Optimal self‐healing strategy for microgrid islanding

Abstract

Renewable resource based microgrids provide reliable and cost-effective electricity with low carbon emissions. The flexibility of operating in grid-connected or islanded modes enables a microgrid to serve loads reliably. In the case of unexpected events happening to the main grid, the microgrid will isolate itself and operate in islanded mode to prevent any adversary impacts. The availability of renewable generation in the microgrid has significant impacts on the islanding strategy and different scenarios need to be considered. This study proposes a comprehensive microgrid self-healing strategy under different circumstances. The proposed strategy encompasses generation re-dispatch, network reconfiguration, and load shedding. The microgrid self-healing problem is formulated as a mixed-integer quadratic programming problem, which provides a globally optimal solution to facilitate smooth islanding of the microgrid. A modified Consortium for Electric Reliability Technology Solutions microgrid is used to conduct simulation under various scenarios. The simulation results demonstrate the efficiency of the proposed self-healing strategy in minimising costs of load shedding and generation with optimal switching actions.

Review of Service Restoration for Distribution Networks

“…However, it does not study the optimal coordinated operation of microgrids for optimal service restoration. A two-layer framework is designed in [92] to coordinate operation of microgrids in the case of one or multiple faults. However, it does not consider network reconfiguration.…”

Amultimicrogridenergy management model implementing an evolutionary game‐theoretic approach

“…This work involved multiple computations, and it satisfies only a certain number of requirements. The excess load conditions were served by self‐healing strategy, which was designed to select a MG in accordance with the economic condition 45 . The load was served with an economical generator, which considers only the cheapest where other qualified power generators were ignored.…”

Improving primary frequency response in networked microgrid operations using multilayer perceptron‐driven reinforcement learning

“…For this application, the selection of MLP is motivated by the need for a simplified neural-network architecture, which benefits the subsequent RL algorithm by reducing the computation time. The input layer consists of eight neurons: θ ∈ ℝ 6 consists of three-phase voltage magnitude and phase angle measurements at given time t, s t ∈ ℝ consists of the state (frequency) measurement at time t, and a t ∈ ℝ consists of the designed control action at time t. Furthermore, three hidden layers were selected with neuron configuration (20,10,5), and the final output layer consists of one neuron, to accommodate the desired output s t + Δt , which is the state (frequency) evolution at time t + Δt. Each layer in this MLP has a 'relu' (rectified linear unit) activation function, where 'relu' is defined as the mapping σ: x → y where σ x = max (0, x).…”

“…Individual microgrids offer resilience benefits when they can serve electricity demand during a bulk power system outage following extreme events [2][3][4][5][6][7]. The benefits of individual microgrids can be expanded by interconnecting multiple microgrids to share resources, especially if the bulk power system is unavailable for an extended period [8,9].…”