Design, exergy and exergoeconomic analysis and optimization of a CCHP + TES for the use in a complex building

2020 ◽  
Vol 41 (6) ◽  
pp. 727-744
Author(s):  
Khodadoost Rostami Zadeh ◽  
Seyed Ali Agha Mirjalily ◽  
Seyed Amir Abbas Oloomi ◽  
Gholamreza Salehi
Keyword(s):  
Energy Storage ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Annual Cost ◽  
Combustion Engine ◽  
Operating Cost ◽  
Operating Time ◽  
Primary Energy ◽  
The Public ◽  
Return On Capital

The present paper aims at the optimization and exergy and thermoeconomic analyses of a combined cooling, heat, and power generation system equipped with a thermal energy storage for the use in a residential complex with a gas-fueled internal combustion engine as the prime mover. The system is optimized using the direct search method by minimizing annual cost in two cases of using/not using a thermal energy storage. In case of the use of a thermal energy storage, an engine with a capacity of 2 MW and an operating time of 4000 h are found to be optimal, but when a thermal energy storage is included, an engine with a capacity of 2 MW and an operating time of 5268 hours and a thermal energy storage with a capacity of 18.93 m3 are found to be the optimal options. Both systems are evaluated assuming selling/not selling surplus power to the public power grid. The best case for the performance of the system is to use a thermal energy storage and to sell surplus electricity to the grid. In this case versus the case of excluding the thermal energy storage, primary energy consumption, CO2 emission, operating cost of the system, and power purchase from the public grid are decreased by 20.8, 19.5, 14.3 and 17%, respectively while return on capital is increased by 3.1% resulting in 10.7% higher annual cost of the system.

Near Optimal Model Predictive Control of Thermal Energy Storage

2020 ◽  
Author(s):  
O. A. Qureshi ◽  
P. R. Armstrong
Keyword(s):  
Energy Storage ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
High Reliability ◽  
Operating Cost ◽  
Weather Conditions ◽  
Preliminary Evaluation ◽  
Novel Algorithm

Abstract Efficient plant operation can be achieved by properly loading and sequencing available chillers to charge a thermal energy storage (TES) reservoir. TES charging sequences are often determined by heuristic rules that typically aim to reduce utility costs under time of use rates. However, such rules of thumb are in most cases far from optimal even for this task. Rigorous optimization, on the other hand, is computationally expensive and can be unreliable as well if not carefully implemented. Model-predictive control (MPC) that is reliable, as well as effective, in TES application must be developed. The goal is to develop an algorithm that can reach ∼80% of achievable energy efficiency and peak shifting capacity with very high reliability. A novel algorithm is developed to reliably achieve near optimal control for charging cool storage in chiller plants. Algorithm provides a constant COP (or cost per ton-hour) for 24-hr dispatch plan at which plant operates during most favorable weather conditions. Preliminary evaluation of this novel algorithm has indicated up to 6% improvement in plant annual operating cost relative to the same plant operating without TES. TOU rate used in both cases charges 7.4cents/kWh during off peak hours and 9.8cents/kWh during peak hours (Peak hours are 10 am to 10 pm).

Effect of Design Parameters on the Performance of Thermal Energy Storage Systems

Solar Energy ◽  
2004 ◽  
Author(s):  
Gregor P. Henze
Keyword(s):  
Energy Storage ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Large Scale ◽  
Control Strategies ◽  
Storage System ◽  
Operating Cost ◽  
Energy Storage System ◽  
Design Parameters ◽  
Wide Range

This paper describes simulation-based results of a large-scale investigation of a commercial cooling plant including a thermal energy storage system. A cooling plant with an ice-on-coil system with external melt and a reciprocating compressor operating in a large office building was analyzed under four different control strategies. Optimal control as the strategy that minimizes the total operating cost (demand and energy charges) served as a benchmark to assess the performance of the three conventional controls. However, all control strategies depend on properly selected design parameters. The storage and chiller capacities as the primary design parameters were varied over a wide range and the dependence of the system’s cost saving performance on these parameters was evaluated.

scholarly journals Investigating thermal performance of an ice rink cooling system with an underground thermal storage tank

2017 ◽  
Vol 36 (2) ◽  
pp. 314-334 ◽  
Author(s):  
Hakan Tutumlu ◽  
Recep Yumrutaş ◽  
Murtaza Yildirim
Keyword(s):  
Energy Storage ◽  
Analytical Model ◽  
Thermal Performance ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Storage Tank ◽  
Cooling System ◽  
Operating Time ◽  
Ambient Air ◽  
Coefficient Of Performance

This study deals with mathematical modeling and energy analysis of an ice rink cooling system with an underground thermal energy storage tank. The cooling system consists of an ice rink, chiller unit, and spherical thermal energy storage tank. An analytical model is developed for finding thermal performance of the cooling system. The model is based on formulations for transient heat transfer problem outside the thermal energy storage tank, for the energy needs of chiller unit, and for the ice rink. The solution of the thermal energy storage tank problem is obtained using a similarity transformation and Duhamel superposition techniques. Analytical expressions for heat gain of the ice rink and energy consumption of the chiller unit are derived as a function of inside design air, ambient air, and thermal energy storage tank temperatures. An interactive computer program in Matlab based on the analytical model is prepared for finding hourly variation of water temperature in the thermal energy storage tank, coefficient of performance of the chiller, suitable storage tank volume depending on ice rink area, and timespan required to attain an annually periodic operating condition. Results indicate that operation time of span 6–7 years will be obtained periodically for the system during 10 years operating time.

scholarly journals Integrating Renewable Energy Technologies and Thermal Energy Storage to Support Building Trigeneration Systems: A Multicriteria Approach

2017 ◽  
Vol 5 ◽  
Author(s):  
Eduardo Antonio Pina
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Annual Cost ◽  
Total Annual Cost ◽  
Multi Objective Optimization ◽  
Trade Off ◽  
Renewable Energy Technologies ◽  
Energy Technologies

The present work proposes a multi-objective optimization model to determine the optimal configuration and operation of trigeneration systems including renewable energy technologies (RET) and thermal energy storage (TES). The model minimises the total annual cost and CO2 emissions. Trade-off solutions between both objectives were obtained and different configurations were analysed.

scholarly journals Performance Optimization and Economic Evaluation of CO2 Heat Pump Heating System Coupled with Thermal Energy Storage

2021 ◽  
Vol 13 (24) ◽  
pp. 13683
Author(s):  
Zhihua Wang ◽  
Yujia Zhang ◽  
Fenghao Wang ◽  
Guichen Li ◽  
Kaiwen Xu
Keyword(s):  
Energy Storage ◽  
Heat Pump ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Performance Optimization ◽  
Life Cycle Cost ◽  
Operating Cost ◽  
Heating System ◽  
Hot Water ◽  
Great Resistance

CO2 air source heat pump (ASHP), as a kind of clean and efficient heating equipment, is a promising solution for domestic hot water and clean heating. However, the promotion of CO2 ASHP encounters a great resistance when it is used for space heating; namely, the return water temperature is too high that cased higher throttle loss, which decreases the COP of the CO2 ASHP unit. To solve this problem, a heating system of CO2 ASHP coupled with thermal energy storage (TES) is developed in this work. The simulation model of the studied system is established using TRNSYS software, and the model is verified by experimental data. Additionally, the performance of the studied system is optimized, and its economy is analyzed by life cycle cost (LCC). The results showed that, compared with the system before optimization, the cost of the optimized system increased, the annual operating cost of the system was reduced, and the COP of the system (COPsys) increased by 7.4%. This research is helpful in improving the application of the CO2 ASHP unit in cold server and cold areas.

scholarly journals Using Thermal Energy Storage to Relieve Wind Generation Curtailment in an Island Microgrid

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2851
Author(s):  
Huanhuan Luo ◽  
Weichun Ge ◽  
Jingzhuo Sun ◽  
Quanyuan Jiang ◽  
Yuzhong Gong
Keyword(s):  
Energy Storage ◽  
Wind Power ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Operating Cost ◽  
Electrical Network ◽  
Wind Generation ◽  
Small Scale ◽  
Cost Models ◽  
Real World Data

The uncertainty and intermittency of the available wind resource in nature would potentially cause wind generation curtailment when the flexibility of the integrated power grid is limited, especially in small-scale microgrids for islands. In this paper, an optimal configuration method is proposed to use thermal energy storage (TES) to relieve wind generation curtailment in an island microgrid. The thermal network is modeled along with the electrical network to utilize its regulation capability, while TES is introduced as an additional flexibility resource. The detailed cost models of combined heat and power (CHP) units and TES are presented to realize the objective of minimizing the overall operating cost. The performance of TES in improving wind power utilization is firstly validated by using an electrical boiler (EB) as a benchmark and further analyzed under different scenarios considering the growths of wind power capacity, electrical load, and heat load. The effectiveness of the proposed method is validated using real-world data obtained from the practical island microgrid.

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