Dynamic optimization of a district energy system with storage using a novel mixed-integer quadratic programming algorithm

Blackburn, Landen D.; Young, Aaron; Rogers, W. Pratt; Hedengren, John D.; Powell, Kody M.

doi:10.1007/s11081-018-09419-w

Cited by 23 publications

(7 citation statements)

References 35 publications

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“…The University of Utah's north chiller plant is optimized by Blackburn et al (2019). The authors model the problem as a mixed-integer quadratic program, for which they develop a heuristic.…”

Section: Overview Of the Special Issue Articlesmentioning

confidence: 99%

Mixed-integer nonlinear programming 2018

Sahinidis

2019

Optim Eng

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Mixed-Integer Nonlinear Programming (MINLP) is the area of optimization that addresses nonlinear problems with continuous and integer variables. MINLP has proven to be a powerful tool for modeling. At the same time, it combines algorithmic design challenges from combinatorial and nonlinear optimization. The MINLP field has received increased attention over the past two decades with contributions on the theoretical, algorithmic, and computational side originating from a growing community that involves engineers, mathematicians, and operations researchers. This special issue was motivated by a seminar on MINLP that took place in Dagstuhl, Germany in 2018. The purpose of this article is to provide a brief introduction to the field and the articles of the special issue.

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Section: Overview Of the Special Issue Articlesmentioning

confidence: 99%

Mixed-integer nonlinear programming 2018

Sahinidis

2019

Optim Eng

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“…For the 4th term, two hours of past demand values are used (eight-time steps in the past). Equations (9) and (10) represent the two-step L-ARX regression model as follows:…”

Section: Linear Arx Regression Modelsmentioning

confidence: 99%

“…Many researchers have proposed models to perform load shifting in smart grids with the inclusion of electric vehicles [3][4][5]. Other researchers have studied DSM for smart building automation [6][7][8][9]. In fact, studies related to DSM and its impact on the power sector cannot be just summarized in a few lines.…”

Section: Introductionmentioning

confidence: 99%

Using Real-Time Electricity Prices to Leverage Electrical Energy Storage and Flexible Loads in a Smart Grid Environment Utilizing Machine Learning Techniques

Sheha

Powell

2019

Processes

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With exposure to real-time market pricing structures, consumers would be incentivized to invest in electrical energy storage systems and smart predictive automation of their home energy systems. Smart home automation through optimizing HVAC (heating, ventilation, and air conditioning) temperature set points, along with distributed energy storage, could be utilized in the process of optimizing the operation of the electric grid. Using electricity prices as decision variables to leverage electrical energy storage and flexible loads can be a valuable tool to optimize the performance of the power grid and reduce electricity costs both on the supply and demand sides. Energy demand prediction is important for proper allocation and utilization of the available resources. Manipulating energy prices to leverage storage and flexible loads through these demand prediction models is a novel idea that needs to be studied. In this paper, different models for proactive prediction of the energy demand for an entire city using different machine learning techniques are presented and compared. The results of the machine learning techniques show that the proposed nonlinear autoregressive with exogenous inputs neural network model resulted in the most accurate predictions. These prediction models pave the way for the demand side to become an important asset for grid regulation by responding to variable price signals through battery energy storage and passive thermal energy storage using HVAC temperature set points.

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“…Meanwhile, we do consider heat loss characteristic in TES. In this regard, the adopted methodology does not reflect the dynamic characteristics of the system such as flexibility, control strategy, which requires more detailed dynamic optimization shown in the literatures [41,[43][44][45][46].…”

Section: Flux-based Modelmentioning

confidence: 99%

Comparison of Direct and Indirect Active Thermal Energy Storage Strategies for Large-Scale Solar Heating Systems

2019

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Large-scale solar heating for the building sector requires an adequate Thermal Energy Storage (TES) strategy. TES plays the role of load shifting between the energy demand and the solar irradiance and thus makes the annual production optimal. In this study, we report a simplified algorithm uniquely based on energy flux, to evaluate the role of active TES on the annual performance of a large-scale solar heating for residential thermal energy supply. The program considers different types of TES, i.e., direct and indirect, as well as their specifications in terms of capacity, storage density, charging/discharging limits, etc. Our result confirms the auto-regulation ability of indirect (latent using Phase Change Material (PCM), or Borehole thermal storage (BTES) in soil) TES which makes the annual performance comparable to that of direct (sensible with hot water) TES. The charging and discharging restrictions of the latent TES, until now considered as a weak point, could retard the achievement of fully-charged situation and prolong the charging process. With its compact volume, the indirect TES turns to be promising for large-scale solar thermal application.

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Dynamic optimization of a district energy system with storage using a novel mixed-integer quadratic programming algorithm

Cited by 23 publications

References 35 publications

Mixed-integer nonlinear programming 2018

Mixed-integer nonlinear programming 2018

Using Real-Time Electricity Prices to Leverage Electrical Energy Storage and Flexible Loads in a Smart Grid Environment Utilizing Machine Learning Techniques

Comparison of Direct and Indirect Active Thermal Energy Storage Strategies for Large-Scale Solar Heating Systems

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