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Hybrid electric vehicle power systems (VPSs) comprise of limited energy resources and variable load requirements. The functional status of components and subsystems that make up the VPS is critical to the overall system operation. The degradation and critical operating states of components affect system efficiency and reliability differently. Therefore, monitoring of the functional status (referred to as functional situational awareness (FSA)) of components, subsystems and system is needed for reliable and efficient operation of VPS. In this study, VPS-FSA is inferred hierarchically at three levels of complexity, namely, at the component, subsystem and system level. In each of these levels, two types of FSA are inferred to qualify and quantify the functional states of components, subsystems and system. Component-FSA is based on currents and temperatures measurements of the components. Subsystem-FSA and System-FSA are derived as fusion of Component-FSA and Subsystem-FSA, respectively. Type 1-FSA is inferred using reasoning and threshold values. Type 2-FSA is derived using more complex relationships of temperature and current states. Typical FSA results are presented for a VPS for a standard drive cycle and long-term effects on the VPS operation are shown over several drive cylces.

With the high proliferation of solar power, curtailments and higher capacity reserves are required for a reliable power system operation. However, system operators can tap into demand flexibility to maintain reliability affordably. Temperature-controlled loads (TCL) have higher flexibility in demand response due to their frequency of operation, power rating, and tolerance in the desired operating region. In this study, a TCL demand flexibility quantification is presented using temperature measurements and consumer preferences, and predictions of TCL demand flexibility and solar power generation are used to improve demand response (DR) reliability. The utility can leverage predictions to issue DR requests considering resource adequacy and operational costs. Consumers are formed as a virtually connected community, and an aggregator facilitates the utility in providing situational intelligence and distributing DR requests among consumers. A distributed DR management framework is proposed based on demand flexibility to (a) simplify the optimization and (b) improve optimality. Typical results show power consumption reduction during peak reduction and emergency DR requests and power consumption with low variability during capacity firming requests compared to individual thermostat controls. Two indices to measure DR reliability and consumer comfort are defined, and results are presented for different DR requests.

Abstract The power distribution system has increasing importance and complexity as a result of the exponential growth in the adoption of smart grid technologies. The ability to model the power distribution system is critical to ensure a smooth transition to a sustainable power system. This study presents a distributed data‐driven framework based on Cellular Computational Networks (CCN) for power distribution system modelling where the CCN framework facilitates for system decomposition. The learning in CCN is distributed and asynchronous, thus adaptive models can be developed. The computational engine of the CCN cells is based on data‐driven, physics‐driven, or a hybrid approach. The CCN‐based distribution system modelling secures the privacy and security of the sensitive utility information, thus allowing third‐party application providers access to system models and behaviours. The application of a CCN‐based power flow model is illustrated on a modified IEEE 34 test system. Typical results show the suitability of the new approach in modelling the sample distribution system, as well as its enhanced performance when compared with the centralised modelling approach.

Abstract PV plant power excursions can have adverse implications on grid frequency. This phenomenon is observable due to inherently uncertain cloud transients across a local PV plant. Hence, provision of decision‐based controllers for centralized power inverters becomes imperative for supporting local grid operations. Such controllers can be improved to better counteract minutes‐based PV power deviations from its stable equilibrium. Thus, grid frequency deviations require further investigation at PV plant point of interconnection to the grid. In this research, single and spatially distributed utility‐scale PV plants operation is studied on a real‐time power system simulator, under fast‐changing meteorological conditions at different PV site loading levels (PPV−ref). Software‐in‐the‐loop Monte Carlo simulation is conducted and an empirical approach is proposed for characterizing minutes‐based variations in grid frequency originating from PV plant operation, that is, power fluctuations at different PPV−ref. The power−frequency curve obtained at the PV site can be incorporated in form of an empirical frequency droop function in characteristics curve of adjacent auxiliary power source(s). A prominent feature of this adaptive frequency droop is that it considers PV site loading levels during different hours, giving it leverage over common practice constant droop(s). A hardware‐in‐the‐loop platform is presented allowing field derivation of adaptive frequency droop curves using hardware PMU time‐series data analytics.

Power system steady-state security relates to its robustness under a normal state as well as to withstanding foreseeable contingencies without interruption to customer service. In this study, a novel cellular computation network (CCN) and hierarchical cellular rule-based fuzzy system (HCRFS) based online situation awareness method regarding steady-state security was proposed. A CCN-based two-layer mechanism was applied for voltage and active power flow prediction. HCRFS block was applied after the CCN prediction block to generate the security level of the power system. The security status of the power system was visualized online through a geographic two-dimensional visualization mechanism for voltage magnitude and load flow. In order to test the performance of the proposed method, three types of neural networks were embedded in CCN cells successively to analyze the characteristics of the proposed methodology under white noise simulated small disturbance and single contingency. Results show that the proposed CCN and HCRFS combined situation awareness method could predict the system security of the power system with high accuracy under both small disturbance and contingencies.