Time filter

Source Type

Milan C.,University of Aalborg | Milan C.,Lawrence Berkeley National Laboratory | Stadler M.,Lawrence Berkeley National Laboratory | Stadler M.,Center for Energy and innovative Technologies | And 3 more authors.
Applied Energy | Year: 2015

Distributed energy resources gain an increased importance in commercial and industrial building design. Combined heat and power (CHP) units are considered as one of the key technologies for cost and emission reduction in buildings. In order to make optimal decisions on investment and operation for these technologies, detailed system models are needed. These models are often formulated as linear programming problems to keep computational costs and complexity in a reasonable range. However, CHP systems involve variations of the efficiency for large nameplate capacity ranges and in case of part load operation, which can be even of non-linear nature. Since considering these characteristics would turn the models into non-linear problems, in most cases only constant efficiencies are assumed. This paper proposes possible solutions to address this issue. For a mixed integer linear programming problem two approaches are formulated using binary and Special-Ordered-Set (SOS) variables. Both suggestions have been implemented into the optimization model DER-CAM to simulate investment decisions of CHP micro-turbines and CHP fuel cells with variable efficiencies. The approaches have further been applied successfully in a case study with four different commercial buildings. Comparison of the results between the standard version and the new approaches indicate that total annual system costs remain almost unchanged. System performance is subject to change and storage technologies become more important. Part load operation has mainly been found important for fuel cell units. The micro-turbine is found almost exclusively in full load, thus rendering the application of the new approaches for this technology unnecessary for the considered unit sizes and building types. The approach using binary variables was the most promising method to model variable efficiencies in terms of computational costs and results. It should especially be considered for specific fuel cell technologies. Further investigation on the impacts of this approach on the prediction of fuel cell and micro-turbine performance is suggested. © 2015.

Cardoso G.,University of Lisbon | Stadler M.,Lawrence Berkeley National Laboratory | Stadler M.,Center for Energy and Innovative Technologies | Bozchalui M.C.,NEC Laboratories America Inc. | And 4 more authors.
Energy | Year: 2014

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. © 2013 Elsevier Ltd.

Stadler M.,Lawrence Berkeley National Laboratory | Stadler M.,Center for Energy and innovative Technologies | Groissbock M.,Lawrence Berkeley National Laboratory | Groissbock M.,Center for Energy and innovative Technologies | And 3 more authors.
Applied Energy | Year: 2014

The pressuring need to reduce the import of fossil fuels as well as the need to dramatically reduce CO2 emissions in Europe motivated the European Commission (EC) to implement several regulations directed to building owners. Most of these regulations focus on increasing the number of energy efficient buildings, both new and retrofitted, since retrofits play an important role in energy efficiency. Overall, this initiative results from the realization that buildings will have a significant impact in fulfilling the 20/20/20-goals of reducing the greenhouse gas emissions by 20%, increasing energy efficiency by 20%, and increasing the share of renewables to 20%, all by 2020.The Distributed Energy Resources Customer Adoption Model (DER-CAM) is an optimization tool used to support DER investment decisions, typically by minimizing total annual costs or CO2 emissions while providing energy services to a given building or microgrid site. This paper shows enhancements made to DER-CAM to consider building retrofit measures along with DER investment options. Specifically, building shell improvement options have been added to DER-CAM as alternative or complementary options to investments in other DER such as PV, solar thermal, combined heat and power, or energy storage. The extension of the mathematical formulation required by the new features introduced in DER-CAM is presented and the resulting model is demonstrated at an Austrian Campus building by comparing DER-CAM results with and without building shell improvement options. Strategic investment results are presented and compared to the observed investment decision at the test site. Results obtained considering building shell improvement options suggest an optimal weighted average U value of about 0.53W/(m2K) for the test site. This result is approximately 25% higher than what is currently observed in the building, suggesting that the retrofits made in 2002 were not optimal. Furthermore, the results obtained with DER-CAM illustrate the complexity of interactions between DER and passive measure options, showcasing the need for a holistic optimization approach to effectively optimize energy costs and CO2 emissions. The simultaneous optimization of building shell improvements and DER investments enables building owners to take one step further towards nearly zero energy buildings (nZEB) or nearly zero carbon emission buildings (nZCEB), and therefore support the 20/20/20 goals. © 2014 .

DeForest N.,Lawrence Berkeley National Laboratory | Mendes G.,Lawrence Berkeley National Laboratory | Mendes G.,University of Lisbon | Stadler M.,Lawrence Berkeley National Laboratory | And 4 more authors.
Applied Energy | Year: 2014

This paper presents an investigation of the economic benefit of thermal energy storage (TES) for cooling, across a range of economic and climate conditions. Chilled water TES systems are simulated for a large office building in four distinct locations, Miami in the U.S.; Lisbon, Portugal; Shanghai, China; and Mumbai, India. Optimal system size and operating schedules are determined using the optimization model DER-CAM, such that total cost, including electricity and amortized capital costs are minimized. The economic impacts of each optimized TES system is then compared to systems sized using a simple heuristic method, which bases system size as fraction (50% and 100%) of total daily on-peak summer cooling loads.Results indicate that TES systems of all sizes can be effective in reducing annual electricity costs (5-15%) and peak electricity consumption (13-33%). The investigation also identifies a number of criteria which drive TES investment, including low capital costs, electricity tariffs with high power demand charges and prolonged cooling seasons. In locations where these drivers clearly exist, the heuristically sized systems capture much of the value of optimally sized systems; between 60% and 100% in terms of net present value. However, in instances where these drivers are less pronounced, the heuristic tends to oversize systems, and optimization becomes crucial to ensure economically beneficial deployment of TES, increasing the net present value of heuristically sized systems by as much as 10 times in some instances. © 2014 Elsevier Ltd.

Cardoso G.,University of Lisbon | Stadler M.,Lawrence Berkeley National Laboratory | Stadler M.,Center for Energy and Innovative Technologies | Siddiqui A.,University College London | And 5 more authors.
Electric Power Systems Research | Year: 2013

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 6%. © 2013 Elsevier B.V. All rights reserved.

Stadler M.,Lawrence Berkeley National Laboratory | Stadler M.,Center for Energy and Innovative Technologies | Kloess M.,Lawrence Berkeley National Laboratory | Kloess M.,Vienna University of Technology | And 6 more authors.
Applied Energy | Year: 2013

Most recent improvements in battery and electric vehicle (EV) technologies, combined with some favorable off-peak charging rates and an enormous PV potential, make California a prime market for electric vehicle as well as stationary storage adoption. However, EVs or plug-in hybrids, which can be seen as a mobile energy storage, connected to different buildings throughout the day, constitute distributed energy resources (DER) markets and can compete with stationary storage, onsite energy production (e.g. fuel cells, PV) at different building sites. Sometimes mobile storage is seen linked to renewable energy generation (e.g. PV) or as resource for the wider macro-grid by providing ancillary services for grid-stabilization. In contrast, this work takes a fundamentally different approach and considers buildings as the main hub for EVs/plug-in hybrids and considers them as additional resources for a building energy management system (EMS) to enable demand response or any other building strategy (e.g. carbon dioxide reduction). To examine the effect of, especially, electric storage technologies on building energy costs and carbon dioxide (CO2) emissions, a distributed-energy resources adoption problem is formulated as a mixed-integer linear program with minimization of annual building energy costs or CO2 emissions. The mixed-integer linear program is applied to a set of 139 different commercial building types in California, and the aggregated economic and environmental benefits are reported. To show the robustness of the results, different scenarios for battery performance parameters are analyzed. The results show that the number of EVs connected to the California commercial buildings depend mostly on the optimization strategy (cost versus CO2) of the building EMS and not on the battery performance parameters. The complexity of the DER interactions at buildings also show that a reduction in stationary battery costs increases the local PV adoption, but can also increase the fossil based onsite electricity generation, making an holistic optimization approach necessary for this kind of analyses. © 2012 Elsevier Ltd.

Steen D.,Lawrence Berkeley National Laboratory | Steen D.,Chalmers University of Technology | Stadler M.,Lawrence Berkeley National Laboratory | Stadler M.,Center for Energy and Innovative Technologies | And 6 more authors.
Applied Energy | Year: 2015

Thermal energy storage (TES) and distributed generation technologies, such as combined heat and power (CHP) or photovoltaics (PV), can be used to reduce energy costs and decrease CO2 emissions from buildings by shifting energy consumption to times with less emissions and/or lower energy prices. To determine the feasibility of investing in TES in combination with other distributed energy resources (DER), mixed integer linear programming (MILP) can be used. Such a MILP model is the well-established Distributed Energy Resources Customer Adoption Model (DER-CAM); however, it currently uses only a simplified TES model to guarantee linearity and short run-times. Loss calculations are based only on the energy contained in the storage. This paper presents a new DER-CAM TES model that allows improved tracking of losses based on ambient and storage temperatures, and compares results with the previous version. A multi-layer TES model is introduced that retains linearity and avoids creating an endogenous optimization problem. The improved model increases the accuracy of the estimated storage losses and enables use of heat pumps for low temperature storage charging. Results indicate that the previous model overestimates the attractiveness of TES investments for cases without possibility to invest in heat pumps and underestimates it for some locations when heat pumps are allowed. Despite a variation in optimal technology selection between the two models, the objective function value stays quite stable, illustrating the complexity of optimal DER sizing problems in buildings and microgrids. © 2014.

Stadler M.,Lawrence Berkeley National Laboratory | Stadler M.,Center for Energy and Innovative Technologies | Siddiqui A.,University College London | Siddiqui A.,University of Stockholm | And 4 more authors.
European Transactions on Electrical Power | Year: 2011

The U.S. Department of Energy has launched the commercial building initiative (CBI) in pursuit of its research goal of achieving zero-net-energy commercial buildings (ZNEB), i.e., ones that produce as much energy as they use. Its objective is to make these buildings marketable by 2025 such that they minimize their energy use through cutting-edge, energy-efficiency technologies and meet their remaining energy needs through on-site renewable energy generation. This paper examines how such buildings may be implemented within the context of a cost- or CO2-minimizing microgrid that is able to adopt and operate various technologies: photovoltaic (PV) modules and other on-site generation, heat exchangers, solar thermal collectors, absorption chillers, and passive/demand-response technologies. A mixed-integer linear program (MILP) that has a multi-criteria objective function is used. The objective is minimization of a weighted average of the building's annual energy costs and CO2 emissions. The MILP's constraints ensure energy balance and capacity limits. In addition, constraining the building's energy consumed to equal its energy exports enables us to explore how energy sales and demand-response measures may enable compliance with the ZNEB objective. Using a commercial test site in northern California with existing tariff rates and technology data, we find that a ZNEB requires ample PV capacity installed to ensure electricity sales during the day. This is complemented by investment in energy-efficient combined heat and power (CHP) equipment, while occasional demand response saves energy consumption. A large amount of storage is also adopted, which may be impractical. Nevertheless, it shows the nature of the solutions and costs necessary to achieve a ZNEB. Additionally, the ZNEB approach does not necessary lead to zero-carbon (ZC) buildings as is frequently argued. We also show a multi-objective frontier for the CA example, which allows us to estimate the needed technologies and costs for achieving a ZC building or microgrid. © 2010 John Wiley & Sons, Ltd.

Rocha P.,University College London | Siddiqui A.,University College London | Siddiqui A.,University of Stockholm | Stadler M.,Center for Energy and Innovative Technologies | Stadler M.,Lawrence Berkeley National Laboratory
Energy and Buildings | Year: 2015

To foster the transition to more sustainable energy systems, policymakers have been approving measures to improve energy efficiency as well as promoting smart grids. In this setting, building managers are encouraged to adapt their energy operations to real-time market and weather conditions. Yet, most fail to do so as they rely on conventional building energy management systems (BEMS) that have static temperature set points for heating and cooling equipment. In this paper, we investigate how effective policy measures are at improving building-level energy efficiency compared to a smart BEMS with dynamic temperature set points. To this end, we present an integrated optimisation model mimicking the smart BEMS that combines decisions on heating and cooling systems operations with decisions on energy sourcing. Using data from an Austrian and a Spanish building, we find that the smart BEMS results in greater reduction in energy consumption than a conventional BEMS with policy measures. © 2014 The Authors. Published by Elsevier B.V.

Kossak B.,Center for Energy and Innovative Technologies | Stadler M.,Center for Energy and Innovative Technologies | Stadler M.,Lawrence Berkeley National Laboratory
Energy and Buildings | Year: 2015

In the course of the European Project Energy Efficiency and Risk Management in public buildings (EnRiMa), a mathematical model has been needed, predicting the room air temperatures based on the physical properties of the thermal zone and weather forecasts. Existing models based on physical building properties and weather forecasts did not deliver acceptable results. Based on the hypothesis that the missing thermal mass in the existing models is the main reason for the unacceptable results, a model based on physical properties and weather forecast, including the storage mass of a building has been developed. Based on this developed model and real data from a test site, Campus Pinkafeld of the University of Applied Science Burgenland, Austria, the model has been verified and validated. With the new developed model it is possible to predict the occurring room air temperature for a whole day with a maximum deviation of approximately ±1 K, which increases the precision compared to other models.

Loading Center for Energy and innovative Technologies collaborators
Loading Center for Energy and innovative Technologies collaborators