NREL Designs DER Modeling Tool
April 1, 2002
Developed by DOE's National Renewable Energy Laboratory (NREL), the Hybrid Optimization Model for Electric Renewables (HOMER) is a computer model that simulates and optimizes distributed energy resource (DER) systems. Whether you are designing a DER system for a Federal facility, investigating the cost of powering an off-grid building, or researching the potential of renewable energy for an agency project, HOMER can be a powerful tool. Its speed and simplicity allows users to perform sophisticated analyses with ease, and provides insight into the complex nature of hybrid power system design.
HOMER is designed to overcome challenges created by the wide variety of possible power system configurations, the intermittent nature of renewable energy sources, the limited availability of renewable resource data, and uncertainty in load, resource, and cost inputs. It quickly performs hourly simulations of thousands of possible system configurations accounting for the seasonal and variable nature of loads and resources. HOMER ranks the solutions by net present cost, which clearly identifies the system with the least life-cycle cost. Built-in resource data can be used when hourly data is unavailable. HOMER's sensitivity analyses demonstrate the effects of changes in the inputs.
HOMER isn't just for modeling renewable energy DER options. HOMER can model any combination of wind turbines, solar photovoltaic (PV) panels, run-of-river hydroelectric, small modular biomass, conventional generators using diesel, propane, or gasoline, and battery storage systems. HOMER has over 1,500 users in 110 countries. NREL's FEMP team has used the current version for several off-grid National Park projects: Canyonlands and Natural Bridges in Utah, Pinnacles and Alcatraz in California, and Assateague in Maryland.
To design the optimal hybrid system, HOMER needs to know about the expected electrical demand, local energy costs, the available renewable energy resources, and the cost and performance of the various components. HOMER divides inputs into four categories:
- load inputs—the system's electrical and thermal loads,
- resource inputs—the available renewable energy resources (using monthly or hourly data) and the price and characteristics of fossil fuels,
- component inputs—the cost and performance of the power system components, and
- optimization inputs—the allowable size range for each system component and various constraints on the power system.
HOMER also considers the local cost of fuel and the cost of grid extensions when evaluating options. Multiple values can be specified for most variables when the data is uncertain or the user is interested in a potentially wide range of applications. HOMER performs its optimization procedure for each sensitivity case, or combination of values.
HOMER provides three levels of outputs. The results of a particular system simulation include:
- summary results like capital cost, net present cost, annual energy production, and fuel usage, as well as hourly data like power production or battery state of charge
- optimization results, which rank all of the different systems simulated for a particular sensitivity case according to net present cost; and
- sensitivity outputs that show the effects of changes in sensitivity variables, in tabular or graphic format.
The next generation of HOMER, currently undergoing beta testing and scheduled for release in fall 2002, will be able to analyze grid-connected DER applications, including combined heat and power (cogeneration) systems. The range of technologies will also be broadened to include fuel cells, microturbines, and biogasification systems. A pre-release version is currently being tested.
For more information about HOMER, or to download a free copy, please see www.nrel.gov/international/tools/homer/homer.html. Comments, questions, and requests for training can be directed to Peter Lilienthal of NREL at firstname.lastname@example.org.
In Figure 1, an example of a sensitivity output, the optimal system type is shown as a function of diesel fuel price and wind speed. Wind power is competitive at the higher wind speeds, although the critical wind speed is dependent on the fuel price. PV is competitive only at low wind speeds and high fuel prices.
Figure 2 shows the results of an analysis considering wind turbines, diesel generators, and natural gas-fueled microturbines for an institutional user of electrical and thermal energy. Microturbines have higher capital cost and slightly lower electrical efficiency than diesel generators, but their waste heat is easily captured for use in industrial processes. The graph shows that microturbines are preferable to diesels if there is sufficient thermal load, and that wind turbines make sense at sufficiently high wind speeds.