SAM is developed by the National Renewable Energy Laboratory (NREL) with funds from the U.S. Department of Energy. The SAM development team collaborates with industry partners, NREL staff and interns, and other research organizations develop and enhance the model. The original solar models were developed in collaboration with Sandia National Laboratories and the University of Wisconsin's Solar Energy Laboratory.
For a general description of the software, see the Welcome page.
|Janine Freeman||Project Lead, photovoltaic and wind models|
|Nicholas DiOrio||Project Lead, code architecture, storage models|
|Nate Blair||Project coordination, financials, costs, systems|
|Matthew Boyd||Concentrating (CSP) solar power models|
|Ty Neises||CSP models|
|Michael Wagner||CSP models|
|Paul Gilman||User support and documentation, user interface|
The System Advisor Model (SAM) is free software that may be used for commercial, academic, or personal purposes under the terms of the legal disclaimer below.
The first time you start SAM after installing the software, it will prompt you for an email address so we can send you a free software key. See this post on the SAM forum for details.
The System Advisor Model ("Model") is provided by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC ("Alliance") for the U.S. Department of Energy ("DOE") and may be used for any purpose whatsoever.
The names DOE/NREL/Alliance shall not be used in any representation, advertising, publicity or other manner whatsoever to endorse or promote any entity that adopts or uses the Model. DOE/NREL/Alliance shall not provide any support, consulting, training or assistance of any kind with regard to the use of the Model or any updates, revisions or new versions of the Model.
You agree to indemnify DOE/NREL/Alliance, and its affiliates, officers, agents, and employees against any claim or demand, including reasonable attorneys' fees, related to your use, reliance, or adoption of the Model for any purpose whatsoever. The Model is provided by DOE/NREL/Alliance as is and any express or implied warranties, including but not limited to the implied warranties of merchantability and fitness for a particular purpose are expressly disclaimed. In no event shall DOE/NREL/Alliance be liable for any special, indirect or consequential damages or any damages whatsoever, including but not limited to claims associated with the loss of data or profits, which may result from any action in contract, negligence or other tortious claim that arises out of or in connection with the use or performance of the Model.
We are currently working on SAM 2019 that will introduce two new models:
- Financial model for merchant plants
- New model for marine hydrokinetic (MHK) wave and tidal systems
SAM 2019 will also include many enhancements. for a complete list see the issues lists on GitHub:
The following items are from our planning discussions.
- Modernize the PVWatts model to use updated best-in-class PV modeling techniques, so that the grid integration studies and multiple external stakeholders dependent on PVWatts align with the latest and most accurate PV modeling methods.
- Implement transient models to more accurately predict production at shorter timescales, and link to the upcoming 5 minute 2-km gridded solar and wind resource data for Puerto Rico, which will be essential for studies on technical and economic potential in this region.
- Continue expanding types of systems that can be modeled in SAM containing multiple MPPT inverters or DC optimizers
- Allow for non-annual simulations (i.e. simulate any time period based on timestamp)
PV + Storage
- Add the ability to model more complex solar plus storage layouts (batteries connected to different subarrays/MPPT inputs) to allow more granular configuration of battery system behavior
- Integrate system costs, electricity rates, and compensation mechanisms into the automated dispatch strategies for solar plus storage plants, which will improve the existing automated dispatch algorithms based on energy production and electricity load, to maximize the value of the system.
- Improve battery degradation modeling by replacing user specified degradation schedules with a model accounting for battery operational conditions.
- Implement models for PV+storage systems capable of providing ancillary and resiliency services, which will help answer questions about how distributed storage can add value to the normal operation of the grid system, how real-time information and responses from those systems can provide value to the grid, how systems will react to proposed new interconnection standards, operating procedures, and equipment, and how much distributed PV+storage should be anticipated under different policies.
- Refactor the SAM SDK to be more intuitive and easier for Python users by distributing it within a Python package, making additional SAM library interfaces available, and using Python data structures and class and code conventions in the Python wrapper. This will encourage broader adoption of the SAM SDK by Python software developers, allowing other tools- such as NREL’s REopt, dGen, and reV tools- to more easily leverage the validated, detailed models available in SAM.
- Fuel cells in a PV+Battery system
- Integration with a detailed PV+Wind+Battery system design optimization tool at NREL
- Integrate an IEC 61400-15 compliant loss and uncertainty model into the wind technology/financial model in SAM
- Add a new technology model for Marine Hydrokinetic (MHK) devices, both wave and tidal
- Incorporate emerging financial mechanisms, such as limited master partnerships and valuing post-PPA power production (which is now routinely done by developers), especially in light of ITC/PTC expirations, to provide industry with a robust representation of current financing mechanisms and allow for an accurate understanding of industry growth.
- Open source community building and support (direct user support, monitor open source issues, facilitate and validate contributions)
- Technical support and stakeholder engagement (Conferences, webinars, round table discussions, SAM support forum and email, PVWatts support, documentation, websites)
- Update the utility rate models to accurately represent emerging rate structures (for example: allowing different rates with and without a PV system and changing rate structures throughout the life of a system), which continues to enable utilities, researchers, policymakers, and system owners to accurately assess the impacts of new rate structures.
- Bug fixes, financial model tweaks, updates to module and inverter component databases, updates to cost defaults
- New releases of SAM desktop tool and SDK
SAM was originally developed by the National Renewable Energy Laboratory in collaboration with Sandia National Laboratories in 2005, and at first used internally by the U.S. 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 2007 as the Solar Advisor Model Version 1, making it possible for solar energy professionals to analyze photovoltaic systems and concentrating solar power parabolic trough systems in the same modeling platform using consistent financial assumptions. Since 2007, two new versions have been released each year, adding new technologies and financing options. In 2010, the name changed to "System Advisor Model" to reflect the addition of non-solar technologies. As of the fall of 2013, NREL began releasing one new version per year with periodic updates as needed.
The DOE, NREL, and Sandia continue to use the model for program planning and in grant programs. Since the first public release, over 35,000 people representing manufacturers, project developers, academic researchers, 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 of 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 different incentive structures.