|2015||Qiu J, Dong ZY, Zhao JH, Meng K, Zheng Y, Hill DJ, 'Low carbon oriented expansion planning of integrated gas and power systems', IEEE Transactions on Power Systems, 30 1035-1046A (2015)|
As a clean fuel source, natural gas plays an important role in achieving a low-carbon economy in the power industry. Owing to the uncertainties introduced by increasing utilization of natural gas in electric power system, gas system and electricity system should be planned in an integrated manner. When considering these two systems simultaneously, there are many emerging difficulties, e.g., increased system complexity and risk, market timeline mismatch, overall system reliability evaluation, etc. In this paper, a novel expansion co-planning (ECP) framework is proposed to address the above challenges. In our approach, the planning process is modeled as a mixed integer nonlinear optimization problem. The best augmentation option is a plan with the highest cost/benefit ratio. Benefits of expansion planning considered are reductions in operation cost, carbon emission cost, and unreliability cost. By identifying several scenarios based on statistical analysis and expert knowledge, decision analysis is used to tackle market uncertainties. The operational and economic interdependency of both systems are well analyzed. Case studies on a three-bus gas and two-bus power system, plus the Victorian integrated gas and electricity system in Australia are presented to validate the performance of the proposed framework.
|2015||Huang J, Xue Y, Jiang C, Wen F, Xue F, Meng K, Dong ZY, 'An experimental study on emission trading behaviors of generation companies', IEEE Transactions on Power Systems, 30 1076-1083 (2015)|
The overall performance of emission trading (ET), a market-based emission regulation tool, strongly relies on participants' participation and responses. In order to improve market design, it is important for policy makers to understand the participants' trading behaviors in different market environments. However, human behaviors cannot be easily modeled with conventional analytical methods due to its "bounded rationality" characteristics. In this paper, based on the complementary features between experimental and agent-based computational methods, a hybrid interactive simulation methodology is proposed to solve human behaviors related problems. Human-subjected experiment based on European Union Emissions Trading System price data in 2006 is conducted, the results show that there is no fixed emission trading interval for generation companies, and the strategic behaviors of market participants are observed. Major driving factors of emission trading are categorized into emission price, emission quantity and time related factors, which are in accordance with empirical analysis results on EU ETS 2005-2006 transaction dataset. Furthermore, more human-subjected experiments are conducted under different emission price scenarios to obtain samples for quantitative analysis. Based on thousands of samples obtained, the joint influences of driving factors on emission trading behaviors are analyzed. The quantitative analysis results obtained can reflect the trading patterns of human participants, which provide basis for constructing computer agents that can act as useful substitutes for human participants.
|2015||Luo F, Meng K, Dong ZY, Zheng Y, Chen Y, Wong KP, 'Coordinated operational planning for wind farm with battery energy storage system', IEEE Transactions on Sustainable Energy, 6 253-262 (2015)|
This paper proposes a coordinated operational dispatch scheme for a wind farm with a battery energy storage system (BESS). The main advantages of the proposed dispatch scheme are that it can reduce the impacts of wind power forecast errors while prolonging the lifetime of BESS. The scheme starts from the planning stage, where a BESS capacity determination method is proposed to compute the optimal power capacity and energy capacity of BESS based on historical wind power data; and then, at the operation stage, a flexible short-term BESS-wind farm dispatch scheme is proposed based on the forecasted wind power generation scenarios. Three case studies are provided to validate the performance of the proposed method. The results show that the proposed scheme can largely improve the wind farm dispatchability.
|2015||Luo F, Dong Z, Chen G, Xu Y, Meng K, Chen Y, Wong K, 'Advanced pattern discovery-based fuzzy classification method for power system dynamic security assessment', IEEE Transactions on Industrial Informatics, 11 416-426 (2015)|
Dynamic security assessment (DSA) is an important issue in modern power system security analysis. This paper proposes a novel pattern discovery (PD)-based fuzzy classification scheme for the DSA. First, the PD algorithm is improved by integrating the proposed centroid deviation analysis technique and the prior knowledge of the training data set. This improvement can enhance the performance when it is applied to extract the patterns of data from a training data set. Secondly, based on the results of the improved PD algorithm, a fuzzy logic-based classification method is developed to predict the security index of a given power system operating point. In addition, the proposed scheme is tested on the IEEE 50-machine system and is compared with other state-of-the-art classification techniques. The comparison demonstrates that the proposed model is more effective in the DSA of a power system.
|2014||Zheng Y, Dong ZY, Luo FJ, Meng K, Qiu J, Wong KP, 'Optimal Allocation of Energy Storage System for Risk Mitigation of DISCOs With High Renewable Penetrations', IEEE TRANSACTIONS ON POWER SYSTEMS, 29 212-220 (2014) [C1]|
|2014||Zheng Y, Dong ZY, Xu Y, Meng K, Zhao JH, Qiu J, 'Electric Vehicle Battery Charging/Swap Stations in Distribution Systems: Comparison Study and Optimal Planning', IEEE TRANSACTIONS ON POWER SYSTEMS, 29 221-229 (2014) [C1]|
|2014||Yao W, Zhao J, Wen F, Dong Z, Xue Y, Xu Y, Meng K, 'A multi-objective collaborative planning strategy for integrated power distribution and electric vehicle charging systems', IEEE Transactions on Power Systems, 29 1811-1821 (2014) [C1]|
An elaborately designed integrated power distribution and electric vehicle (EV) charging system will not only reduce the investment and operation cost of the system concerned, but also promote the popularization of environmentally friendly EVs. In this context, a multi-objective collaborative planning strategy is presented to deal with the optimal planning issue in integrated power distribution and EV charging systems. In the developed model, the overall annual cost of investment and energy losses is minimized simultaneously with the maximization of the annual traffic flow captured by fast charging stations (FCSs). Additionally, the user equilibrium based traffic assignment model (UETAM) is integrated to address the maximal traffic flow capturing problem. Subsequently, a decomposition based multi-objective evolutionary algorithm (MOEA/D) is employed to seek the non-dominated solutions, i.e., the Pareto frontier. Finally, collaborative planning results of two coupled distribution and transportation systems are presented to illustrate the performance of the proposed model and solution method. Â© 2014 IEEE.
|2014||Xu Y, Dong ZY, Meng K, Yao WF, Zhang R, Wong KP, 'Multi-objective dynamic VAR planning against short-term voltage instability using a decomposition-based evolutionary algorithm', IEEE Transactions on Power Systems, 29 2813-2822 (2014) [C1]|
Short-term voltage stability is an increasing concern in today's power systems due to the growing penetration of induction motors. This paper proposes a systematic method for optimal placement of dynamic VAR support against short-term voltage instability. The problem is formulated as a multi-objective optimization model minimizing two conflicting objectives: 1) the total investment cost and 2) the expected unacceptable short-term voltage performance subject to a set of probable contingencies. STATCOM is employed for its stronger dynamic VAR support capability. Indices for quantifying the short-term voltage stability and the related risk level are proposed for problem modeling. Candidate buses are selected based on the concept of trajectory sensitivity. Load dynamics are fully considered using a composite load model containing induction motor and other typical components. A relatively new and superior multi-objective evolutionary algorithm called MOEA/D is introduced and employed to find the Pareto optimal solutions of the model. The proposed method is verified on the New England 39-bus system using industry-grade models and simulation tool.
|2014||Yang H, Zhang D, Meng K, Dong ZY, Lai M, 'MULTI-NETWORK COMBINED COOLING HEATING AND POWER SYSTEM SCHEDULING CONSIDERING EMISSION TRADING', PACIFIC JOURNAL OF OPTIMIZATION, 10 177-198 (2014)|
|2014||Yang H, Zhang D, Meng K, Dong ZY, Lai M, 'Multi-network combined cooling heating and power system scheduling considering emission trading', Pacific Journal of Optimization, 10 177-198 (2014) [C1]|
|2013||Li XR, Yu CW, Ren SY, Chiu CH, Meng K, 'Day-ahead electricity price forecasting based on panel cointegration and particle filter', Electric Power Systems Research, 95 66-76 (2013) [C1]|
|2013||Xu Y, Dai Y, Dong ZY, Zhang R, Meng K, 'Extreme learning machine-based predictor for real-time frequency stability assessment of electric power systems', NEURAL COMPUTING & APPLICATIONS, 22 501-508 (2013) [C1]|
|2013||Zhang R, Dong ZY, Xu Y, Meng K, Wong KP, 'Short-term load forecasting of Australian national electricity market by an ensemble model of extreme learning machine', IET Generation, Transmission and Distribution, 7 391-397 (2013) [C1]|
Artificial Neural Network (ANN) has been recognized as a powerful method for short-term load forecasting (STLF) of power systems. However, traditional ANNs are mostly trained by gradient-based learning algorithms which usually suffer from excessive training and tuning burden as well as unsatisfactory generalization performance. Based on the ensemble learning strategy, this paper develops an ensemble model of a promising novel learning technology called extreme learning machine (ELM) for high-quality STLF of Australian National Electricity Market (NEM). The model consists of a series of single ELMs. During the training, the ensemble model generalizes the randomness of single ELMs by selecting not only random input parameters but also random hidden nodes within a pre-defined range. The forecast result is taken as the median value the single ELM outputs. Owing to the very fast training/tuning speed of ELM, the model can be efficiently updated to on-line track the variation trend of the electricity load and maintain the accuracy. The developed model is tested with the NEM historical load data and its performance is compared with some state-of-the-art learning algorithms. The results show that the training efficiency and the forecasting accuracy of the developed model are superior over the competitive algorithms.Â©The Institution of Engineering and Technology 2013.
|2012||Xu Y, Dong ZY, Meng K, Zhao J, Wong KP, 'A hybrid method for transient stability-constrained optimal power flow computation', IEEE Transactions on Power Systems, 27 1769-1777 (2012) [C1]|
|2012||Dong ZY, Meng K, Xu Y, Wong KP, Ngan HW, 'Electricity price forecasting with extreme learning machine and bootstrapping', IEEE Transactions on Power Systems, 27 2055-2062 (2012) [C1]|
|2012||Xu Y, Dong ZY, Xu Z, Meng K, Wong KP, 'An intelligent dynamic security assessment framework for power systems with wind power', IEEE Transactions on Industrial Informatics, 8 995-1003 (2012) [C1]|
|2012||Yao F, Dong ZY, Meng K, Xu Z, Iu HH-C, Wong KP, 'Quantum-inspired particle swarm optimization for power system operations considering wind power uncertainty and carbon tax in Australia', IEEE Transactions on Industrial Informatics, 8 880-888 (2012) [C1]|