The increasing penetration of renewable energy sources (RESs) has led to the proliferation of microgrids (MGs) in the power system. Recently, the concept of multimicrogrid (MMG) systems has come into prominence due to the economic benefits accrued through the sharing of resources between the constituent MGs. The uncertainties caused by the penetration of RESs necessitate the usage of advanced optimization procedures to manage the MMG system. This article proposes a robust optimization (RO) framework to determine the day-ahead schedule of an MMG system. Unlike the existing works in the literature, the proposed RO framework preserves the nonanticipativity in reserve scheduling. The proposed RO framework also includes a cooperative bidding-based trading scheme to facilitate the sharing of energy and reserves between the constituent MGs in the MMG system. The results highlight the economic benefits obtained through the sharing of resources between the constituent MGs in an MMG system. Furthermore, the results also demonstrate that the proposed nonanticipative RO framework performs better in terms of robustness when compared with the existing RO frameworks for MMG systems in the literature.

With the proliferation of enabling smart grid technologies, industries are increasingly looking to reduce their electricity costs through the adoption of renewable energy sources and efficient load management strategies. In this context, the realization of lower electricity costs requires the design of efficient energy management systems (EMSs). An EMS needs to factor in the unique operational requirements of the industry for which it is designed. Consequently, there has been a lot of research interest in designing EMSs for various industrial applications. However, the existing EMS formulations and models are not suitable for a shipyard drydock. Shipyard drydocks may be treated as grid-connected microgrids containing pump loads, interruptible loads, critical loads and heterogeneous generation sources. This paper proposes three modules, which can constitute a shipyard drydock energy management system (SEMS). A load forecasting module generates short term and medium term load forecasts using historical load demand data and ship arrival schedules as inputs. A multilayer feed-forward neural network with backpropagation is used to perform the load forecasting. A contracted capacity optimization module uses the medium term load forecast to find the optimal contracted capacity for the drydock. The short term load forecast and the optimal contracted capacity are used by an optimal scheduling module to reduce the total electricity cost incurred by the shipyard drydock. Case studies performed using data from a local shipyard demonstrate the efficacies of the proposed load forecasting and optimal scheduling modules in improving the accuracies of the load forecasts and reducing the overall electricity cost, respectively.

This paper focuses on the identification of islanding and power quality (PQ) disturbances in microgrids using signal processing techniques such as wavelet transform (WT) and independent component analysis (ICA). WT is known to be a suitable approach for detecting various disturbances such as PQ events, islanding, transients and harmonics due to better multiresolution analysis. However, it is observed that WT is susceptible to increased noise levels or transients in the voltage signal extracted at the point of common coupling (PCC). Therefore, ICA is proposed to find the significant components of the voltage signal which will help in identifying the disturbances more accurately. A comparative analysis, both qualitative and quantitative, is presented in this paper to demonstrate the superior detection capabilities of the proposed ICA approach when compared with the WT approach under noisy scenarios. The proposed technique is validated in real time using OPAL-RT's OP5600 to demonstrate its feasibility for practical applications.

This paper studies the impact of false data injection (FDI) attacks on automatic generation control (AGC), a fundamental control system used in all power grids to maintain the grid frequency at a nominal value. Attacks on the sensor measurements for AGC can cause frequency excursion that triggers remedial actions, such as disconnecting customer loads or generators, leading to blackouts, and potentially costly equipment damage. We derive an attack impact model and analyze an optimal attack, consisting of a series of FDIs that minimizes the remaining time until the onset of disruptive remedial actions, leaving the shortest time for the grid to counteract. We show that, based on eavesdropped sensor data and a few feasible-to-obtain system constants, the attacker can learn the attack impact model and achieve the optimal attack in practice. This paper provides essential understanding on the limits of physical impact of the FDIs on power grids, and provides an analysis framework to guide the protection of sensor data links. For countermeasures, we develop efficient algorithms to detect the attack, estimate which sensor data links are under attack, and mitigate attack impact. Our analysis and algorithms are validated by experiments on a physical 16-bus power system test bed and extensive simulations based on a 37-bus power system model.

Simulations for the quadratically-constrained model predictive control (qc-MPC) with power system linear models are studied in this work. In qc-MPC, the optimization is imposed with two additional constraints to achieve the closed-loop system stability and the recursive-feasibility simultaneously. Instead of engaging the traditional terminal constraint for MPC, both constraints in qc-MPC are imposed on the first control vector of the MPC control sequence. As a result, qc-MPC has the potential for further extension to the control of network centric power systems. The algorithm of qc-MPC has been developed in a previous paper. Here, simulation studies with small-signal linear models of three typical power systems are presented to demonstrate its efficacy. We also develop a computational strategy for the decentralized static state-feedback control using the same quadratic dissipativity constraint as of the qc-MPC. Only state constraints are considered in the state feedback design. A comparison is then provided in the simulation study of qc-MPC relatively to the constrained-state feedback control.