An Overview of Optimal Control for Central Cooling Plants with Ice Thermal Energy Storage

2003 ◽  
Vol 125 (3) ◽  
pp. 302-309 ◽  
Author(s):  
Gregor P. Henze
Keyword(s):  
Optimal Control ◽  
Energy Storage ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Optimal Strategies ◽  
Rate Information ◽  
Utility Rate ◽  
Operating Strategies ◽  
Water Plants ◽  
Chilled Water Plants

This paper surveys past and current research of optimal control for central chilled water plants with ice thermal energy storage. The motivation for thermal energy storage in commercial buildings is provided and common operating strategies for ice storage including their implementation are presented. The concept of optimality serves as the basis for introducing the various approaches to optimal control of thermal energy storage. Optimal strategies minimizing either energy or demand costs, near-optimal rule based control minimizing total cost, comfort-based energy optimal control, and combined optimal sizing and energy cost optimal control are discussed and contrasted with mathematically non-optimal, but heuristically improved operating strategies. Fully optimal control based on perfect knowledge is introduced and subsequent developments of predictive optimal control subject to uncertain weather, load, and utility rate information illustrated. In addition, recent investigations of adaptive optimal reinforcement learning based control are presented.

Cold Thermal Energy Storage

2017 ◽  
pp. 93-123
Author(s):  
Franc Franc Kosi ◽  
Branislav Zivkovic ◽  
Mirko S. Komatina ◽  
Dragi Antonijevic ◽  
Mohamed Abdul Galil ◽  
...  
Keyword(s):  
Energy Storage ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Storage System ◽  
Thermal Storage ◽  
Cooling Load ◽  
Air Conditioning System ◽  
Operational Characteristics ◽  
Calculated Load ◽  
Operating Strategies

The chapter gives an overview of cold thermal energy storage (CTES) technologies. Benefits as well as classification and operating strategies of CTES are discussed. Design consideration and sizing strategies based on calculated load profile for design day is presented. Some recommendation concerning designing of CTES equipment are given. Special attention was paid to the analysis of specific features of heat transfer phenomena in ice storage tank including the assessment of the duration and the rate of ice formation and melting. The methodology of sizing components of the ice thermal storage system included in an air conditioning system for an office building situated in hot wet and dry climate are presented. Based on hourly cooling load calculation that was carried out using Carrier's Hourly Analysis Program, sizing of ice thermal storage system for different operating strategies included full, chiller priority and ice priority storage operation for the design day are presented. Finally, an analysis of some operational characteristics of the system are analyzed.

scholarly journals Integrated Optimal Design and Control of Fourth Generation District Heating Networks with Thermal Energy Storage

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2766 ◽  
Author(s):  
van der Heijde ◽  
Annelies Vandermeulen ◽  
Salenbien ◽  
Helsen
Keyword(s):  
Optimal Control ◽  
Energy Storage ◽  
Optimal Design ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
District Heating ◽  
Energy System ◽  
And Control ◽  
Heating Networks ◽  
Optimal Design And Control

In the quest to increase the share of renewable and residual energy sources in our energy system, and to reduce its greenhouse gas emissions, district heating networks and seasonal thermal energy storage have the potential to play a key role. Different studies prove the techno-economic potential of these technologies but, due to the added complexity, it is challenging to design and control such systems. This paper describes an integrated optimal design and control algorithm, which is applied to the design of a district heating network with solar thermal collectors, seasonal thermal energy storage and excess heat injection. The focus is mostly on the choice of the size and location of these technologies and less on the network layout optimisation. The algorithm uses a two-layer program, namely with a design optimisation layer implemented as a genetic algorithm and an optimal control evaluation layer implemented using the Python optimal control problem toolbox called modesto. This optimisation strategy is applied to the fictional district energy system case of the city of Genk in Belgium. We show that this algorithm can find optimal designs with respect to multiple objective functions and that even in the cheaper, less renewable solutions, seasonal thermal energy storage systems are installed in large quantities.

Feasibility Analysis of Thermal Energy Storage: Differences Using Blended Versus Actual Rate Schedules

2015 ◽  
Author(s):  
Vivek Ramesh ◽  
T. Agami Reddy
Keyword(s):  
Energy Storage ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Energy Use ◽  
Payback Period ◽  
Existing Building ◽  
Rate Structure ◽  
Design Choice ◽  
Operating Strategies ◽  
Reducing Costs

This paper reports on the results of analyzing yearlong monitored 15 min data from the cooling plant of a large university campus consisting of multiple chillers and multiple chilled water Thermal Energy Storage (TES) tanks. The objective of the analysis was to determine whether the addition of another TES tank would be economically justified under the present electric rate structure and cooling load demand of the campus. The analysis was done: (i) using blended on-peak and off-peak energy rates (an approach commonly adopted due to its simplicity for evaluating different system alternatives and operating strategies meant to reduce cost and/or energy use), and (ii) using the actual electric rate structure which includes energy and demand charges. The latter rate structure suggests a 42 year payback, while the former rates predicted a payback period of over 100 years. If the incremental avoided cost of an additional chiller (to meet anticipated increases in cooling loads) is included in the economic analysis, the payback will be greatly reduced from the 42 year payback, and make this option a design choice meriting further investigation. The study also suggests a way of generating indifference plots which provide insights into how future changes in the electric rate structure would impact the payback period. The methodology adopted in this study would serve as a case study example to energy analysts evaluating TES systems as a design option for meeting increasing cooling demand and reducing costs in an existing building or campus facility.

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