Jiaojiao Dong

Distribution System State Estimation (DSSE) infers the states (e.g., voltage phasors) using measurements from the electric distribution grid. As the distribution network becomes more active due to the increasing penetration of Distributed Energy Resources (DER), a deeper investigation into conventional DSSE approaches is needed. This review paper focuses on state-of-the-art techniques and challenges for DSSE in the context of emerging concepts, such as new sensor technologies, data-driven approaches, and high DER penetration.

Achieving clean energy goals will require significant advances in regard to addressing the computational needs for next-generation renewable-dominated power grids. One critical obstacle that lies in the way of transitioning today’s power grid to a renewable-dominated power grid is the lack of a faster-than-real-time stability assessment technology for operating a fast-changing power grid. This paper proposes an artificial intelligence (AI) -based method that predicts the system’s stability margin information (e.g., the frequency nadir in the frequency stability assessment and the critical clearing time (CCT) value in the transient stability assessment) directly from the system operating conditions without performing the conventional time-consuming time-domain simulations over detailed dynamic models. Since the AI method shifts the majority of the computational burden to offline training, the online evaluation is extremely fast. This paper has tested the AI-based stability assessment method using multiple dispatch cases that are converted and tuned from actual dispatch cases of the Western Electricity Coordinating Council (WECC) system model with more than 20,000 buses. The results show that the AI-based method could accurately predict the stability margin of such a large power system in less than 0.2 milliseconds using the offline-trained AI agent. Therefore, the proposed method has great potential to achieve faster-than-real-time stability assessment for practical large power systems while preserving sufficient accuracy.

Power system simulations with long-term data typically have large time steps, varying from one second to a few minutes. However, for PV inverter semiconductors in grid-connected applications, the minimum thermal stress cycle occurs over the fundamental grid frequency (50 or 60 Hz). This requires the time step of the fatigue simulation to be approximately 100 μs. This small time step requires long computation times to process yearly power production profiles. In this paper, we propose a fast fatigue simulation for inverter semiconductors using the quasi-static time-series simulation concept. The proposed simulation calculates the steady state of the semiconductor junction temperature using a fast Fourier transform. The small thermal cycling during a switching period and even over the fundamental waveform is disregarded to further accelerate the simulation speed. The resulting time step of the fatigue simulation is 15 min, which is consistent with the solar dataset. The error of the proposed simulation is 0.16% compared to the fatigue simulation results using the complete thermal stress profile. The error of the proposed method is significantly less than the conventional averaged thermal profile. A PV inverter that responds to a transactive energy system is simulated to demonstrate the use of the proposed fatigue simulation. The proposed simulation has the potential to cosimulate with system-level simulation tools that also adopt the quasi-static time-series concept.

In contrast with conventional microgrids (MGs) with fixed boundaries, a smart and flexible MG with dynamic boundary is introduced in this paper. Such a MG can dynamically change its boundary by picking up or shedding load sections of a distribution feeder depending on its available power, leading to more flexible operation, better utilization of renewables, smaller size of energy storage system, higher reliability, and lower cost. To achieve a flexible MG, the main challenges in MG design are addressed, including recloser placement, MG asset sizing considering resilience, system grounding design, and protection system design. Meanwhile, a hierarchical structure is employed to design and implement the MG controller. On top of the functions defined in IEEE 2030.7-2018, a few new functions, e.g., online topology identification and PQ balance, are added, while the planned/unplanned islanding and reconnection functions are enhanced. The controller is implemented on a CompactRIO, a general-purpose hardware platform provided by National Instruments (NI), and tested on a controller hardware-in-the-loop setup based on an OPAL-RT real-time simulator and a reconfigurable power electronic converter-based hardware testbed. The test results have validated the performance of the developed controllers. Such a flexible MG and its controller have been deployed at a municipal utility, and part of the controller’s functions have been tested on-site.

Non-utility owned distributed energy resources (DERs) are mostly untapped currently, but they can provide many grid services such as voltage regulation and service restoration, if properly controlled, and can improve the distribution system's reliability when coordinated with utility-owned assets such as self-healing control and microgrids. This paper integrates transactive energy control into the distribution system reliability evaluation to quantitatively assess the impact of non-utility owned DERs on reliability improvement. A transactive reactive power control strategy is designed to incentivize the DERs to provide reactive power support for improving voltage profiles thus enabling additional customer load restoration during an outage. Also, an operational sequence to coordinate the non-utility owned DERs with the utility owned self-healing control and utility owned microgrids is designed and integrated into the service restoration process with the operational constraints guaranteed by checking the three-phase unbalanced power flow for post-fault network reconfiguration. The reliability indices are then calculated through a Monte Carlo simulation. The transactive reactive power control strategy is tested on a four-feeder distribution system operated by Duke Energy in the U.S. Results demonstrate that the non-utility owned DERs with the transactive control improve the reliability of both the system and critical loads by more than 30%.

Multiple severe forced oscillation events have recently been observed across the North American interconnections and around the world. These forced oscillations have caused power swings, limited power transfer capability, damaged equipment, and persisted indefinitely until the driving source was located and removed. This paper proposes a comprehensive method to mitigate forced oscillations. Once a forced oscillation is detected, a new source location algorithm can be used to locate the source based on the oscillation mode angle without requiring system topology information. If the source cannot be quickly located and removed, a control strategy can be activated to modulate the active power of utility-scale inverter-based battery energy storage systems (BESSs) to reduce the energy of forced oscillations to a safe level and allow sufficient time for locating the exact source. The proposed source location algorithm is validated using measurements collected during the January 11, 2019 forced oscillation event in North America and other actual grid events, while the proposed control strategy is verified using the Eastern Interconnection dynamic model under the replicated January 11, 2019 forced oscillation event. The simulation results demonstrated that the proposed source location algorithm can accurately identify the forced oscillation source, and the proposed control strategy can significantly reduce forced oscillation energy.

PV inverters can provide reactive power while generating active power. An ongoing microgrid implementation at Duke Energy actively engages non-utility PVs to generate/absorb reactive power in support of ancillary services to increase microgrid resiliency during extreme events. PV systems are requested to provide reactive power support: 1) in response to grid voltage variation to better regulate the local voltage; or 2) in response to utility incentives, such as following Transactive Energy System (TES) incentives. However, providing ancillary services might shorten the lifetime expectation of PV inverter semiconductors. This paper summarizes the potential impacts on a PV inverter semiconductor's lifetime when providing ancillary services. The analysis presented in this research work shows that providing reactive power support will increase the mean junction temperature and the junction temperature variation of the inverter diodes. This increased junction temperature will eventually lead to shorter diode lifetime. The lifetime estimation of semiconductors is briefly reviewed. The power losses of PV inverter semiconductors are derived as a support analysis to the junction temperature calculation. In addition, the impact of filtering inductor on the semiconductor current distribution is discussed. The theoretical analysis presented in this research work is supported by simulation results.

This paper proposes a novel method for solving and tracing power flow solutions with changes of a loading parameter. Different from the conventional continuation power flow method, which repeatedly solves static AC power flow equations, the proposed method extends the power flow model into a fictitious dynamic system by adding a differential equation on the loading parameter. As a result, the original solution curve tracing problem is converted to solving the time domain trajectories of the reformulated dynamic system. A non-iterative algorithm based on differential transformation is proposed to analytically solve the aforementioned dynamized model in form of power series of time. This paper proves that the nonlinear power flow equations in the time domain are converted to formally linear equations in the domain of the power series order after the differential transformation, thus avoiding numerical iterations. Case studies on several test systems including a 2383-bus system show the merits of the proposed method.

This paper proposes a novel method for solving and tracing power flow solutions with changes of a loading parameter. Different from the conventional continuation power flow method, which repeatedly solves static AC power flow equations, the proposed method extends the power flow model into a fictitious dynamic system by adding a differential equation on the loading parameter. As a result, the original solution curve tracing problem is converted to solving the time domain trajectories of the reformulated dynamic system. A non-iterative algorithm based on differential transformation is proposed to analytically solve the aforementioned dynamized model in form of power series of time. This paper proves that the nonlinear power flow equations in the time domain are converted to formally linear equations in the domain of the power series order after the differential transformation, thus avoiding numerical iterations. Case studies on several test systems including a 2383-bus system show the merits of the proposed method.

Wind power generation is hierarchically organized and can be aggregated/disaggregated based on geography and electrical network structure. Forecasts are required at all levels of such a hierarchy for different operational requirements, which are also known as “hierarchical forecasts”. In this paper, we propose a new method to produce hierarchical wind power forecasts which performs better than the conventional top-down or bottom-up method. At first, wind power generation at all levels of the hierarchy is independently forecasted using state-of-the-art techniques. Then, a least squares regression model is used to optimally reconcile these base forecasts. Adjusted forecasts are as close as possible to base forecasts but also satisfy the requirement of the aggregate consistency (i.e., the lower-level forecasts add up exactly to the higher-level forecasts). Case studies validate the effectiveness of the proposed approach for very short-term wind power forecasts. The accuracy improvement of reconciled forecasts over benchmarks is confirmed at all levels of the hierarchy, regardless of forecast horizons and hierarchical structures.