Vikas Chandan

Modeling and optimization of a combined cooling, heating and power plant system

In this paper, we develop a modeling and optimization procedure for minimizing the operating costs of a combined cooling, heating, and power (CCHP) plant at the University of California, Irvine, which uses co-generation and Thermal Energy Storage (TES) capabilities. Co-generation allows the production of thermal energy along with electricity, by recovering heat from the generators in a power plant. TES provides the ability to `reshape the cooling demands during the course of a day, in refrigeration and air-conditioning plants. Therefore, both cogeneration and TES provide a potential to improve the efficiency and economy of energy conversion. The proposed modeling and optimization approach aims to design a supervisory control strategy to effectively utilize this potential, and involves analysis over multiple physical domains which the CCHP system spans, such as thermal, mechanical, chemical and electrical. Advantages of the proposed methodology are demonstrated using simulation case studies.

Optimal Partitioning for the Decentralized Thermal Control of Buildings

This paper studies the problem of thermal control of buildings from the perspective of partitioning them into clusters for decentralized control. A measure of deviation in performance between centralized and decentralized control in the model predictive control framework, referred to as the optimality loss factor, is derived. Another quantity called the fault propagation metric is introduced as an indicator of the robustness of any decentralized architecture to sensing or communication faults. A computationally tractable agglomerative clustering approach is then proposed to determine the decentralized control architectures, which provide a satisfactory trade-off between the underlying optimality and robustness objectives. The potential use of the proposed partitioning methodology is demonstrated using simulated examples.

Optimal control architecture selection for thermal control of buildings

The problem of partitioning a building into clusters is considered in this paper, with reference to its decentralized thermal control. Optimal control schemes for these systems are often centralized and address both the thermal comfort and energy efficiency requirements. However, due to robustness considerations, a decentralized architecture may be preferred for large scale systems, which is at best sub-optimal. Therefore, the ‘degree of decentralization’ governs the trade-off between optimality and robustness. This paper proposes a combinatorial optimization based systematic methodology for obtaining an optimal degree of decentralization on the basis of two metrics - one for optimality (defined as Coupling Loss Factor) and one for robustness (defined as Mean Cluster Size). The methodology was evaluated on a building model and results were found to be in agreement with the physics of the underlying thermal interactions.

Predictive control of complex hydronic systems

The control of hydronic building systems is considered in this paper, using a simulated chilled water system as a case study. In this context, model-based predictive control strategies have been proposed and compared with traditional feedback control schemes. The advantages and limitations associated with these methodologies has been demonstrated. The cornerstone of this work is the development of a novel, distributed predictive scheme which provides the best compromise in the multidimensional evaluation framework of `regulation, `optimality, `reliability and `computational complexity.

Decentralized architectures for thermal control of buildings

This paper considers the problem of partitioning a building or any other complex energy system into clusters for its decentralized thermal control. Using a Model Predictive Control (MPC) framework, a measure of deviation in performance between centralized controland decentralized control, called the Optimality Loss Factor (OLF) is derived. For a given partition size, the computationally intractable problem of determining the partition with the smallest OLF is then considered and an agglomerative clustering approach is proposed to overcome the computational limitation. The potential use of this approach to determine decentralized control architectures which yield the best trade-off between the underlying optimality and robustness objectives is demonstrated using an example.

Modeling of Complex Hydronic Systems for Energy Efficient Operation

Energy requirements for heating and cooling of residential, commercial and industrial spaces constitute a major fraction of end use energy consumed. Centralized systems such as hydronic networks are becoming increasingly popular to meet those requirements. Energy efficient operation of such systems requires intelligent energy management strategies, which necessitates an understanding of the complex dynamical interactions among its components from a mathematical and physical perspective. In this work, concepts from linear graph theory are applied to model complex hydronic networks. Further, time-scale decomposition techniques have been employed to obtain a more succinct representation of the overall system dynamics. Lastly, the usefulness of the proposed model for energy efficient operation of the system through advanced control techniques has been discussed.Copyright