Imperial College London
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Dr Salvador Acha

Faculty of Engineering, Department of Chemical Engineering

Senior Research Fellow
 
 
 
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Contact

 

+44 (0)20 7594 3379salvador.acha Website CV

 
 
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Location

 

453AACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Olympios:2020:10.1016/j.enconman.2020.112916,
author = {Olympios, AV and Le, Brun N and Acha, S and Shah, N and Markides, CN},
doi = {10.1016/j.enconman.2020.112916},
journal = {Energy Conversion and Management},
pages = {1--17},
title = {Stochastic real-time operation control of a combined heat and power (CHP) system under uncertainty},
url = {http://dx.doi.org/10.1016/j.enconman.2020.112916},
volume = {216},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - In this paper we present an effort to design and apply a multi-objective real-time operation controller to a combined heat and power (CHP) system, while considering explicitly the risk-return trade-offs arising from the uncertainty in the price of exported electricity. Although extensive research has been performed on theoretically optimizing the design, sizing and operation of CHP systems, less effort has been devoted to an understanding of the practical challenges and the effects of uncertainty in implementing advanced algorithms in real-world applications. In this work, a two-stage control architecture is proposed which applies an optimization framework to a real CHP operation application involving intelligent communication between two controllers to monitor and control the engine continuously. Since deterministic approaches that involve no measure of uncertainty provide limited insight to decision-makers, the methodology then proceeds to develop a stochastic optimization technique which considers risk within the optimization problem. The uncertainty in the forecasted electricity price is quantified by using the forecasting model’s residuals to generate prediction intervals around each forecasted electricity price. The novelty of the proposed tool lies in the use of these prediction intervals to formulate a bi-objective function that represents a compromise between maximizing the expected savings and minimizing the associated risk, while satisfying specified environmental objectives. This allows decision-makers to operate CHP systems according to the risk they are willing to take. The actual operation costs during a 40–day trial period resulting from the installation of the dynamic controller on an existing CHP engine that provides electricity and heat to a supermarket are presented. Results demonstrate that the forecasted electricity price almost always falls within the developed prediction intervals, achieving savings of 23% on energy costs against
AU  - Olympios,AV
AU  - Le,Brun N
AU  - Acha,S
AU  - Shah,N
AU  - Markides,CN
DO  - 10.1016/j.enconman.2020.112916
EP  - 17
PY  - 2020///
SN  - 0196-8904
SP  - 1
TI  - Stochastic real-time operation control of a combined heat and power (CHP) system under uncertainty
T2  - Energy Conversion and Management
UR  - http://dx.doi.org/10.1016/j.enconman.2020.112916
UR  - https://www.sciencedirect.com/science/article/pii/S0196890420304544?via%3Dihub
UR  - http://hdl.handle.net/10044/1/79376
VL  - 216
ER  -
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