• Energy Research

Abstract Reliable gas supply for minimum risk of supply shortage and minimum power demand for low energy cost are two fundamental objectives of natural gas pipeline networks. In this paper, a multi-objective optimization method is developed to trade-off reliability and power demand in the decision process. In the optimization, the steady state behavior of the natural gas pipeline networks is considered, but the uncertainties of the supply conditions and customer consumptions are accounted for. The multi-objective optimization regards finding operational strategies that minimize power demand and risk of gas supply shortage. To quantify the probability of supply interruption in pipeline networks, a novel limit function is introduced based on the mass conservation equation. Then, the risk of interruption is calculated by combining the probability of interruption and its consequences, measured in utility terms. The multi-objective optimization problem is solved by the NSGA-II algorithm and its effectiveness is tested on two typical pipeline networks, i.e., a tree-topology network and a loop-topology network. The results show that the developed optimization model is able to find solutions which effectively compromise the need of minimizing gas supply shortage risk and reducing power demand. Finally, a sensitivity analysis is conducted to analyze the impact of demand uncertainties on the optimization results.

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.

Abstract Integrated Energy Systems (IES) become more and more important for the sustainability in energy system development and for promoting the application of clean energy technologies. System complexity and various uncertainties interaction make it difficult to maintain a reliable energy supply. In this paper, a systematic method is proposed for energy supply reliability analysis in complex Integrated Energy Systems (IES). The developed method integrates stochastic modeling, supply capacity analysis and reliability evaluation, which is able to give more comprehensive knowledge of the ability of the IES for satisfying the energy needs under different, coupled uncertainties. Firstly, stochastic models are developed for each unit in IES, including renewable energy, customer demands and components in natural gas pipeline networks and electric power grids, according to their characteristics. Then, a two-stage optimization model is developed for simulating the operation strategies of IESs, and calculating the supply capacities under randomly generated scenarios. Finally, the reliability of supply is evaluated based on the results of the random simulations. The developed method is used to analyze the supply reliability of an assumed IES, to test its effectiveness. The results show the ability to provide valuable information from multiple perspectives including system, individual customer and resource allocation, for design, extension and management of IES.

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 an integrated energy system (IES), the operating state of each energy subsystem changes relatively frequently, which can seriously threaten the security of IES operation. A systematic data-driven approach is proposed for detecting anomalies and analyzing the dynamics of IES vulnerability. Firstly, an anomaly detection method is introduced to determine whether there are anomalies in the system operation. The method can be set up even if the data labels for discriminating the anomalies are unknown, often the cause in practice. Secondly, a method of complex network phase theory is proposed to model information propagation among IES nodes representative of the IES physical entities. Complex network models can then be constructed to describe the system behavior in different operating conditions and over different time horizons. The degree centrality, betweenness centrality, and closeness centrality are used as indications to analyze changes in IES vulnerability. Finally, a method is proposed to identify the critical points of the IES from the point of view of its vulnerability. The new approach is applied to analyze the vulnerability of an IES in Spain. The results show that the proposed methods allow revealing system anomalies, vulnerability and weaknesses. Outcomes from an analysis by these methods can be used by managers to take defensive measures in advance for preventing and mitigating the impact of potential factors and threats on the IES.

Abstract Different energy systems become highly connected to provide better flexibility. However, this change poses new challenges for system management considering the diversity of demands, complexities of the energy networks, uncertainties, etc. This work develops a smart Supply-Demand Side Management method to overcome these challenges. The main objectives of this Supply-Demand Side Management framework are improving system efficiency and smoothing energy load, through flexible supply planning and dynamic pricing. Firstly, the customer response analysis method is proposed by combining the Deep Learning model and the economic model. Then, the energy network simulation model is used to coordinate the Supply-Demand Side Management strategies and the overall energy system capacity. A method is proposed to introduce the compressibility of natural gas in the management framework to offset the uncertain disturbances. Finally, a multi-objective decision method is developed to find the optimal strategy. The results of the application on a typical integrated energy system show that the proposed method can reduce the energy load fluctuation by 4%–8% under different planning horizons, and improve the system efficiency by reducing energy loss and increasing the profitability. The results also present a possibility of the development toward resilient Integrated Energy Systems by managing the buffer capacity of natural gas pipeline networks.

International audience