Nonintrusive load monitoring (NILM) is crucial for extracting patterns of electricity consumption of household appliance that can guide users’ behavior in using electricity while their privacy is respected. This study proposes an online method based on the transient behavior of individual appliances as well as system steady-state characteristics to estimate the operating states of the appliances. It determines the number of states for each appliance using the density-based spatial clustering of applications with noise (DBSCAN) method and models the transition relationship among different states. The states of the working appliances are identified from aggregated power signals using the Kalman filtering method in the factorial hidden Markov model (FHMM). Thereafter, the identified states are confirmed by the verification of system states, which are the combination of the working states of individual appliances. The verification step involves comparing the total measured power consumption with the total estimated power consumption. The use of transient features can achieve fast state inference and it is suitable for online load disaggregation. The proposed method was tested on a high-resolution data set such as Labeled hIgh-Frequency daTaset for Electricity Disaggregation (LIFTED) and it outperformed other related methods in the literature.

With the continuous development of the spot market, in the multi-stage power market environment with the day-ahead market and right market, the study associated with the portfolio of energy storage devices requires that attention should be paid to transmission congestion and power congestion. To maximize the profit of energy storage and avoid the imbalance of power supply and consumption and the risk of node price fluctuation caused by transmission congestion, this paper presents a portfolio strategy of energy storage devices with financial/physical contracts. First, the concepts of financial/physical transmission rights and financial/physical storage rights are proposed. Then, the portfolio models of financial contract and physical contract are established with the conditional value-at-risk to measure the risks. Finally, the portfolio models are verified through the test data of the Pennsylvania-New Jersey-Maryland (PJM) electric power spot market, and the comparison between the risk aversion of portfolios based on financial/physical contract with the portfolio of the market without rights. The simulation results show that the portfolio models proposed in this paper can effectively avoid the risk of market price fluctuations.

As a form of hybrid multi-energy systems, the integrated energy system contains different forms of energy such as power, thermal, and gas which meet the load of various energy forms. Focusing mainly on model building and optimal operation of the integrated energy system, in this paper, the dist-flow method is applied to quickly calculate the power flow and the gas system model is built by the analogy of the power system model. In addition, the piecewise linearization method is applied to solve the quadratic Weymouth gas flow equation, and the alternating direction method of multipliers (ADMM) method is applied to narrow the optimal results of each subsystem at the coupling point. The entire system reaches its optimal operation through multiple iterations. The power-thermal-gas integrated energy system used in the case study includes an IEEE-33 bus power system, a Belgian 20 node natural gas system, and a six node thermal system. Simulation-based calculations and comparison of the results under different scenarios prove that the power-thermal-gas integrated energy system enhances the flexibility and stability of the system as well as reducing system operating costs to some extent.

This paper proposes a data-driven topology identification method for distribution systems with distributed energy resources (DERs). First, a neural network is trained to depict the relationship between nodal power injections and voltage magnitude measurements, and then it is used to generate synthetic measurements under independent nodal power injections, thus eliminating the influence of correlated nodal power injections on topology identification. Second, a maximal information coefficient-based maximum spanning tree algorithm is developed to obtain the network topology by evaluating the dependence among the synthetic measurements. The proposed method is tested on different distribution networks and the simulation results are compared with those of other methods to validate the effectiveness of the proposed method.

Smart buildings have been proven to be a kind of flexible demand response resources in the power system. To maximize the utilization of the demand response resources, such as the heating, ventilating and air-conditioning (HVAC), the energy storage systems (ESSs), the plug-in electric vehicles (PEVs), and the photovoltaic systems (PVs), their controlling, operation and information communication technologies have been widely studied. Involving human behaviors and cyber space, a traditional power system evolves into a cyber-physical-social system (CPSS). Lots of new operation frameworks, controlling methods and potential resources integration techniques will be introduced. Conversely, these new techniques urge the reforming requirement of the techniques on the modeling, structure, and integration techniques of smart buildings. In this paper, a brief comprehensive survey of the modeling, controlling, and operation of smart buildings is provided. Besides, a novel CPSS-based smart building operation structure is proposed, and the integration techniques for the group of smart buildings are discussed. Moreover, available business models for aggregating the smart buildings are discussed. Furthermore, the required advanced technologies for well-developed smart buildings are outlined.

More flexibility is desirable with the proliferation of variable renewable resources for balancing supply and demand in power systems.Thermostatically controlled loads (TCLs) attract tremendous attentions because of their specific thermal inertia capability in demand response (DR) programs. To effectively manage numerous and distributed TCLs, intermediate coordinators, e.g., aggregators, as a bridge between end users and dispatch operators are required to model and control TCLs for serving the grid. Specifically, intermediate coordinators get the access to fundamental models and response modes of TCLs, make control strategies, and distribute control signals to TCLs according the requirements of dispatch operators. On the other hand, intermediate coordinators also provide dispatch models that characterize the external characteristics of TCLs to dispatch operators for scheduling different resources. In this paper, the bottom-up key technologies of TCLs in DR programs based on the current research have been reviewed and compared, including fundamental models, response modes, control strategies, dispatch models and dispatch strategies of TCLs, as well as challenges and opportunities in future work.