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Cardoso, Goncalo

Publications and source records attributed to Cardoso, Goncalo.

9 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Remote Off-grid Microgrid Design Support Tool (ROMDST) - An Optimal Design Support Tool for Remote, Resilient, and Reliable Microgrids (Phase II, Final Report)

The objective of the project is to develop an advanced optimization-based design support tool for AC or DC microgrids in remote locations, where utility grids may not be accessible. The mathematical model and the interface are being developed such that multiple design objectives and criteria/constraints can be easily enabled or disabled, to deliver a flexible tool relevant to a large, diverse user base, and to facilitate future feature developments. The new tool delivered by this project leverages the team’s extensive expertise in the development, testing, deployment, and commercialization of the state-of-the-art Distributed Energy Resources Customer Adoption Model (DER-CAM) – which is the foundation for the new tool. Phase I of the project leverages the microgrid design and decision support tool DER-CAM. Key Phase I developments include the following adaption to the DER-CAM model: a) AC or DC microgrid architectures, b) multiple economic objectives (e.g. single customer vs community vs utility perspective), c) constraints on fuel availability (e.g. total fuel available during outage periods), d) active and reactive power flow constraints for normal and N-1 contingency cases, e) component part load efficiencies (e.g. power electronic devices), and f) interactive data visualization capabilities (e.g. design topology elements). A component library including components’ default technical and economical characteristics was also developed. In Phase II, the project is focused on tool testing and validation, use for real-world microgrid designs, and outreach. Fully developing this tool will have the following impacts: 1) optimal off-grid microgrid designs that replace existing back of the envelope or non-optimal calculations, reducing capital costs and risk of microgrid deployment; 2) removing barriers to microgrid assessments by lowering microgrid soft costs, as the tool is freely usable; 3) reliable and resilient designs that reduce the cost of critical load shedding due to component outages.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Distributed Energy Resources Customer Adoption Model (DER-CAM) v5.9

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. Version 5.9 introduces significant advancements to DER-CAM, including support for wholesale market integration (ancillary services), storage aging effects, improved solar thermal collector modeling, improved part-load efficiency modeling for conventional generators, added support for hydro generation, among others.

Deforest, Nicholas↗
DOE OSTI2018

Remote Off-grid Microgrid Design Support Tool (ROMDST) - An Optimal Design Support Tool for Remote, Resilient, and Reliable Microgrids Phase II - Final Report

The objective of the project is to develop an advanced optimization-based design support tool for AC or DC microgrids in remote locations, where utility grids may not be accessible. The mathematical model and the interface are being developed such that multiple design objectives and criteria/constraints can be easily enabled or disabled, to deliver a flexible tool relevant to a large, diverse user base, and to facilitate future feature developments. The new tool delivered by this project leverages the team’s extensive expertise in the development, testing, deployment, and commercialization of the state-of-the-art Distributed Energy Resources Customer Adoption Model (DER-CAM) – which is the foundation for the new tool. Phase I of the project leverages the microgrid design and decision support tool DER-CAM. Key Phase I developments include the following adaption to the DER-CAM model: a) AC or DC microgrid architectures, b) multiple economic objectives (e.g. single customer vs community vs utility perspective), c) constraints on fuel availability (e.g. total fuel available during outage periods), d) active and reactive power flow constraints for normal and N-1 contingency cases, e) component part load efficiencies (e.g. power electronic devices), and f) interactive data visualization capabilities (e.g. design topology elements). A component library including components’ default technical and economical characteristics was also developed. In Phase II, the project is focused on tool testing and validation, use for real-world microgrid designs, and outreach. Fully developing this tool will have the following impacts: 1) optimal off-grid microgrid designs that replace existing back of the envelope or non-optimal calculations, reducing capital costs and risk of microgrid deployment; 2) removing barriers to microgrid assessments by lowering microgrid soft costs, as the tool is freely usable; 3) reliable and resilient designs that reduce the cost of critical load shedding due to component outages.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

The impact of ancillary services in optimal DER investment decisions

Microgrid resource sizing problems typically include the analysis of a combination of value streams such as peak shaving, load shifting, or load scheduling, which support the economic feasibility of the microgrid deployment. However, microgrid benefits can go beyond these, and the ability to provide ancillary grid services such as frequency regulation or spinning and non-spinning reserves is well known, despite typically not being considered in resource sizing problems. This paper proposes the expansion of the Distributed Energy Resources Customer Adoption Model (DER-CAM), a state-of-the-art microgrid resource sizing model, to include revenue streams resulting from the participation in ancillary service markets. Results suggest that participation in such markets may not only influence the optimum resource sizing, but also the operational dispatch, with results being strongly influenced by the exact market requirements and clearing prices.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

Optimal investment and scheduling of distributed energy resources with uncertainty in electric vehicles driving schedules

The large scale penetration of electric vehicles (EVs) will introduce technical challenges to the distribution grid, but also carries the potential for vehicle-to-grid services. Namely, if available in large enough numbers, EVs can be used as a distributed energy resource (DER) and their presence can influence optimal DER investment and scheduling decisions in microgrids. In this work, a novel EV fleet aggregator model is introduced in a stochastic formulation of DER-CAM [1], an optimization tool used to address DER investment and scheduling problems. This is used to assess the impact of EV interconnections on optimal DER solutions considering uncertainty in EV driving schedules. Optimization results indicate that EVs can have a significant impact on DER investments, particularly if considering short payback periods. Furthermore, results suggest that uncertainty in driving schedules carries little significance to total energy costs, which is corroborated by results obtained using the stochastic formulation of the problem.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2013

Microgrid Reliability Modeling and Battery Scheduling Using Stochastic Linear Programming

This paper describes the introduction of stochastic linear programming into Operations DER-CAM, a tool used to obtain optimal operating schedules for a given microgrid under local economic and environmental conditions. This application follows previous work on optimal scheduling of a lithium-iron-phosphate battery given the output uncertainty of a 1 MW molten carbonate fuel cell. Both are in the Santa Rita Jail microgrid, located in Dublin, California. This fuel cell has proven unreliable, partially justifying the consideration of storage options. Several stochastic DER-CAM runs are executed to compare different scenarios to values obtained by a deterministic approach. Results indicate that using a stochastic approach provides a conservative yet more lucrative battery schedule. Lower expected energy bills result, given fuel cell outages, in potential savings exceeding 6percent.

25 ENERGY STORAGE↗
DOE OSTI2013

Encouraging Combined Heat and Power in California Buildings

Governor Brown’s research priorities include an additional 6.5 GW of combined heat and power (CHP) by 2030. As of 2009, roughly 0.25 GW of small natural gas and biogas fired CHP is documented by the Self-Generation Incentive Program (SGIP) database. The SGIP is set to expire, and the anticipated grid de-carbonization based on the development of 20 GW of renewable energy will influence the CHP adoption. Thus, an integrated optimization approach for this analysis was chosen that allows optimizing the adoption of distributed energy resources (DER) such as photovoltaics (PV), CHP, storage technologies, etc. in the California commercial sector from the building owners’ perspective. To solve this DER adoption problem the Distributed Energy Resources Customer Adoption Model (DER-CAM), developed by the Lawrence Berkeley National Laboratory and used extensively to address the problem of optimally investing and scheduling DER under multiple settings, has been used. The application of CHP at large industrial sites is well known, and much of its potential is already being realized. Conversely, commercial sector CHP, especially those above 50 to 100 kW peak electricity load, is widely overlooked. In order to analyze the role of DER in CO 2 reduction, 147 representative sites in different climate zones were selected from the California Commercial End Use Survey (CEUS). About 8000 individual optimization runs, with different assumptions for the electric tariffs, natural gas costs, marginal grid CO 2 emissions, and nitrogen oxide treatment costs, SGIP, fuel cell lifetime, fuel cell efficiency, PV installation costs, and payback periods for investments have been performed. The most optimistic CHP potential contribution in this sector in 2020 will be 2.7 GW. However, this result requires a SGIP in 2020, 46% average electric efficiency for fuel cells, a payback period for investments of 10 years, and a CO 2 focused approach of the building owners. In 2030 it will be only 2.5 GW due to the anticipated grid decarbonization. The 2030 result requires a 60% electric efficiency and 20 year life time for fuel cells, a payback period of 10 years, and a CO 2 minimization strategy of building owners. Finally, the possible CHP potential in 2030 shows a significant variance between 0.2 GW and 2.5 GW, demonstrating the complex interactions between technologies, policies, and customer objectives.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗