The Distributed Energy Resources Customer Adoption Model (DER-CAM)
The Distributed Energy Resources Customer Adoption Model (DER-CAM) is a powerful and comprehensive decision support tool that primarily serves the purpose of finding optimal distributed energy resource (DER) investments in the context of either buildings or multi-energy microgrids.
Technically mature and extensively peer-reviewed, DER-CAM has been developed by researchers at Lawrence Berkeley National Laboratory (Berkeley Lab) since 2000, and can be used to find the optimal portfolio, sizing, placement, and dispatch of a wide range of DER, while co-optimizing multiple stacked value streams that include load shifting, peak shaving, power export agreements, or participation in ancillary service markets.
While the objective function of DER-CAM can be easily modified — or even replaced by a multi-objective analysis — it is most commonly defined as a site's total annual cost of energy supply. This includes costs associated with both new and existing DER, operation and maintenance costs, fuel costs, and also all costs related to utility imports either fixed, time-dependent, energy-based, or power-based. Additionally, all value streams associated with the optimal DER dispatch determined by DER-CAM are considered in the objective function, both in the form of avoided costs and market participation.
For a more in-depth review on different revenue streams associated with microgrid deployment we suggest the paper found on eta.lbl.gov/publications/value-streams-microgrids-literature.
Access to DER-CAM
DER-CAM is publicly available and free to use. To use DER-CAM on your local machine, please register and install the user interface application DER-CAM Desktop.
DER-CAM answers several important questions related to optimal DER solutions for microgrids:
- What is the optimal portfolio of DER that meet the specific needs of this microgrid?
- What is the ideal installed capacity of these technologies to minimize costs?
- How should the installed capacity be operated so as to minimize the total customer energy bill?
- Where in the microgrid should distributed energy resources be installed and how should they be operated to ensure voltage stability?
- What is the optimal DER solution that minimizes costs while ensuring resiliency targets?
Advanced Mathematical Modeling Techniques
DER-CAM uses advanced mathematical modeling techniques to formulate the optimal multi-energy microgrid design problem as a mixed-integer linear program (MILP). Unlike simulation-based models or optimization models based on heuristic and non-linear formulations, DER-CAM can quickly find globally optimal solutions to this highly complex problem. The key challenge lies in developing and implementing linear formulations that adequately represent different non-linear phenomena, and DER-CAM achieves this using a wide range of techniques.
Examples of advanced modeling solutions implemented in DER-CAM include linearized AC and DC optimal power flow (OPF) algorithms, or multiple piece-wise approximations of non-linear efficiency curves. A list of publications describing the modeling techniques implemented in DER-CAM is available at building-microgrid.lbl.gov/publications.
Supported by its optimization algorithm and by a public facing graphical user interface, DER-CAM enables users across the world to answer a multitude of questions in the context of distributed energy resources and microgrids.
A typical example consists of using DER-CAM to find which DG and/or CHP technologies a site-owner should deploy and how they should be operated in order maximize the economic benefit. These answers are based on specific site loads, weather data, utility data, and price and performance data for different DER options.
- The site's hourly end-use load profiles for a typical year (electric, cooling, refrigeration, space heating, hot water, and natural gas loads)
- The site's default electricity tariff, natural gas prices, and other relevant price data
- Capital, operating and maintenance (O&M), and fuel costs of various available technologies, together with the interest rate on customer investment
- Basic physical characteristics of alternative generating, heat recovery and cooling technologies, including the thermal-electric ratio that determines how much residual heat is available as a function of generator electric output
- Information on the site's topology and distributed heating infrastructure (only for multi-node models)
- Optimal selection and capacity of DER to be installed
- Optimal placement of DER inside the microgrid (for multi-node models)
- When and how the available DER should be dispatched (both to maximize economic performance and meet resiliency and reliability targets)
- Detailed cost breakdown of supplying end-use loads
- Detailed breakdown of carbon emissions associated with supplying end-use loads
- Customer decisions are made based either on economic or environmental criteria, but economic constraints always apply. In other words, even when optimizing towards carbon emissions, meeting user-defined payback constraints is still required.
- No deterioration in output or efficiency during the lifetime of the equipment is considered. Furthermore, start-up and other ramping constraints are only included when finding security-constrained microgrid designs.
- Reliability and power quality benefits for multiple units of the same technology are not directly taken into account.
- Possible reliability or power quality improvements accruing to customers are only considered in terms of avoided interruption costs.
Browse through the presentation linked below to learn about the DER-CAM workflow:
The Investment & Planning DER-CAM uses up to three representative day-types per month to describe hourly energy loads: week-day, weekend-day, and peak-day. This spreadsheet tool was developed to support users in the process of converting loads to the DER-CAM format. It can take data in 15-min, 30-min, and 1-hour time steps as input and process loads to the appropriate format. Week-day and weekend-day loads are obtained by calculating average values, and peak-day loads can be obtained either by filtering the maximum observed loads or a user-specified load percentile. Once data has been processed in this spreadsheet it can be easily exported to DER-CAM using standard copy-paste procedures.
Note the following link will automatically download: DER-CAM Data Processing Template
Learn more about how to use the Distributed Energy Resources Customer Adoption Model, or DER-CAM, a design and analysis tool for microgrids from Lawrence Berkeley National Laboratory (Berkeley Lab).