• Energy Research

Abstract The increased complexity of electric distribution networks, entails higher requirements on the interoperability between the information and communication technology (ICT) infrastructure and the power system. In current literature, distribution network monitoring methods require reliable operation of the ICT infrastructure, and depend on load estimation accuracy in cases of unavailable measurements. In the low voltage (LV) distribution network, ICT infrastructure is sensible to noise and congestion, and load estimation is challenged by irrational consumer behaviour. Such limitation impede the implementation of currently proposed methods. The conditions are complicated further when considering the impact of emerging data privacy concerns that can prevent utilization of household consumption data. Therefore, this work proposes a novel method for voltage magnitude interval estimation by utilizing the information from existing data acquisition systems in LV distribution networks. The proposed method is based on knowledge of load connection, and estimates the entire radial feeder voltage magnitude conditions from measurements of voltage magnitudes from minimum one node. The proposed method can therefore be implemented on existing ICT infrastructure and effectively keeps the privacy of consumers consumption profiles. The ability of the proposed method to estimate the feeder voltage interval is illustrated by an example and evaluated using Monte Carlo simulations for different meter distribution and accuracy scenarios.

Driven by the Energy Strategy 2050 of Denmark, renewable energy sources (RESs) are increasingly integrated into the Danish power grid. Solar photovoltaic (PV) plants play an important role in this process. This paper conducted a study to investigate the impacts of residential solar PV integration in the distribution grid on voltage security and grid loss based on the 10 kV distribution grid in Bornholm. Three case studies are performed to test three different reactive power control methods, i.e., PF(P), constant PF and constant Q, at different penetration levels. The assessment of the impacts of PV integration and different control methods are done in the DIgSILENT PowerFactory. It was found that PV integration can contribute to reducing the loss of the system, increased overvoltage in buses and overload in transformers, and 40% penetration at the low voltage is considered to be an optimal level based on the result. PF(P) control gives the best performance among all three methods under the current grid codes. With constant PF control, it was found that the system loss can be significantly reduced if the PV systems operate with a power factor of 0.9 leading, which is not the norm of the current Danish grid code.

With the increasing wind power integration, the security and economy of the power system operations are greatly influenced by the intermittency and fluctuation of wind power. Due to the flexible operational modes for charging/discharging, the hybrid energy storage system (HESS) is composed of battery energy storage system and super-capacitor can effectively mitigate the wind power uncertainty. This paper proposes a probabilistic forecasting-based HESS sizing and control scheme to cost-effectively smooth wind power fluctuations. First, probabilistic wind power forecasting is combined with multivariate Gaussian copula to generate temporally correlated wind power scenarios. Then, an adaptive variational mode decomposition (VMD) method is proposed to extract the frequency components of each wind scenario and determine the pre-scheduled power of wind-HESS system adaptively. Finally, a two-stage stochastic optimization model is constructed to determine the capacity and correct the pre-scheduled power of HESS with the objective of minimizing total cost. Case studies based on actual data from a Danish wind farm demonstrate that the proposed HESS sizing and control scheme can significantly reduce the installation cost and operation cost of HESS and prominently smooth wind power fluctuations.

This paper presents a novel approach for realizing coordinated control of Distributed Energy Resources (DERs) based on cloud-hosted and edge-hosted digital twins (DTs) of DERs. DERs are playing an increasingly important role in supporting the frequency regulation of power systems with massive integration of renewable resources. However, due to the significant differences in DERs' capability and characteristics, individual and un-coordinated responses from DERs could lead to a less effective overall response with undesirable traits, e.g. slow response, severe overshoots, etc. Therefore, the coordination of DERs is critical to ensure the desirable aggregated overall response. A major shortcoming of conventional centralized or distributed approaches is their significant reliance on real-time communications. This paper addresses the challenges by the application of DTs that can be hosted in the cloud for the centralized control approach and the edge for the distributed approach to minimize the need for real-time communications, while being able to achieve the overall coordination among DERs. The proposed DT-based coordinated control is validated using a realistic real-time simulation test setup, and the results demonstrate that the DT-based coordinated control can significantly improve the aggregated DERs' response, thus offering effective support to the grid during contingency events.

Synchronous condensers (SCs) play important roles in integrating wind energy into relatively weak power grids. However, the design of SCs usually depends on specific application requirements and may not be adaptive enough to the frequently-changing grid conditions caused by the transition from conventional to renewable power generation. This paper devises a software-defined virtual synchronous condenser (SDViSC) method to address the challenges. Our contributions are fourfold: 1) design of a virtual synchronous condenser (ViSC) to enable full converter wind turbines to provide built-in SC functionalities; 2) engineering SDViSCs to transfer hardware-based ViSC controllers into software services, where a Tustin transformation-based software-defined control algorithm guarantees accurate tracking of fast dynamics under limited communication bandwidth; 3) a software-defined networking-enhanced SDViSC communication scheme to allow enhanced communication reliability and reduced communication bandwidth occupation; and 4) Prototype of SDViSC on our real-time, cyber-in-the-loop digital twin of large-wind-farm in an RTDS environment. Extensive test results validate the excellent performance of SDViSC to support reliable and resilient operations of wind farms under various physical and cyber conditions.

[No abstract available]

The increasing number of photovoltaic (PV) generation and electric vehicles (EVs) on the load side has necessitated an aggregator (Agg) in power system operation. In this paper, an Agg is used to manage the energy profiles of PV generation and EVs. However, the daily management of the Agg is challenged by uncertain PV fluctuations. To address this problem, a robust multi-time scale energy management strategy for the Agg is proposed. In a day-ahead phase, robust optimization is developed to determine the power schedule. In a real-time phase, a rolling horizon-based convex optimization model is established to track the day-ahead power schedule based on the flexibilities of the EVs. A case study indicates a good scheduling performance under an uncertain PV output. Through the convexification, the solving efficiency of the real-time operation model is improved, and the over-charging and over-discharging problems of EVs can be suppressed to a certain extent. Moreover, the power deviation between day-ahead and real-time scheduling is controllable when the EV dispatching capacity is sufficient. The strategy can ensure the flexibility of the Agg for real-time operation.