Building demand-side control using thermal energy storage under uncertainty: An adaptive Multiple Model-based Predictive Control (MMPC) approach

Kim, Sean Hay

doi:10.1016/j.buildenv.2013.05.005

Cited by 31 publications

(9 citation statements)

References 20 publications

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“…However, energy consumption and weather forecasts which allow for effective predictive and proactive control are often inaccurate, which leads to sub-optimal control [26,47,48]. In addition to uncertainty in building energy forecasts and weather forecasts, system disturbances can occur which could be unpredicted by the proactive controller [49]. There have been many methods presented to deal with these issues in the academic literature.…”

Section: Accounting For Forecast Uncertainty and System Disturbancesmentioning

confidence: 99%

“…Vahid-Pakdel et al use a stochastic mixed-integer linear programming (MILP) methodology to account for uncertainties such as in demands, prices, and wind speed in a smart grid optimization, reducing costs by 5% [50]. Kim et al use adaptive MPC and multiple distributed simple system models to both alleviate computational burden and address system disturbances in building energy control [49]. Zhang et al introduce randomized model predictive control (RMPC) which samples multiple possible scenarios within an uncertainty window to implement stochastic methods in the absence of a probabilistic disturbance model [51].…”

Section: Accounting For Forecast Uncertainty and System Disturbancesmentioning

confidence: 99%

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Proactive Energy Optimization in Residential Buildings with Weather and Market Forecasts

et al. 2019

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This work explores the development of a home energy management system (HEMS) that uses weather and market forecasts to optimize the usage of home appliances and to manage battery usage and solar power production. A Moving Horizon Estimation (MHE) application is used to find the unknown home model parameters. These parameters are then updated in a Model Predictive Controller (MPC) which optimizes and balances competing comfort and economic objectives. Combining MHE and MPC applications alleviates model complexity commonly seen in HEMS by using a lumped parameter model that is adapted to fit a high-fidelity model. Heating, ventilation, and air conditioning (HVAC) on/off behaviors are simulated by using Mathematical Program with Complementarity Constraints (MPCCs) and solved in near real time with a non-linear solver. Removing HVAC on/off as a discrete variable and replacing it with an MPCC reduces solve time. The results of this work indicate that energy management optimization significantly decreases energy costs and balances energy usage more effectively throughout the day. A case study for Phoenix, Arizona shows an energy reduction of 21% and a cost reduction of 40%. This simulated home contributes less to the grid peak load and therefore improves grid stability and reduces the amplitude of load-following cycles for utilities. The case study combines renewable energy, energy storage, forecasts, cooling system, variable rate electricity plan and a multi-objective function allowing for a complete home energy optimization assessment. There remain several challenges, including improved forecast models, improved computational performance to allow the algorithms to run in real time, and mixed empirical/physics-based machine-learning methods to guide the model structure.

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Section: Accounting For Forecast Uncertainty and System Disturbancesmentioning

confidence: 99%

Section: Accounting For Forecast Uncertainty and System Disturbancesmentioning

confidence: 99%

Proactive Energy Optimization in Residential Buildings with Weather and Market Forecasts

et al. 2019

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“…TRNSYS is another flexible graphics-based software environment used to simulate the behavior of transient systems. There are also several studies focusing on developing distributed energy system models in TRNSYS, such as PV panels models [22] and battery models [23]. To the best of the authors' knowledge, no simulation tool exists that can simulate the Net-zero building cluster cases proposed in this study.…”

Section: Figure 1 Building Cluster Connection and Operationmentioning

confidence: 99%

Net-zero energy building clusters emulator for energy planning and operation evaluation

Wen

2017

Computers, Environment and Urban Systems

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The emergence of smart grids, Net-zero energy buildings, and advanced building energy demand response technologies continuously drives the needs for better design and operation strategies for buildings and distributed energy systems. It is envisioned that similar to micro-communities in a human society, neighboring buildings will have the tendency to form a building cluster, an open cyber-physical system to exploit the economic opportunities provided by smart grids and distributed energy systems. To realize this building cluster envision, it requires better urban energy planning and operation control strategies to determine which type of buildings should be clustered and what operation strategies should be implemented to fully utilize the potential in load aggregation, load shifting, and resource allocation. However, most of the current tools are focusing on single buildings or devices, which are not suitable for building cluster studies. To this end, this study proposes to develop a Net-zero building cluster emulator that can simulate realistic energy behaviors of a cluster of buildings and their distributed energy devices as well as exchange operation data and control schemes with real-world building control systems. The developed emulator has the flexibility to integrate with different buildings and distributed energy systems to study the performance of this building cluster to propose suggestions in urban energy planning and operation. To show the application of this emulator, a proof-of-concept demonstration is also presented in this paper.

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“…Following a brief overview of the methodology (section II), the present article illustrates the concept for two adjacent rooms in a modern university building (sections III and IV), and discusses some of the modelling issues involved. The electrical analogy is chosen so that the models obtained can be quickly extended and used for future research into demand-side control [4] of multiple buildings on the university network, requiring a fast computation time for energy optimisation purposes. With this objective in mind, the model should be simple to construct and implement, initially using readily available physical parameters, such as room dimensions and estimates of thermal resistance.…”

Section: Introductionmentioning

confidence: 99%

Simplified models for heating system optimisation using the thermal–electrical analogy

Tate

Cheneler

Taylor

2019

2019 25th International Conference on Automation and Computing (ICAC)

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The well-known electrical analogy for thermal modelling is based on the observation that Fourier's equation for one dimensional heat transfer takes the same form as Ohm's law. This provides a system for creating and resolving complex heat transfer problems using an established set of physically-based laws. The present article illustrates the concept for adjacent rooms in a modern university building, and investigates some of the modelling issues involved. The electrical analogy is chosen so that the models can be extended and used for future research into demand-side control of multiple buildings on the university network, requiring a fast computation time. For illustrative purposes, the present article is limited to a relatively straightforward two-room system, for which the modelling equations are conveniently represented and solved using MATLAB-SIMULINK. The coefficients of this model are estimated from data using standard nonlinear optimisation tools. For comparison, the article also develops an equivalent multiple-input Transfer Function form of the model. Finally, suggestions are made for the inclusion of occupancy estimates in the model.

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Building demand-side control using thermal energy storage under uncertainty: An adaptive Multiple Model-based Predictive Control (MMPC) approach

Cited by 31 publications

References 20 publications

Proactive Energy Optimization in Residential Buildings with Weather and Market Forecasts

Proactive Energy Optimization in Residential Buildings with Weather and Market Forecasts

Net-zero energy building clusters emulator for energy planning and operation evaluation

Simplified models for heating system optimisation using the thermal–electrical analogy

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