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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. Source


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. Source


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. Source


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. Source


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. Source

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