Optimization for ice-storage air-conditioning system using particle swarm algorithm

Lee, Wen‐Shing; Chen, Yi –Ting; Wu, T. H.

doi:10.1016/j.apenergy.2008.12.025

Cited by 117 publications

(44 citation statements)

References 11 publications

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“…Suitable selection of the inertia weight x can provide a balance between global and local exploration abilities, thus require less iterations on average to find the optimum. The linearly decrease x-strategy [27][28][29][30] is a kind of setting for many problems.…”

Section: Original Psomentioning

confidence: 99%

A new fuzzy adaptive particle swarm optimization for daily Volt/Var control in distribution networks considering distributed generators

2010

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Section: Original Psomentioning

confidence: 99%

A new fuzzy adaptive particle swarm optimization for daily Volt/Var control in distribution networks considering distributed generators

2010

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“…Other than utilizing building thermal mass, the operation strategies for TES which are derived by mathematical programming, model predictive control (MPC) and reinforcement learning approaches are demonstrated to outperform the conventional control strategy such as chiller-priority and storage-priority strategies [8][9][10][11][12][13][14]. The near-optimal TES control strategy proposed in [9] is comparable to optimal TES control strategy obtained by dynamic programming.…”

Section: Introductionmentioning

confidence: 99%

A data-driven feed-forward decision framework for building clusters operation under uncertainty

2015

Applied Energy

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“…Ma et al [14] introduced an MPC approach to estimate the resulting electricity cost reductions in a university cooling system, using periodic invariant sets and dualstage optimization to tackle feasibility issues with their proposed scheme. Lee et al [15] presented an optimal design of an ice-based TES system, using particle swarm algorithms. This case study used minimal life cycle cost as the objective function to analyze the increase in power consumption and its potential influences on the system's optimization.…”

Section: Introductionmentioning

confidence: 99%

“…Lu et al [17] developed an optimal scheduling strategy for a Zero Carbon Building in Hong Kong, using the MINLP method, reducing 25% of operational energy cost compared with a rule-based strategy. In these studies, different models and algorithms like Genetic Algorithm (GA) [9][11] [20], Particle Swarm Optimization (PSO) [15][21], Mixed-integer Linear Programming (MILP) [18][22] [23], and Mixed-integer Nonlinear Programming (MINLP) [17] [24] were widely used to solve the optimization problem. Two previous reviews introduced these optimization techniques in the context of TES operations [25] [26].…”

Section: Introductionmentioning

confidence: 99%

Data analytics and optimization of an ice-based energy storage system for commercial buildings

Luo

Hong

Li³

et al. 2017

Applied Energy

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Ice-based thermal energy storage (TES) systems can shift peak cooling demand and reduce operational energy costs (with time-of-use rates) in commercial buildings. The accurate prediction of the cooling load, and the optimal control strategy for managing the charging and discharging of a TES system, are two critical elements to improving system performance and achieving energy cost savings. This study utilizes data-driven analytics and modeling to holistically understand the operation of an ice-based TES system in a shopping mall, calculating the system's performance using actual measured data from installed meters and sensors. Results show that there is significant savings potential when the current operating strategy is improved by appropriately scheduling the operation of each piece of equipment of the TES system, as well as by determining the amount of charging and discharging for each day. A novel optimal control strategy, determined by an optimization algorithm of Sequential Quadratic Programming, was developed to minimize the TES system's operating costs. Three heuristic strategies were also investigated for comparison with our proposed strategy, and the results demonstrate the superiority of our method to the heuristic strategies in terms of total energy cost savings. Specifically, the optimal strategy yields energy costs of up to 11.3% per day and 9.3% per month compared with current operational strategies. A one-day-ahead hourly load prediction was also developed using machine learning algorithms, which facilitates the adoption of the developed data analytics and optimization of the control strategy in a real TES system operation.Keywords: thermal energy storage; optimization; data analytics; energy cost saving; heuristic strategy; machine learning 1.IntroductionIn recent decades, peak demand management of commercial buildings has become an active research area. Different strategies for shifting energy loads have been developed to reduce total operating costs without sacrificing the thermal comfort of building occupants. As noted in the report from the International Energy Agency, it is possible to gain an annual savings of $10-$15 billion for the U.S. market through peak demand management [1]. In general, load shifting control can be achieved using three strategies: building thermal mass, thermal energy storage (TES), and phase change materials. Recent reviews present and compare the current status of the three control strategies [2][3]; TES is the most widely used technology in existing air conditioning systems. A statistical study [4] shows that in the 1990s, about 1,500-2,000 units of TES cooling systems were employed in the U.S., of which the ice-based TES systems had the largest proportion of the market, at about 80%-85%. This sizeable market share for TES systems has increased interest in studying their operation.In an ice-based TES system, cooling can be provided to meet the indoor thermal requirement either by directly operating the chiller or by discharging the ice storage. The chille...

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Optimization for ice-storage air-conditioning system using particle swarm algorithm

Cited by 117 publications

References 11 publications

A new fuzzy adaptive particle swarm optimization for daily Volt/Var control in distribution networks considering distributed generators

A new fuzzy adaptive particle swarm optimization for daily Volt/Var control in distribution networks considering distributed generators

A data-driven feed-forward decision framework for building clusters operation under uncertainty

Data analytics and optimization of an ice-based energy storage system for commercial buildings

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