The concern of energy price hikes and the impact of climate change because of energy generation and usage forms the basis for residential building energy conservation. Existing energy meters do not provide much information about the energy usage of the individual appliance apart from its power rating. The detection of the appliance energy usage will not only help in energy conservation, but also facilitate the demand response (DR) market participation as well as being one way of building energy conservation. However, energy usage by individual appliance is quite difficult to estimate. This paper proposes a novel approach: an unsupervised disaggregation method, which is a variant of the hidden Markov model (HMM), to detect an appliance and its operation state based on practicable measurable parameters from the household energy meter. Performing experiments in a practical environment validates our proposed method. Our results show that our model can provide appliance detection and power usage information in a non-intrusive manner, which is ideal for enabling power conservation efforts and participation in the demand response market.

In order to evaluate system frequency regulation ability of BESS, we propose a system equivalent model that can reconstruct the system environment using frequency characteristics. This equivalent model is designed to reflect the relationship between the characteristics of various generator units and the system frequency, and each parameter is parameterized by the actual frequency and the demand power. For the validity of the model, the output power of model and the output power of optimal power flow was compared and verified. In the case study, we verified that we were able to evaluate contribution of Frequency Regulation BESS using the model in actual grid and mentioned various applications.

With the seriousness of environmental problems, people begin to use electric vehicles(EVs) instead of traditional vehicles, which can reduce carbon dioxide emissions. But a large number of electric vehicles plug-in grid will arise overload, voltage deviation issue, harmonic distortion, etc. which affect the regular operation of the power grid. Seriously, it may cause blackouts in the entire power grid. Therefore, it is essential for real-time detection of grid operation safety. In this paper, we propose a sensitivity analysis of active power and voltage at the same time to identify the impact of electric vehicle charging on the different load profile. The simulation results strongly suggest that when the EV penetration rate at a specific value, it will increase the deviation of active power and voltage and influence the electricity utilisation by users. Based on these results, we can specifically recommend them about the management of charging strategic of EVs.

Risk in a power system’s ability to survive imminent disturbances without recourse to low operational cost and non-interruptive energy delivery remains the responsibility of every grid operator. Intermittencies in renewable energy and dynamic load variations influence the quality of power supply. The sudden changes affect the system frequency, compromising the reliability of the system grid; generation response to frequency regulation is momentous in such an incident. Slower response or smaller reserve capacity may cause a power shortage. This paper proposes a novel predictive scheme for a short-term operational reliability evaluation for system operations planning. The proposed method evaluates the operational reliability of system frequency whiles considering high renewable power penetration and energy storage system incorporation. Required energy generations, and other grid parameters, are modelled as stochastic inputs to the framework. We formulate a reliability index as a frequency distribution considering system frequency control dynamics and processes. The IEEE Reliability Test System (RTS) is used to prove the efficacy of the proposed model.

In a distribution system with a fleet of EVs, various charging loads can easily change the load profile of the system and affect components such as transformer and feeders in a short time period. Thus, the conventional adequacy assessments of the power network do not explicably address these issues. Hence, a compendious model is needed for the reliability evaluation of the power networks with high penetration of EVs. The goal of this paper is to develop a panoramic adequacy assessments index, which addresses these concerns in a comprehensive but concise manner. This paper proposes a framework for adequacy assessments of EVs on both distribution and national grid networks. Based on this framework, a new EV Adequacy Assessments (EAA) index will be developed. Various simulation studies are carried out to verify the efficacy of the proposed assessment methods. The simulation results show that the proposed approach is effective in enhancing both local and global adequacy evaluation of the power network.

In this work, we provide a new short-term reliability index that accounts for practical grid properties. The proposed index addresses the system frequency reliability and controllability of the grid and its deviation over a short time period. In the formulation, demand, renewable source, battery and generators are incorporated in stochastic manner. Using the equilibrium of demand and supply, and the physical constraints of automatic generation control (AGC), a model is developed, from which the system frequency distribution is obtained. From the contrived system frequency statistical function, referred to as Frequency Reliability Distribution Function (FRDF), various criteria can be developed for short-term reliability. The developed FRDF along with the pertaining metrics are utilized for some case studies with IEEE reliability test system.