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The acceleration of distributed energy resources and carbon pricing policies have compelled utilities to act and to prioritize carbon-constrained infrastructure augmentation in their capital programs. To implement various carbon emission reduction policies, power system transmission planning has become more challenging. The existing energy system will face massive retirement of coal-fired power plants (CFPPs), large scale integration of renewable energy and network expansion. In this paper, an electricity network planning and transformation roadmap, which has two milestones, is put forward. In the first stage, a mathematical model is proposed based on the average cost of carbon emission reduction to realize the cooperation of CFPPs retirement and renewable energy investment. It can help the network carry out the transition from a fossil-fuel dominated system to a low-carbon oriented system. Because of the promising prospect of power-to-gas (P2G) technology, in the second milestone, a method based on carbon emission flow (CEF) is employed to help the power-to-gas stations (P2GSes) to select the construction site and capacity. The gas network constraints are modeled to guarantee that P2GSes can work smoothly without energy flow congestion in both electricity and gas networks. According to the simulation results in case studies, our method can reach the emission reduction target more economically and effectively, and the P2GSes can produce and absorb clean energy.

With the ever-increasing penetration level of renewable generation sources, a modern power system is facing more inevitable uncertainties that could lead to weakly damped oscillations. Detecting coherency among synchronous generators is one of the key steps of situational awareness for a given power system with a very high level of renewable penetration. In this paper, a wide-area measurement system (WAMS) based coherency detection algorithm employing the kernel principal component analysis (KPCA) and clustering analysis based on affinity propagation (AP) is proposed for a power system with extensive penetration of renewable generation sources. First, several trajectory similarity indexes are presented for determining the similarity between the trajectories of any two generators in the center of inertia coordinate. Second, a KPCA-based method is presented to integrate the trajectory similarity indexes for addressing the correlations among multiple indexes. Next, the AP-based clustering analysis method is utilized to detect the coherency among synchronous generators without the need of prespecifying the number of clusters. Finally, Southern China power system and a part of northern China power system with Zhangbei wind farms included, both with very high levels of renewable generation penetration, are utilized to demonstrate the proposed WAMS-based coherency detection methodology, and the application to actual Guangdong power system in south China to verify the applicability and practicality.

Widespread utilization of plug-in electric vehicles (PEVs) would incur more uncertainties, and thus challenge the traditional planning strategies for distribution systems. In this paper, a scenario-based comprehensive expansion planning (SCEP) approach for distribution systems is presented to cope with future uncertainties. This approach can not only take into account the initial investment and operation costs but also consider the expected adaptation cost to other future scenarios, and in this way the flexibility of an initial planning scheme can be achieved. Future scenarios are identified in line with three uncertainties, namely the conventional load levels, penetration levels of PEVs and whether or not with successful implementation of a coordinated charging strategy. The charging profile for dump charging mode has been determined straightforwardly, while that for coordinated charging mode is obtained by solving the proposed day-ahead dispatching model (DDM). Furthermore, a priority-based real time dispatching strategy (PRTDS) is proposed to obtain direct charging instructions for PEVs, and thus to demonstrate the rationality of utilizing the charging profile acquired from DDM in distribution system expansion planning (DSEP). Finally, the performance of the proposed methodology is demonstrated by numerical studies.

Traditional security-constrained unit commitment (SCUC) considers only static security criteria, which may however not ensure the ability of the system to survive dynamic transition before reaching a viable operating equilibrium following a large disturbance, such as transient stability. This paper proposes a tractable mathematical model for transient stability-constrained unit commitment (TSCUC) and a practical solution approach. The problem is modeled without explicit differential-algebraic equations, reducing the problem size to one very similar to a conventional SCUC. The whole problem is decomposed into a master problem for UC and a range of subproblems for steady-state security evaluation and transient stability assessment (TSA). Additional constraints including Benders cut and so-named stabilization cut are generated for eliminating the security/stability violations. The extended equal-area criterion (EEAC) is used for fast TSA and analytically deriving the stabilization cut, wherein multiple contingencies having common instability mode can be simultaneously stabilized by one cut. The proposed approach is demonstrated on the New England 10-machine system and the IEEE 50-machine system, reporting very high computational efficiency and high-quality solutions.

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.

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.

The paper presents a new control strategy to enhance the ability of reactive power support of a doubly fed induction generator (DFIG) based wind turbine during serious voltage dips. The proposed strategy is an advanced low voltage ride through (LVRT) control scheme, with which a part of the captured wind energy during grid faults is stored temporarily in the rotor's inertia energy and the remaining energy is available to the grid while the DC-link voltage and rotor current are kept below the dangerous levels. After grid fault clearance, the control strategy ensures smooth release of the rotor's excessive inertia energy into the grid. Based on these designs, the DFIG's reactive power capacity on the stator and the grid side converter is handled carefully to satisfy the new grid code requirements strictly. Simulation studies are presented and discussed.

Plug-in electric vehicles (PEVs) are increasingly popular in the global trend of energy saving and environmental protection. However, the uncoordinated charging of numerous PEVs can produce significant negative impacts on the secure and economic operation of the power system concerned. In this context, a hierarchical decomposition approach is presented to coordinate the charging/discharging behaviors of PEVs. The major objective of the upper-level model is to minimize the total cost of system operation by jointly dispatching generators and electric vehicle aggregators (EVAs). On the other hand, the lower-level model aims at strictly following the dispatching instructions from the upper-level decision-maker by designing appropriate charging/discharging strategies for each individual PEV in a specified dispatching period. Two highly efficient commercial solvers, namely AMPL/IPOPT and AMPL/CPLEX, respectively, are used to solve the developed hierarchical decomposition model. Finally, a modified IEEE 118-bus testing system including 6 EVAs is employed to demonstrate the performance of the developed model and method.

Managing electricity market risks is crucial for market participants. For electricity price risk management, expectation and standard deviation of price, along with possible occurrence of price spike, need to be assessed in order to support further risk control. In this paper, a hybrid probabilistic assessment method based on adaptive importance sampling (AIS) and sequential importance sampling (SIS) is developed. Improvements on AIS and SIS make the method better suit electricity market problems. Case studies are conducted on an equivalent Australian National Electricity Market (NEM) system. Uncertainties considered include system load, renewable energy output, generator bidding strategy, and outage rate. The proposed method provides much faster estimation of both normal price and price spike probability, meanwhile achieving comparable accuracy as Monte Carlo (MC) simulation results. Sensitivity of its estimation efficiency against different load level is also analyzed, which shows the robustness of the proposed method.

An eigenstructure-based performance index is proposed in this paper to measure the dynamic performance of the system as well as control efforts. Calculation of this index is based on eigenstructure of the closed loop system and the design parameters; it does not rely on control structures. Therefore, this index can be applied for solving structurally constrained control problems. A tuning scheme based on this index is proposed for coordinating power system stabilizers (PSSs) and supplementary damping controllers (SDCs) for flexible AC transmission systems (FACTS) devices to damp inter-area oscillations of systems and to optimize their control efforts under multiple operating conditions. Both PSSs and SDCs utilize control structures as a low order single-input-single-output phase lead-lag compensator. Wide-area signals are employed to upgrade their effectiveness in damping inter-area oscillations. Time delays caused by usage of wide-area signals are also considered in the tuning scheme. Results of simulation on a four-machine two-area system and the New England and New York interconnected system show that the proposed index is effective in measuring dynamic performance of the system and the coordinatedly tuned PSSs and SDCs based on this index can robustly damp inter-area oscillations of systems with optimized control efforts.

This paper addresses the issue of improving the conventional iterative method used for integrated extended equal area criterion (IEEAC) and single machine equivalent (SIME) for multi-swing transient stability limit analysis in power system operation. Effects of the complicated phenomena on the conventional method, i.e., nonmonotonic variation of the stability margin and the isolated stability domain (ISD), are studied. Accordingly, the conventional method is improved for both efficiency and accuracy. With this knowledge, an improved sensitivity-based iterative method for searching multi-swing transient stability limit is proposed. The advantage of this method is that the complicated phenomena are fully considered and their effects are either overcome or avoided as much as possible. Case studies on the ten-generator New England power system are presented to verify the effectiveness and efficiency of this improved method.