Berkeley Lab has been developing the Distributed Energy Resources Customer Adoption Model (DER-CAM) for several years. Given load curves for energy services requirements in a building microgrid (µ⋅grid), fuel costs and other economic inputs, and a menu of available technologies, DER-CAM finds the optimum equipment fleet and operating schedule. This capability is being applied using a software as a service (SaaS) model. The evolution of this approach is demonstrated by description of four past and present projects: (1) a public access web site focused on solar photovoltaic generation and battery viability for large non-residential customers, (2) a building CO2 emissions reduction operations problem for a university dining hall with potential investments considered, (3) a battery selection problem and a rolling operating schedule problem for a large County jail, and (4) the direct control of the solar-assisted heating ventilation and air conditioning system of a university building by providing optimised daily schedules that are automatically implemented in the building’s energy management and control system. Together these examples show that optimisation of building µ⋅grid design and operation can be effectively achieved using SaaS.
BT - Institution of Mechanical Engineers Journal of Power and Energy C2 - LBNL-6302E DA - 02/2013 IS - Special Issue N2 -Berkeley Lab has been developing the Distributed Energy Resources Customer Adoption Model (DER-CAM) for several years. Given load curves for energy services requirements in a building microgrid (µ⋅grid), fuel costs and other economic inputs, and a menu of available technologies, DER-CAM finds the optimum equipment fleet and operating schedule. This capability is being applied using a software as a service (SaaS) model. The evolution of this approach is demonstrated by description of four past and present projects: (1) a public access web site focused on solar photovoltaic generation and battery viability for large non-residential customers, (2) a building CO2 emissions reduction operations problem for a university dining hall with potential investments considered, (3) a battery selection problem and a rolling operating schedule problem for a large County jail, and (4) the direct control of the solar-assisted heating ventilation and air conditioning system of a university building by providing optimised daily schedules that are automatically implemented in the building’s energy management and control system. Together these examples show that optimisation of building µ⋅grid design and operation can be effectively achieved using SaaS.
PY - 2013 T2 - Institution of Mechanical Engineers Journal of Power and Energy TI - Applications of Optimal Building Energy System Selection and Operation ER -