costs and system design parameters that you specify as inputs to the model.

Projects can be either on the customer simulator kostenlos binare optionen template of the utility meter, buying and selling electricity at retail rates, or on the utility side of the meter, selling electricity at a price negotiated through a power purchase agreement PPA. The first step in simulator kostenlos binare optionen template a SAM file is to choose a technology and financing option for your project. SAM automatically populates input variables with a set of default values for the type of project.

It is your responsibility as an analyst to review and modify all of the input data as appropriate for each analysis. Next, you provide information about a project's location, the type of equipment in the system, the cost of installing and operating the system, and financial and incentives assumptions.

Each performance model represents a part of the system, and each financial model represents a project's financial structure. The models require input data to describe the performance characteristics of physical equipment in the system and project costs. SAM's user interface makes it possible for people with no experience developing computer models to build a model of a renewable energy project, and to make cost and performance projections based on model results.

To describe the renewable energy resource and weather conditions at a project location, SAM requires a weather data file.

Depending on the kind of system you are modeling, you either choose a weather data file from a list, download one from the Internet, or create the file using your own data. SAM includes several libraries of performance data and coefficients that describe the characteristics of system components such as photovoltaic modules and inverters, parabolic trough receivers and collectors, wind turbines, and biopower combustion systems.

For those components, you simply choose an option from a list, and SAM applies values from the library to the input variables. SAM can automatically download data and populate input variable values from the following online databases:. For the remaining input variables, you either use the default value or simulator kostenlos binare optionen template its value. Some examples of input variables are:. Once you are satisfied with the input variable values, you run simulations, and then examine results.

A typical analysis involves running simulations, examining results, revising inputs, and repeating that process until you understand and have confidence in the results. SAM's performance models make hour-by-hour calculations of a power system's electric output, generating a set of 8, hourly values that represent the system's electricity production over a single year.

You can explore the system's performance characteristics in detail by viewing tables and graphs of the hourly and monthly performance data, or use performance metrics such as the system's total annual output and capacity factor for more general performance evaluations. You can compare different kinds of projects by creating more than one case in a file. For example, you can compare the savings of a residential rooftop solar water heater to those of a photovoltaic system, or for a large utility-scale project, compare the simulator kostenlos binare optionen template purchase price that would be required to make a wind, photovoltaic, and concentrating solar power project profitable at a given location.

SAM does not model hybrid power systems, so, for example you cannot model a single project that combines wind turbines and photovoltaic modules. SAM's financial model calculates financial metrics for various kinds of power projects based on a project's cash flows over an analysis period that you specify. The financial model uses the system's electrical output calculated by the performance model to calculate the series of annual cash flows.

It also includes a simple levelized cost of energy calculator based either on a fixed charge rate input. Residential and commercial projects are financed through either a loan or cash payment, and simulator kostenlos binare optionen template investment costs through savings from reduced electricity purchases from the electricity service provider.

For electricity pricing, SAM can model simple flat buy and sell rates, monthly net metering, or complex rate structures with tiered time-of-use pricing. For these projects, SAM reports the following metrics:. Utility and commercial PPA projects are assumed to sell electricity through a power purchase agreement at a fixed price with optional annual escalation and simulator kostenlos binare optionen template TOD factors.

For these projects, SAM calculates:. SAM can either calculate the internal rate of return based on a power price you specify, or calculate the power price based on the rate of return you specify.

SAM calculates financial metrics from project annual cash flows representing the value of energy savings for projects using retail electricity rates, and the value of revenue from electricity sales for projects selling electricity under a power purchase agreement.

For the PPA partnership models, SAM simulator kostenlos binare optionen template cash flows from the project perspective and from the perspective of each partner.

In addition to simulating a system's performance over a single year and calculating a project cash flow over a multi-year period, SAM's analysis options make it possible to conduct studies involving multiple simulations, linking SAM inputs to a Microsoft Excel workbook, and working with custom simulation modules.

The following options are for analyses that investigate impacts of variations and uncertainty in assumptions about weather, performance, cost, and financial parameters on model results:. Department of Energy's Solar Energy Technologies Program for systems-based analysis of solar technology improvement opportunities within the program. The first public version was released in August as Version 1, making it possible for solar energy professionals to analyze photovoltaic systems and concentrating solar power parabolic trough systems simulator kostenlos binare optionen template the same modeling platform using consistent financial assumptions.

Sincetwo new versions have been released each year, adding new technologies and financing options. Inthe name changed to "System Advisor Model" to reflect the addition of non-solar technologies.

Since the first public release, over 35, people representing manufacturers, project developers, academic simulator kostenlos binare optionen template, and policy makers have downloaded the software.

Manufacturers are using the model to evaluate the impact of efficiency improvements or cost reductions in their products on the cost simulator kostenlos binare optionen template energy from installed systems. Project developers use SAM to evaluate different system configurations to maximize earnings from electricity sales.

Policy makers and designers use the model to experiment with simulator kostenlos binare optionen template incentive structures. It requires about MB of storage space on your computer. SAM is available for free download. The following resources are available for learning to use SAM and for getting simulator kostenlos binare optionen template with your analyses:. SAM consists of a user interface, calculation engine, and programming interface.

The user interface is the part of SAM that you see, and provides access to input variables and simulation controls, and displays tables and graphs of results. SAM's calculation engine performs a time-step-by-time-step simulation of a power system's performance, and a set of annual financial calculations to generate a project cash flow and financial metrics.

The programming interface allows external programs to interact with SAM. Each renewable energy technology in SAM has a corresponding performance model that performs calculations specific to the technology. Similarly, each financing option in SAM is also associated with a particular financial model with its own set simulator kostenlos binare optionen template inputs and outputs.

The financial models are as independent as possible from the performance models to allow for consistency in financial calculations across the different technologies. A performance simulation consists of a series of many calculations to emulate the performance of the system over a one year period in time steps of one hour for most simulations, and shorter time steps for some technologies.

NREL is a national laboratory of the U. Skip to main content. The System Advisor Model SAM is a performance and financial model designed to facilitate decision making for people involved in the renewable energy industry: Project simulator kostenlos binare optionen template and engineers Policy analysts Technology developers Researchers. SAM's results summary table and graphs. The results data table showing inverter losses for simulator kostenlos binare optionen template photovoltaic system.

SAM displays simulation results in tables and graphs, ranging from the metrics table displaying the project's net present value, first year annual production, and other single-value metrics, to the detailed annual cash flow and hourly performance data that can be viewed in tabular or graphical form.

A built-in graphing tool displays a set of simulator kostenlos binare optionen template graphs and allows for creation of custom graphs.

All graphs and tables can be exported in various formats for inclusion in reports and presentations, and also for further analysis with spreadsheet or other software. Time series graph showing hourly results for a MW parabolic trough system with 6 hours of storage. The first several rows of the cash flow table for a utility-scale project. SAM's scripting language LK allows you to write your own scripts within the SAM user interface to control simulations, change values of input variables, and write data to text files.

SAM macros are LK scripts that come with the software and can be run with no knowledge of scripting. SAM macros include a weather file checker, multiple subsystems, photovoltaic system sizing assistant, and tornado plot generator. SAM also makes it possible to work with external models developed in Excel with Excel Exchange, which allows Excel to calculate the value of input variables, and automatically pass values of input variables between SAM and Excel.

SAM's help system includes detailed descriptions of the user interface, modeling options, and results.

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January 09, ; Accepted Date: February 22, ; Published Date: J Appl Computat Math 3: This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Three contents for the pricing of bond options on the arbitrage-free model with jump are included in this paper.

There is a substantial difference between bond option prices which are obtained by the HW model with jump and the HJM model based on jump. Through the empirical simulation of our method suggested, we obtain a better accurate estimation for the pricing of bond options.

In pricing and hedging with financial derivatives, term structure models with jump are particularly important [ 1 ], since ignoring jumps in financial prices may cause inaccurate pricing and hedging rates [ 2 ]. Solutions of term structure model under jump-diffusion processes are justified because of movements in interest rates displaying both continuous and discontinuous behaviors [ 3 ]. Moreover, to explain term structure movements used in the latent factor models, it means how macro variables affect bond prices and the dynamics of the yield curves [ 4 ].

Current research using jump-diffusion processes relies mostly on two classes of models: In this paper, we show the actual proof analysis of the HJM model based on jump easily under the extended restrictive condition of Ritchken and Sankarasubramanian RS [ 7 ]. By beginning with certain forward rate volatility processes, it is possible to obtain classes of interest models under HJM model based on jump that closely resembles the traditional models [ 8 ].

Finally, we confirm that there is a substantial difference between bond option prices which are obtained by HW model with jump and HJM model based on jump through the empirical computer simulation which used MCS, which is used by many financial engineers to place a value on financial derivatives.

For this, we use the well-known MSE. We make sure that lower value of PCS in the proposed models corresponds to sharper estimates [ 9 ].

These results mean an accurate estimate in the empirical computer. The structure of the remainder of this paper is as follows. In section 4, investigate the pricing of bond on arbitrage-free models with jump. In section 4, the pricing of bond option on arbitrage-free models with jump are presented.

Section 6, explains the simulation procedure of the proposed models using MCS. Finally, Section 8 concludes this paper. All our models will be set up in a given complete probability space and an argument filtration generated by an Winear process and N t represents a Poisson process with intensity rate h and the total number of extreme shocks that occur in a financial market until time t [ 10 ]. In the same way that a model for the asset price is proposed as a lognormal random walk, let us suppose that the interest rate r and the forward rate f are governed by a SDE of the form.

When interest rates follow the SDE 1 , a bond has a price of the form V t; T ; the dependence on T will only be made explicit when necessary. To get the bond pricingequation with jump, we set up a riskless portfolio containing two bonds with different maturities T1 and T2. Hence, we derive the partial differential equation PDE for bond pricing. If r satisfies SDE 1 , then the zero-coupon bondpricing equation with jumps is.

Boundary conditions depend on the form of u r, t and w r, t. We now consider a quite different type of random environment. In this paper, we extend jump-diffusion version of equilibrium single factor model to reflect this time dependence. This leads to the following model for r t:.

Under the process specified in equation 5 , r t is defined as:. It can be shown that the probability density function for r t. Therefore, the conditional expectation and variance of jump-diffusion process given the current level are.

Thus, we get the partial differential difference bond pricing equation:. Bond price derivatives can be calculated from 4 , and then the substitution of these derivatives into 9. Thus, equating powers of r t yields the following equations for A and B. Then the price at time t of a discount bond with maturity T, is defined as.

Where is a standard Wiener process generated by the risk-neutral measure Q, and dN t is the Poisson process with intensity rate h. In similar way as before, therefore, the conditional expectation and variance of the SDE 15 given the current level are. In the study, we use the relation between short rate and forward rate process to obtain the formula of bond price under the extended restrictive condition of RS. Let be the jump-diffusion process in short rate r t is the equation 5.

Let be the volatility form is. We know the SDE for forward rate Then we obtain theequivalent model is. By the theorem 4, we derive the relation between short rate and forward rate. Using the equation 19 , we obtain bond pricing equation as follows;. Then discount bond price V t, T for forward rate is given by. We derive a CFS for bond options when the prices of the underlying instantaneous interest and forward rate evolve as discontinuous processes. We now consider the value of European options on discount bond equations 4 and Thus, the equation 21 becomes.

In similar way, we obtain the price of a put option on the discount bond. In this section, we explain about the simulation procedure ofthe pricing of bond options on the arbitrage-free models withjump. The MCS is actually a very general tool and its applicationsare by no means restricted to numerical integration. For small time steps, wecan obtain the bond price by sampling n short and forwardrates paths under the discrete version of the risk-adjusted SDEs 5 and The bond price estimate is given by:.

The option price is evaluated using formula 21 , using the Euler-Maruyama scheme for the integration, as.

Lower values of PCS in equation 25 correspond to sharper estimates. In this section, we investigate the pricing of bond options on the arbitrage-free models with jump. Tables 1 and 2 represent the pricing of bond options on arbitrage-free models using the MCS.

We nowinvestigating the pricing of bond options on the HJM model based on jump which is shown in Figure 2 and Table 2. For this experiment, the parameter values are assumed as before. In empirical computer simulation Tables 1 and 2 , we show that the lower values of PCS in the proposed models correspond to sharper estimates using the Mathematica [ 11 ]. After investigating the models which allow the short term interest and the forward rate following a jump-diffusion process, we obtained the closed-form solutions on jump models, which are more useful to evaluate the accurate estimate for the values of bond options in the financial market.

Through the MCS simulation of these solutions with jump, the price of the expected stable figure like right-downward flow as maturity increases while the graph of bond options on the HW-Jump model with the short term interest rate is humped. We need further investigation on this difference which can be caused by performing jump term simulation of different interest rate cases.

Also, we obtained the more accurate estimate in empirical computing by showing the fact that the PCS for the HJM based on jump is lower than that for the HW model withjump. There are still problems remained for further research. Some of them, for instance, are i using the MCS to simulate more complicated two factors of the proposed models; ii considering a dynamic algorithm to predict the bond option prices using actual data set of bond. Home Publications Conferences Register Contact.

Research Article Open Access. February 25, Citation: The pricing of bond options on the HW model with jump. The pricing of bond options on the HJM model based on jump. Select your language of interest to view the total content in your interested language.

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