Reducing a semiarid city’s peak electrical demand using distributed cold thermal energy storage

Ruddell, Benjamin L.; Salamanca, Francisco; Mahalov, Alex

doi:10.1016/j.apenergy.2014.07.096

Cited by 50 publications

(12 citation statements)

References 49 publications

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“…Policies that would guide new or replacement ACs based on different performance constraints or different technologies would aid in reducing the risk of excessive peak demand during extreme heat events. It is possible to design AC units that are more efficient under the hottest conditions or that utilize thermal storage to achieve 'flat' efficiency curves that do not degrade at the hottest temperatures [70,71]. For example, developing a new 'peak performance rating' for ACs at 50°C (122°F) could be useful to mitigate peak load during extreme heat waves.…”

Section: Electrical Systems: Loadsmentioning

confidence: 99%

Effects of Climate Change in Electric Power Infrastructures

Burillo¹

2019

Power System Stability

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Climate change mitigation and adaptation has been a major driving force to modernize electric power infrastructure and include more renewable energy systems. This chapter explains several ways in which electric power infrastructure has contributed to climate change, how climate change affects electric power infrastructure, mitigation options, and adaptation options. Electricity infrastructure categories include power generation technologies, transmission lines, substations, and building loads. Climate change categories include atmospheric greenhouse gas concentration levels, rising sea levels, changes in precipitation patterns and river flows, as well as more extreme air temperatures. Specific quantitative case studies are provided to estimate vulnerabilities from heat waves in the US desert southwest, including long-term forecasting of infrastructure performance, as well as, various supply-side and demand-side strategic options to maintain reliable operations.

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Section: Electrical Systems: Loadsmentioning

confidence: 99%

Effects of Climate Change in Electric Power Infrastructures

Burillo¹

2019

Power System Stability

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“…Using this management system, this research studied six buildings and evaluated the difference in technology adoption between different TES models. Ruddell et al [19] estimated the cooling electricity demand during summer time in Phoenix and studied the application of using distributed TES for peak demand shifting. Moreover, there also are extensive literature improving the building energy efficiency by optimizing the operation HVAC systems [20][21][22][23] without using any distributed energy systems.…”

Section: Introductionmentioning

confidence: 99%

An operation optimization and decision framework for a building cluster with distributed energy systems

Wen

Malkawi

2016

Applied Energy

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h i g h l i g h t sDeveloped and validated energy forecasting models for a building cluster with distributed energy systems. Proposed an operation optimization and decision framework for the building cluster. Optimized the operation strategies through particle swarm algorithm based multi-objective optimizations. Derived the Pareto frontiers to balance the energy cost saving and thermal comfort. a b s t r a c tDriven by the development of smart buildings and smart grids, numerous of research has focused on developing optimal operation strategies for smart buildings with the aims of reducing energy consumption and cost, as well as improving the grid reliability. Unfortunately, most of the studies from smart building perspective only target on a single building with elaborated energy forecasting models. Few of them addresses the effects of multiple buildings on power grid operation. On the other hand, a few studies from smart grid area focus on multiple buildings and their influence on power grid, they usually, however, use simplified linear energy forecasting models, which are hard to guarantee the findings reflecting the cases in real fields. As a result, this research proposes to bridge this research gap, through developing and validating high fidelity energy forecasting models for a building cluster with multiple buildings and distributed energy systems, as well as creating a collaborative operation framework to determining the optimal operation strategies of this building cluster. The operation framework utilizes multi-objective optimizations to determine the operation strategies: building temperature setpoints, energy storage charging and discharging schedules, etc., using particle swarm optimization. Pareto curves for energy cost saving and thermal comfort maintaining are also derived with different thermal comfort requirements. The results from this study show that the developed building cluster collaborative operation framework is able to reduce the energy cost by 12.1-58.3% under different electricity pricing plans and thermal comfort requirements.

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“…Thermal energy of low to high grades, as classified by the temperature, may be stored by latent heat TES by carefully choosing the PCMs with appropriate phase change temperatures. Water, a ubiquitous matter on the earth, has long been practiced in cold TES [1] for its abundance, chemical compatibility, and superior storage density with application to food preservation and air conditioning [2].…”

Section: Introductionmentioning

confidence: 99%