This paper summarizes the report prepared by an IEEE PES Task Force. Resilience is a fairly new technical concept for power systems, and it is important to precisely delineate this concept for actual applications. As a critical infrastructure, power systems have to be prepared to survive rare but extreme incidents (natural catastrophes, extreme weather events, physical/cyber-attacks, equipment failure cascades, etc.) to guarantee power supply to the electricity-dependent economy and society. Thus, resilience needs to be integrated into planning and operational assessment to design and operate adequately resilient power systems. Quantification of resilience as a key performance indicator is important, together with costs and reliability. Quantification can analyze existing power systems and identify resilience improvements in future power systems. Given that a 100% resilient system is not economic (or even technically achievable), the degree of resilience should be transparent and comprehensible. Several gaps are identified to indicate further needs for research and development.

This paper presents a methodology for integrated planning of real-life medium-voltage networks based on the utility planning concepts. The research is motivated by the need to develop a methodology that would line-up with utility day-to-day businesses and could be applied in real-life. Its core is a two-stage optimization process, where the first stage solves the static investment optimization and the second stage considers operational problem. A probabilistic decision tree approach is proposed for the solution of the entire problem to consider uncertainties in the planning period. The overall formulation is given first, which is followed by details of the investment model and outlines of the proposed operation planning. The novelty of the investment problem, which determines optimal network reinforcements, is explicit modelling of network security constraints of radially operated networks, whilst considering different operating regimes. Additional novel features include modelling of real-life supply restoration rules through network reconfiguration and optimal placement of new switching devices, as well as consideration of ‘customer flows’ on the network. Connection of new distributed generation and demand centers and construction of circuits on new corridors are also included. Two investment models are formulated as mixed-integer non-linear optimization problems, tested on several MV networks and compared with established methods. The proposed operational problem is solved in two stages, quality-of-supply and operation cost optimization. Computational aspects are also presented.

The paper proposes a probabilistic methodology for minimizing wind spillage and maximizing capacity of the deployed wind generation, whilst improving system reliability. Capacities of the connected wind units are initially determined by using a method developed by the industry. A probabilistic approach is applied for the day-ahead planning to find maximum deployable wind sources so that the prescribed wind spillage is not exceeded. This is done using the optimum power flow, where wind spillages are prioritised with the probabilistic “cost coefficients.” Further improvement of wind energy utilization is achieved by installing FACTS devices and making use of real-time thermal ratings. Two ranking lists are developed to prioritize location of SVCs and TCSCs, and they are then combined into a unified method for best FACTS placement. The entire methodology is realized in two sequential Monte Carlo procedures, and the probabilistic results are compared with the state enumeration ones. Results show improved wind utilization, network reliability, and economic aspects.

Real-time monitoring and control of distribution networks relies on a robust distribution system state estimation (DSSE). The use of pseudo measurements, typical for DSSE, may negatively affect estimation accuracy as their uncertainties are high. Increased integration of intermittent renewable generation makes active distribution networks more prone to sudden state changes. To overcome these challenges, this paper proposes a Variational Bayesian Unscented Kalman Filter (VBUKF). By efficiently adapting the prediction error covariance matrix and measurement noise covariance matrix, VBUKF copes with unpredictable sudden state changes and bad data, as well as unknown measurement noise. The proposed VBUKF makes use of a vector autoregressive process to capture temporal and spatial correlations in system states and improve prediction accuracy. Extensive simulations are conducted on three IEEE test systems with PV generations to demonstrate the performance of the proposed VBUKF in terms of estimation accuracy, convergence speed, numerical stability and scalability. Results obtained are compared with state-of-the-art state estimation algorithms to highlight the advantages of the proposed approach.

This paper summarizes the technical activities of a three-year-long IEEE Task Force (TF) on State Estimation (SE) for Integrated Energy Systems (IES). It presents the formal definition and characteristics of IES, along with the comprehensive discussion on Electric Power Systems (EPS) model, and static and dynamic models associated with heating and natural gas systems. The paper also identifies the barriers of SE for IES, such as estimation modeling, observability analysis, and measurement requirements, together with addressing multi-scale dynamics. An extensive comparative analysis between Integrated Energy Systems–State Estimation (IES-SE) and more established Electric Power System–State Estimation (EPS-SE) is presented. The paper also provides future research needs and directions related to IES-SE.