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With the increasingly widespread of advanced metering infrastructure, electric load clustering is becoming more essential for its great potential in analytics of consumers' energy consumption patterns and preference through data mining. Moreover, a variety of electric load clustering techniques have been put into practice to obtain the distribution of load data, observe the characteristics of load clusters, and classify the components of the total load. This can give rise to the development of related techniques and research in the smart grid, such as demand-side response. This paper summarizes the basic concepts and the general process in electric load clustering. Several similarity measurements and five major categories in electric load clustering are then comprehensively summarized along with their advantages and disadvantages. Afterwards, eight indices widely used to evaluate the validity of electric load clustering are described. Finally, vital applications are discussed thoroughly along with future trends including the tariff design, anomaly detection, load forecasting, data security and big data, etc.

Abstract An electric vehicle (EV) centred ecosystem has not yet been formed, the existing limited statistic data are far from enough for the analysis of EV users’ travel and charge behaviors, which however tends to be affected by many certain and uncertain factors. An experimental economics (EE) based simulation method can be used to analyze the behaviors of key participants in a system. However, it is restricted by the system size, experimental site and the number of qualified human participants. Therefore, this method is hard to be adopted for the behavioral analysis of a large number of human participants. In this paper, a new method combining a questionnaire statistics and the EE-based simulation is proposed. The causal relationship is considered in the design of the questionnaires and data extraction, then a multi-agent modeling integration method is introduced in the EE-based simulation, which enables the integration of causal/statistical/behavioral models into the multi-agent framework to reflect the EV users’ travel willingness statistically. The generated multi-agents are used to replace human participants in the EE-based simulation in order to evaluate EV users’ travel demands in different scenarios, and compare the differences of simulated or measured travel behaviors between potential EV users and internal combustion engine (ICE) vehicle users.

Static security assessment (SSA) is an important procedure to ensure the static security of the power system. Researches recently show that cyber-attacks might be a critical hazard to the secure and economic operations of the power system. In this paper, the influences of false data injection attack (FDIA) on the power system SSA are studied. FDIA is a major kind of cyber-attacks that can inject malicious data into meters, cause false state estimation results, and evade being detected by bad data detection. It is firstly shown that the SSA results could be manipulated by launching a successful FDIA, which can lead to incorrect or unnecessary corrective actions. Then, two kinds of targeted scenarios are proposed, i.e., fake secure signal attack and fake insecure signal attack. The former attack will deceive the system operator to believe that the system operates in a secure condition when it is actually not. The latter attack will deceive the system operator to make corrective actions, such as generator rescheduling, load shedding, etc. when it is unnecessary and costly. The implementation of the proposed analysis is validated with the IEEE-39 benchmark system.

A reasonable islanding strategy of a power system is the final resort for preventing a cascading failure and/or a large-area blackout from occurrence. In recent years, the applications of wide area measurement systems (WAMS) in emergency control of power systems are increasing. Therefore, a new WAMS-based controlled islanding scheme for interconnected power systems is proposed. First, four similarity indexes associated with the trajectories of generators are defined, and the weights of these four indexes are determined by using the well-developed entropy theory. Then, a coherency identification algorithm based on hierarchical clustering is presented to determine the coherent groups of generators. Secondly, an optimization model for determining controlled islanding schemes based on the coherent groups of generators is developed to seek the optimal cutset. Finally, a 16-generator 68-bus power system and a reduced WECC 29-unit 179-bus power system are employed to demonstrate the proposed WAMS-based controlled islanding schemes, and comparisons with existing slow coherency based controlled islanding strategies are also carried out.

Shale gas resources have the potential to significantly contribute to worldwide energy portfolio. A great number shale gas reserves have been identified in many countries. Connections of newly found gas reserves to the existing energy infrastructures are challenging, as many stakeholders and market uncertainties are involved. The proposed co-planning approach is formulated as a mixed integer nonlinear programming problem so as to minimize investments and enhance the reliability of the overall system. We propose a reliability assessment approach that is applicable for the coupled gas and electricity networks. In addition, the IEEE 24-bus RTS and a test gas system are applied to validate the performance of our approach. Based on the simulation results, the novel expansion co-planning approach is a robust and flexible decision tool, which provides network planners with comprehensive information regarding trade-offs between cost and system reliability.

Following the deregulation of the power industry, transmission expansion planning (TEP) has become more complicated due to the presence of uncertainties and conflicting objectives in a market environment. Also, the growing concern on global warming highlights the importance of considering carbon pricing policies during TEP. In this paper, a probabilistic TEP approach is proposed with the integration of a chance constrained load curtailment index. The formulated dynamic programming problem is solved by a hybrid solution algorithm in an iterative process. The performance of our approach is demonstrated by case studies on a modified IEEE 14-bus system. Simulation results prove that our approach can provide network planners with comprehensive information regarding effects of uncertainties on TEP schemes, allowing them to adjust planning strategies based on their risk aversion levels or financial constraints.

Demand side management techniques have drawn significant attentions along with the development of smart grid. This paper proposes a new direct load control (DLC) model for scheduling interruptible air conditioner loads. The model is coordinated with the unit commitment and economic dispatch to minimize the total operation cost over the whole dispatch horizon. The network constraints are also considered in the model. To ensure the thermal comfort of the occupants, we are among the first to incorporate the advanced two-parameter thermal inertia dynamical model of customer houses into the DLC model to calculate the indoor temperature variation. This paper also proposes a distributed imperialist competitive algorithm to effectively solve the model. The simulation studies prove the efficiency of the proposed methodology.