• Energy Research

The rapid development of the technology of energy conversion is changing the global energy landscape. In this study, a reliability analysis framework for Integrated Energy Systems (IESs) is developed, based on the concept of Integrated Deterministic and Probabilistic Safety Analysis (IDPSA). A bi-directional energy conversion system model is developed to simulate the deterministic evolution of the IES. Then, the dynamic event tree (DET) analysis technique is used to describe the stochastic evolution of IES, based on the physics of IES. Given the scenarios from the DET, the probabilistic safety margin method is used to evaluate the reliability of IES. The contract pressures at delivery points are adopted as critical safety parameters for the evaluation of the safety margins. To reduce the computational burden, Order Statistics, combining the Bracketing and Coverage approaches, are used to obtain the percentiles of the probabilistic distribution of the safety parameters. An application of the developed framework is performed on an IES. The results indicate that the framework can evaluate the reliability of IES from the perspectives of dynamic failure probability and operation performance. Furthermore, the impact of P2G (power to gas) on the energy supply and operation security of the IES is analysed in detai

Abstract In this paper, an integrated power generation system of Kalina cycle system (KCS) and organic Rankine cycle (ORC) with LNG cold energy exploitation and CO2 capture is introduced. The composite circulation system can fully recover and use the medium and low temperature waste heat to reduce the greenhouse gas emissions. To evaluate the system performances, the thermodynamics model was established and the system was simulated by using Aspen HYSYS software. Thermodynamic analysis has been performed to study the effects of turbine inlet temperature, NH3 mass fraction, evaporation pressure and CO2 capture pressure on the KCS/ORC integrated system. The optimal conditions were also investigated and identified. Exergy analysis of the components operated on KCS or ORC has also been demonstrated. The results showed that the optimal values of the network output, thermal efficiency and CO2 captured quantity of the system were respectively 394.685 kW, 53.25% and 0.3 t/tCO2 (0.6 t/tLNG), while the evaluation indexes of the power recovery efficiency and exergy efficiency were respectively 36% and 41.42%. The exergy loss efficiency of each component was examined. It was indicated that the whole system has significant advantages in waste heat recovery.

Abstract Reliability analysis of IESs (Integrated Energy System) is complicated because of the complexity of system topology and dynamics and different kinds of uncertainties. Reliability is often calculated based on statistic methods, which always focus on historical performances and neglect the importance of their dynamics and structure. To overcome this problem, in this paper, a systematic framework for dynamically analysing the real-time reliability of IESs is proposed by integrating different machine learning methods and statistics. Firstly, the bootstrap-based Extreme Learning Machine is developed to forecast the conditional probability distributions of the productions of renewable energies and the energy consumptions. Then, the dynamic behaviour of IESs is simulated based on a stacked auto-encoder model, instead of using traditional mechanism-based simulation models, for improving computational efficiency. Besides, the variables representing the transient properties of natural gas pipeline networks, such as delivery pressures and flow rates, are taken as the indicators for quantifying the energy supply security in natural gas pipeline networks. The time-dependent relationships among these indicators and their statistic correlations are modelled for improving the effectiveness of the analysis results. Finally, the reliability assessment is performed by estimating the probability distribution of each functional state of the target IES. A case study of a realistic bi-directional IES is carried out to demonstrate the effectiveness of the proposed method. The results show that the method is able to effectively evaluate the reliability of IESs, which can provide useful information for system operation and management.

A thermodynamic and economic comparative analysis are presented for waste heat recovery (WHR) from the heavy oil production with steam-assisted gravity drainage (SAGD) process employing organic Rankine cycle (ORC) and Kalina cycle (KC). The liquefied natural gas (LNG) cold energy is employed as the cold source. Thus, a combined cooling heating and power system is proposed. The effect of key parameters on thermodynamic performance is investigated. The results showed that increasing the turbine inlet temperature (TIT), ORC is more appropriate for WHR in SAGD process than KC, but KC provides better energy use and exergy efficiency, while the reverse situation occurs when the evaporation pressure is increased. The compression ratio has little effect on the cold exergy recovery efficiency of the refrigeration cycles. In addition, the total exergy destruction and the total WHR efficiency in the combined SAGD/KC are slightly higher than these in the combined SAGD/ORC. Moreover, for the TIT below 180 °C and the evaporation pressure above 6 MPa, the SAGD/KC can obtain more energy return on investment (EROI) than SAGD/ORC. The results obtained through economic analysis show that the use of the SAGD/ORC is more economical. Through the thermos-economic comparison of the two combined systems, it helps to choose different combined cycles according to the different actual operation, which can facilitate the future engineering applications.