Qianyao Xu

Balance of Power: Toward a More Environmentally Friendly, Efficient, and Effective Integration of Energy Systems in China

To fulfill the Chinese governments targets for energy conservation and emission reductions, significant efforts to increase efficiency and reduce emissions in the energy system have been made by developing combined heat and power plants, expanding transmission, and incorporating renewables. These elements are not always compatible with each other, however. Renewables in particular face difficulties being integrated into the energy system, and a significant portion of this generation is often curtailed, in particular generation from wind. This article describes the work currently being done in China to move toward better energy system integration, including certain institutional and regulatory changes, continental-scale grid connection, microgrids, and storage. Further opportunities for flexible interaction between electricity and heat along with carbon capture and storage and the potential for increased gas supply from shale resources are also discussed.

Carbon Emission Flow in Networks

As the human population increases and production expands, energy demand and anthropogenic carbon emission rates have been growing rapidly, and the need to decrease carbon emission levels has drawn increasing attention. The link between energy production and consumption has required the large-scale transport of energy within energy transmission networks. Within this energy flow, there is a virtual circulation of carbon emissions. To understand this circulation and account for the relationship between energy consumption and carbon emissions, this paper introduces the concept of “carbon emission flow in networks” and establishes a method to calculate carbon emission flow in networks. Using an actual analysis of Chinas energy pattern, the authors discuss the significance of this new concept, not only as a feasible approach but also as an innovative theoretical perspective.

A Short-Term Wind Power Forecasting Approach With Adjustment of Numerical Weather Prediction Input by Data Mining

This paper proposes a novel short-term wind power forecasting approach by mining the bad data of numerical weather prediction (NWP). Todays short-term wind power forecast (WPF) highly depends on the NWP, which contributes the most in the WPF error. This paper first introduces a bad data analyzer to fully study the relationship between the WPF error with several new extracted features from the raw NWP. Second, a hierarchical structure is proposed, which is composed of a K-means clustering-based bad data detection module and a neural network (NN)-based forecasting module. In the NN module, the WPF is fully adjusted based on the output of the bad data analyzer. Simulations are performed comparing with two other different methods. It proves that the proposed approach can improve the short-term wind power forecasting by effectively identifying and adjusting the errors from NWP.

Modelling and Simulating the Spatio-Temporal Correlations of Clustered Wind Power Using Copula

Modelling and simulating the wind power intermittent behaviour are the basis of the planning and scheduling studies concerning wind power integration. The wind power outputs are evidently correlated in space and time and bring challenges in characterizing their behaviour. This paper provides a methodology to model and simulate the clustered wind power considering its spatio- temporal correlations using the theory of copula. The sampling approach captures the complex spatio- temporal connections among the wind farms by employing a conditional density function calculated using multidimensional copula function. The empirical study of real wind power measurement shows how the wind power outputs are correlated and how these correlations affect the overall uncertainty of clustered wind power output. The case study validates the simulation technique by comparing the simulated results with the real measurements.

A Game Theoretical Pricing Mechanism for Multi-Area Spinning Reserve Trading Considering Wind Power Uncertainty

The rapid development of wind power has led to an increased demand for spinning reserve in power systems today. However, one of the most severe challenges to Chinas power systems is the mismatch between wind power installation capacity and the capability for supplying spinning reserve within each independently operated provincial power system. Coordinating the spinning reserve across multiple areas would providentially improve the accommodation of wind power. This paper proposes a game-theoretical model for spinning reserve trading between provincial systems that treat spinning reserve as a commodity. Based on the incomplete information, the trading price is calculated by satisfying the Bayesian Nash equilibrium, and then the trading quantity is determined. This ensures that both the buyer and the seller are able to maximize their expected profit. Case studies are performed using a 2-bus interconnected system and a 3-area IEEE RTS system. The results show that the proposed model is valid and effective.

A Probabilistic Method for Determining Grid-Accommodable Wind Power Capacity Based on Multiscenario System Operation Simulation

When conducting the wind power (WP) planning, it is very important for electric power companies to evaluate the penetration limit of the grid-accommodable WP. This paper proposes a probabilistic method for determining grid-accommodable WP capacity based on the multiscenario analysis. Typical power system operation scenarios are generated from the combination of different WP scenarios and demand scenarios. A power system operation simulation model is proposed and implemented to the generated scenarios. The operation results are further used as the basis of the proposed probabilistic method. The validity and effectiveness of the new method are demonstrated in two cases, i.e., the IEEE 39-bus test system and a real large power system in China, respectively.

Anti-disaster transmission expansion planning considering wind power integration using ordinal optimization

With the rapid development of various renewable energy sources, a sustainable power system planning scheme is required throughout the world. For a power grid with large-scale wind power integration, it becomes critical to achieve a better performance to supply electricity in disaster circumstances, such as ice-snow. This paper proposes an anti-disaster transmission expansion planning model, and introduces the use of ordinal optimization theory to solve this combinatorial explosion problem. The ordinal optimization focuses on obtaining a good enough solution using a softened optimization object. Finally, the IEEE 39 benchmark bus system is studied and the results prove that the proposed method is valid and efficient.

Day-ahead battery scheduling in microgrid considering wind power uncertainty using ordinal optimization

This paper introduces the Ordinal Optimization (OO) theory into the microgrid operation, considering the wind power uncertainty. An energy balance model is established to obtain a day-ahead battery scheduling. Comparing with the stochastic optimization and the robust optimization, the OO method has the advantages of neither requiring a huge computation burden, nor resulting in an unexpectedly high operating cost. Case studies on different algorithms have been done in the paper. The results show that the OO method outperforms the stochastic and robust solutions, with a more reasonable operating cost while without compromising the microgrid reliability.

Ordinal Optimization Theory Based Planning for Clustered Wind Farms Considering the Capacity Credit

Wind power planning aims to locate and size wind farms optimally. Traditionally, wind power planners tend to choose the wind farms with the richest wind resources to maximize the energy benefit. However, the capacity benefit of wind power should also be considered in large-scale clustered wind farm planning because the correlation among the wind farms exerts an obvious influence on the capacity benefit brought about by the combined wind power. This paper proposes a planning model considering both the energy and the capacity benefit of the wind farms. The capacity benefit is evaluated by the wind power capacity credit. The Ordinal Optimization (OO) Theory, capable of handling problems with non-analytical forms, is applied to address the model. To verify the feasibility and advantages of the model, the proposed model is compared with a widely used genetic algorithm (GA) via a modified IEEE RTS-79 system and the real world case of Ningxia, China. The results show that the diversity of the wind farm enhances the capacity credit of wind power.