This work presents robust virtual inertia control of an islanded microgrid considering high penetration of renewable energy sources (RESs). In such microgrids, the lack of system inertia due to the replacement of traditional generating units with a large amount of RESs causes undesirable influence to microgrid frequency stability, leading to weakening of the microgrid. In order to handle this challenge, the H ∞ robust control method is implemented to the virtual inertial control loop taking into account the high penetration of RESs, thus enhancing robust performance and stability of the microgrid during contingencies. The controller’s robustness and performance are determined along with numerous disturbances and parametric uncertainties. The comparative study between H ∞ and optimal PI-based virtual inertia controller is also presented. The results show the superior robustness and control effect of the proposed H ∞ controller in terms of precise reference frequency tracking and disturbance attenuation over the optimal PI controller. It is validated that the proposed H ∞-based virtual inertia controller successfully provides desired robust frequency support to a low-inertia islanded microgrid against high RESs penetration.

Renewable energy sources (RESs), such as wind and solar generations, equip inverters to connect to the microgrids. These inverters do not have any rotating mass, thus lowering the overall system inertia. This low system inertia issue could affect the microgrid stability and resiliency in the situation of uncertainties. Today’s microgrids will become unstable if the capacity of RESs become larger and larger, leading to the weakening of microgrid stability and resilience. This paper addresses a new concept of a microgrid control incorporating a virtual inertia system based on the model predictive control (MPC) to emulate virtual inertia into the microgrid control loop, thus stabilizing microgrid frequency during high penetration of RESs. The additional controller of virtual inertia is applied to the microgrid, employing MPC with virtual inertia response. System modeling and simulations are carried out using MATLAB/Simulink^® software. The simulation results confirm the superior robustness and frequency stabilization effect of the proposed MPC-based virtual inertia control in comparison to the fuzzy logic system and conventional virtual inertia control in a system with high integration of RESs. The proposed MPC-based virtual inertia control is able to improve the robustness and frequency stabilization of the microgrid effectively.

A power outage brings in economic losses for both the customers and the utilities. Studying these unwanted events and making solid predictions about the outcomes of the interruptions has been an attractive area of interest for the researchers for the last couple of decades. By making use of a customer survey study conducted in Finland, this paper benefits from both the reported cost data collected from customers and from the analytical data that are available and then presents a new hybrid approach to estimate the customer interruption costs of service sector customer segment. Making use of Value Added information of the customers is a common practice for the cost normalization purposes. This paper verifies the approach by comparing the findings of the customer survey and the econometric model suggested here. This study is a unique source in terms of providing a reliable, easy to apply, and a straightforward model for calculating the economic impacts of power outages.Peer reviewe

Estimation of economic impacts of power interruptions in residential customers segment is a challenging and tedious task. The literature presents different methods to come up with sound calculations for these customer interruption costs. This paper makes use of a detailed customer survey study that was conducted in Finland and presents a comparison of five different models: WTA, WTP, direct worth approach, price elasticity approach and a new macroeconomic model. When doing the analysis a total of 1009 customers are divided into three sub-categories regarding the distinct characteristics of power consumptions: households, vacation houses and farm house customers.Peer reviewe