2 results found
Olympios A, Pantaleo AM, Sapin P, et al., Centralised vs distributed energy systems options: District heating for the Isle of Dogs in London, ICAE2019: The 11th International Conference on Applied Energy
This work focuses on a multi-scale framework for the design and comparison of low-carbon heat generation solutions to serve the residential and commercial thermal energy demand of high energy density urban areas. The adopted methodology assesses the cost and performance of four configurations integrated in a district heating network: (i) centralised cogeneration with gas turbine and bottoming steam turbine with flexible heat-to-electricity ratio; (ii) centralised cogeneration with gas-fired internal combustion engine; (iii) distributed building-integrated ground-source heat pumps for domestic hot water only; and (iv) distributed building-integrated ground-source heat pumps for both domestic hot water and space heating. Cost and performance data were obtained by conducting relevant market research and developing a simplified heat pump thermodynamic model. The different configurations are evaluated utilizing whole-year space heating and hot water demand profiles for the Isle of Dogs area in East London, UK. Scale effects are included by considering various technology size scenarios and the results indicate that a 50 MW centralised internal combustion cogeneration system appears to be the most profitable option, while the competitiveness of building-integrated heat pumps is dependent on their size.
Olympios A, Le Brun N, Acha Izquierdo S, et al., Installation of a dynamic controller for the optimal operation of a CHP engine in a supermarket under uncertainty, 32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems
This work is concerned with the integration and coordination of decentralized combined heat and power (CHP) systems in commercial buildings. 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 to real-world applications. This paper provides details of an undergoing field trial involving the installation of a dynamic controller for the optimal operation of an existing CHP engine, which provides electricity and heat to a supermarket. The challenges in developing and applying an optimization framework and the software architecture required to implement it are discussed. Deterministic approaches that involve no measure of uncertainty provide limited useful insight to decision makers. For this reason, the methodology here develops a stochastic programming technique, which performs Monte Carlo simulations that can consider the uncertainty related to the exporting electricity price. The method involves the formation of a bi-objective function that represents a compromise between maximizing the expected savings and minimizing the associated risk. The results reveal a risk-return trade-off, demonstrating that conservative operation choices emerging from the stochastic approach can reduce risk by about 15% at the expense of a noticeably smaller reduction of about 10% in expected savings.
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