Power Distribution System Blackstart Restoration Using Renewable Energy

Abstract

Integrating renewable energy sources into the grid not only reduces global carbon emissions, but also facilitates distribution system (DS) blackstart restoration. This process leverages renewable energy, inverters, situational awareness and distribution automation to initiate blackstart at the DS level, obtaining a fast response and bottom-up restoration. In this Review, we survey the latest technological advances for DS blackstart restoration using renewable energy. We first present mathematical models for distributed energy resources (DERs), network topology, and load dynamics. We then discuss how the situational awareness can help improve restoration performance through real-time monitoring and forecasting. Next, the DS blackstart restoration problem, including objectives, constraints, and existing methodologies for decision-making are provided. Lastly, we outline remaining challenges, and highlight the opportunities and future research directions.

Figures (3)

• Figure 1: Evolution of blackstart restoration. In traditional power systems, blackstart is initiated by large thermal power plants, which supply power through a step-up transformer to the transmission system. The power flows through transmission lines to substation transformers, which then energize distribution feeders in a top-down restoration sequence. Finally, industrial, commercial, and residential loads are gradually restored. In modern power systems, blackstart restoration using distributed energy resources (DERs) follows a bottom-up approach. DERs first restore loads nearby, then expand their coverage to bring more DERs and loads back online.
• Figure 2: Modeling framework for distribution system blackstart restoration. a | Distributed energy resources (DERs) model: DERs participating in blackstart include, photovoltaics, and wind turbines and energy storage systems. Grid-forming inverters operate in voltage source mode, initiating blackstart, while grid-following inverters operate in current source mode, injecting power once the network is energized. b | Network model: The network model represents the grid topology. It captures power flow dynamics, switch status, and the behavior of protection devices such as fuses and reclosers. c | Load model: Load restoration is influenced by cold load pickup, which accounts for inrush currents and increased demand after re-energization. Loads are categorized as critical and non-critical groups. d | Situational awareness: Advanced metering infrastructure and phasor measurement units provides real-time monitoring of nteworks. State estimation and fault location detection help determine the system's operational status.
• Figure 3: Methodology for distribution system blackstart restoration. DS blackstart restoration is addressed through analytical and learning-based approaches, both relying on historical and real-time data inputs. Analytical approaches formulate the problem into an optimization framework, using mixed-integer linear/nonlinear programming, stochastic optimization, and robust optimization to determine the optimal cranking path, switching sequences, and distributed energy resource (DER) dispatch under uncertainty. Control-based approaches, such as model predictive control, and Markov decision process dynamically adjust restoration decisions using real-time system feedback. Learning-based approaches leverage machine learning to enhance decision-making. Supervised learning predicts load recovery behaviors, while unsupervised learning clusters restoration scenarios. Reinforcement learning models blackstart as a Markov decision process, where an agent learns optimal restoration sequences by maximizing predefined rewards. The outputs of these methodologies include the restoration plan, such as cranking path, microgrid formation, system performance metrics, such as restored load percentage, voltage and frequency stability, and DER utilization, such as energy storage system state of charge, and inverter operation modes.