Thermal Energy Storage Control Research Articles

Thermal energy storage control using phase change materials in a rectangular energy storage chamber with metal foam gradient and magnetic field

Considering the low thermal conductivity of phase change materials (PCM) and the slowness of the melting process in the thermal energy storage chamber (TESC), a comprehensive study on the use of magnetic field and porous foam gradient in the phase change process of PCM in a rectangular chamber with a cylinder is presented. The numerical solution is done using the finite volume method. PCM, metal foam and nanoparticles are respectively lauric acids, aluminum and iron oxide. Porosity coefficient changes have been examined in the positive and negative directions of the coordinate axes during melting and compared to a uniform porosity coefficient in the common case. As a result of a constant magnetic field, melting does not take place as much, but when a non-uniform magnetic field is used, the process of melting in the TESC increases by about 35 times, and the intensity of the magnetic field can be used between 0.025 and 0.05 mT. The average thawing fraction of the PCM has augmented by 5.046, 6.86, 7.66 and 7.819 %, respectively, compared to the no-field. The metal foam gradient has an imperative efficacy in enhancing the process of melting and up to about 97 % reduces melting time. When the porosity coefficient is higher near the active wall, the heat transfer and liquifying are quicker. In the chamber, temperature changes of the cylinder can have a momentous efficacy on the thawing process. The greatest effect is observed when the changes of cylinder temperature with time are stepwise. The sensitivity analysis was done based on the Sobol method, which shows that the porosity coefficient has the greatest effect on the melting rate.

Intelligent Control of Thermal Energy Storage in the Manufacturing Sector for Plant-Level Grid Response

Industrial facilities are seeking new strategies that help in providing savings mechanisms for demand charges. Demand charges are the charges incurred by industrial facilities as a result of power usage. Thermal energy storage has advanced significantly with lots of new applications, garnering the interest of many industrial facilities. These applications could be used to shave the industrial facilities’ peak electric demand and reduce their demand charges. This paper aims to demonstrate the efficacy of thermal energy storage in reducing demand charges and highlight new developments in the integration of smart control systems with thermal energy storage. The study compares energy consumption and peak demand for a facility equipped with and without thermal energy storage tanks using a fixed schedule for charging and discharging. Additionally, the paper examines the impact of incorporating a smart controller to determine when to charge and discharge the tank based on the facility’s real-time power usage and a given setpoint. The results indicate cost savings from the use of thermal energy storage tanks under two proposed scenarios, reflected in the reduced cost of power consumption for the studied facility. The incorporation of a smart controller with the thermal energy storage tank in the facility studied could provide estimated savings of 3.3% per year of power consumption charges, without considering the contribution of any incentives. The estimated savings provided by the fixed schedule scenario are 2.7% per year.

Analysing the Economic Viability of Implicit Demand Response Control of Thermal Energy Storage in Hot Water Tanks

Demand-responsive control of electrically heated hot water storage tanks (HWSTs) is one solution, already present in the building stock, to stabilise volatile energy networks and markets. This has been put into sharp focus with the current energy crisis in Europe due to reduced access to natural gas. Furthermore, increasing proportions of intermittent renewable energy will likely add to this volatility. However, the adoption of demand response (DR) by consumers is highly dependent on the economic benefit. This study assesses the economic potential of DR of centralised HWSTs through both an analysis of spot price data and an optimisation algorithm approximating DR control. The methods are applied to a case study apartment building in Norway using current pricing models and examine the effect of the demand profile, electricity prices, heating power and storage capacity on energy cost and energy flexibility. Unit cost savings from DR are closely linked to the variation in unit energy price during the optimisation period. Increasing the storage capacity or the heating power increases the flexibility with a diminishing rate of return. However, increasing storage capacity does not result in cost savings as additional heat losses are greater than the saving from shifting demand, except for during highly volatile electricity price periods. Changing the minimum setpoint temperature improves the cost curve as a greater thermal storage capacity can be achieved without increasing heat loss. Systems utilising a smaller heating power are more economical due to the dominant role of the monthly price related to the peak energy demand of the system.

Dynamic Modelling and Control of Thermal Energy Storage

Thermal energy storage (TES) is a critical element in district heating systems and having a good understanding of its dynamic behaviour is necessary for effective energy management. TES supports heat sources in achieving a steady power supply. Achieving heat and electric load demand translates into a discharging and charging control problem in terms of stored heat energy. To this end, an accurate dynamic model is essential to design effective controllers to improve district heating performance. A thermal dynamic model of a water tank, together with controllers for energy charging and discharging processes, are presented in this paper. The model is based on a computational fluid dynamics approach. It is developed using thermal stratification. The hot or cold-water stream which vertically crosses each tank section is considered to describe heat transfer. This is a non-linear process represented by partial differential equations, which are linearised to obtain a suitable model for control system design. State-space and transfer function representations of the system are obtained. The non-linear model is implemented in MATLAB/Simulink to design a linear controller that regulates the mass flow rate of cold and hot water to fill or empty the tank’s energy according to performance specifications. The design of regulation and tracking controllers is explained. Simulation results show that a good performance in terms of the mass flow rate input demands is achieved with the proposed controllers.

Analysis of Thermal Energy Storage for a Pharmaceutical CompanyAnalyse eines thermischen Energiespeichers für ein pharmazeutisches Unternehmen

Abstract A pharmaceutical facility located in Southern Germany is experiencing a trend of growing cooling loads to be met by the chilled water plant composed of ten chillers of greatly varying cost effectiveness. With a capacity shortfall inevitable, the question arises whether to install an additional chiller or improve the utilization of the existing chillers, in particular those with low operating costs per unit cooling, through the addition of a chilled water thermal energy storage (TES) system. To provide decision support in this matter, an optimization environment was developed and validated that adopts mixed integer programming as the approach to optimizing the chiller dispatch for any load condition, while an overarching dynamic programming based optimization approach optimizes the charge/discharge strategy of the TES system. In this fashion, the chilled water plant optimization is decoupled but embedded in the TES control optimization. The approach was selected to allow for arbitrary constraints and optimization horizons, while ensuring a global optimum to the problem. The results show that a relatively small TES tank provides significant economic and operational benefits. Yet, in order to facilitate long-term supply security, a larger TES tank capacity was decided on and the TES system was constructed in 2008.

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

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.