طراحی هزینه بهینه سیستم خنک کننده ذخیره سازی یخ با استفاده از تکنیک های برنامه ریزی خطی مخلوط عدد صحیح در طرح های مختلف تعرفه های برق

The increasing costs of energy encourage the development of cost-efficient building cooling systems. Based on a thermal model of a commercial building and its cooling system, the cost-optimal design of a vapor compression system and an incorporated ice storage is determined. Special emphasis is placed on the refrigeration machine and its interaction with the thermal capacity of the building. The implementation as a mixed integer linear programming problem allows the evaluation of the optimal system operation for an entire year in approximately 50 s on a desktop computer. Based on these results, the design of the optimal systems is determined for various electricity tariff schemes. Therefore, a model for variable electricity rates is introduced including costs for control reserve power. Compared to the cooling system without storage, the system including an optimally designed ice storage achieves lifetime cost reductions of approximately 8% by reducing the operational costs and the investment costs for the downsized refrigeration machine.

The growing number of power plants based on renewable energy with their intermittent production asks for new control policies of the future smart power grid. Demand-side management using financial incentives is a promising solution to reduce the usage of expensive control reserve power and to prevent an overload of the power grid. Buildings in general, and particularly their cooling systems, are responsible for high peak loads. Their associated costs of operation account for a considerable share of the total lifetime costs. As a consequence, their operating costs are highly sensitive to increasing energy prices. The integration of thermal energy storages, such as ice storage systems, is a promising solution to reduce the operating costs and the peak power consumption. Several previous studies have already made important contributions to the optimal design and control of cooling systems with ice storage. In [1], Kintner-Meyer and Emery presented an optimal design for a cooling system including cold storage facilities. Their model-based operating strategy includes a precooling of the building structure. The optimal control is based on a nonlinear model. In [2], MacPhee and Dincer provide a good introduction to the topic of thermal energy storage. Their focus lies on the performance assessment of different ice storage systems in terms of energy efficiency. In [3], Ma et al. present a model predictive control approach for a university cooling system and estimate the resulting electricity cost reductions at 24.5%. In [4], Henze et al. compare four different control strategies using optimal control strategies as a benchmark. In [5], Lee et al. focus on the optimal design of an ice storage system using particle swarm algorithms. Their objective is to determine the optimal system configuration while minimizing the life cycle costs and CO2 emissions. In [6], Stuhlenmiller and Koenigsdorff showed the optimal design for providing the grid with control reserve power. In this study special attention is paid to the thermal modeling of the building, the ice storage system and the refrigeration machine. Fig. 1 shows the system configuration investigated. Due to the linear modeling approach of the total cooling system including the thermal capacity of the building structure, the optimal design and control problem can be solved for an entire year. The costs are then extrapolated to the lifetime of the cooling system. Full-size image (26 K) Fig. 1. Cooling system schematics. Figure options The paper is structured as follows. First, the thermal model of the building are presented, including all of the important heat gains, the vapor compression system (VCS), and the ice storage system (ISS). The simplifications introduced to linearize the model are shown next. Finally, the model is used to derive optimal designs under various electric tariff schemes.

A comprehensive investigation of the optimal cooling system under various electricity tariff schemes is performed. In order to determine the optimal system design and operation, thermal models of a large office building, an ice storage system and a refrigeration machine are developed. Based on this accurate but still computationally efficient MILP model, the optimal control strategy for a given system design is solved in less than one minute on a normal desktop computer. The cost saving potential of ice storage system is successfully shown. Furthermore, ice storage systems are shown to improve the ability of the cooling system to respect the comfort constraints of the building temperature. The cost reductions of ice storage systems are primarily achieved through lowered electricity costs, while the additional expenditures for the ice storage system and the savings due to a reduced capacity of the refrigeration machine approximately balance. By adding an ice storage system, the total costs are reduced by 11% under the two-level electricity tariff scheme, while the cooling power capacity of the vapor compression system is lowered by 26%. The analysis of the optimal cooling system operation reveals that a cost minimization is achieved by equalizing the cooling power production using the ice storage during peak cooling loads. However, shifting the cooling load completely to the low tariff regime is not an optimal solution since large cooling devices with high investment costs would be necessary. Based on spot market data, grid loading factors, taxes, and control reserve market data, a variable electricity price scheme is developed. Including an ice storage system, cost reductions of 5.7% are achieved under this tariff scheme. The analysis further reveals that cooling systems make optimal use of the control reserve market by producing a considerable amount of the cooling energy during times of low or negative electricity prices. However, the main target of including ice storage systems is to equalize the cooling power production, to reduce the peak power consumption, and to downsize the refrigeration machine. This target is diametrically opposed to making use of low and negative electricity prices, where large refrigeration machine capacities are needed.