This paper investigates and proposes a mitigation strategy for denial-of-service (DoS) cyber-attacks, targeting the most critical distributed energy resource (DER) in an islanded microgrid featuring a high penetration of renewables; the energy storage system (ESS) operating as the isochronous generator that forms and regulates the microgrid voltage and frequency. A rule-based fallback control strategy is proposed to enhance the resiliency of the microgrid to DoS cyber-attacks by managing the ESS state-of-charge in a decentralized manner, such that it can continue operating as the isochronous DER, while dispatching the remaining DERs in a centralized manner. Supplementary control loops are added to the ESS to manage and coordinate, using local frequency signals, with the remaining DERs, whose local controllers are specifically designed to provide frequency support in the event of DoS cyber-attacks. The proposed scheme is applied on a 25 kV islanded microgrid under two configurations, synchronous machine-based and 100% inverter-interfaced, and its effectiveness is validated on a real-time hardware-in-the loop testing platform.

Future distribution systems are faced with more challenges on islanding detection due to the increasing penetration level of inverter-based distributed generators (DGs). Different DG technologies, inverter control as well as other advanced inverter functions such as fault ride through are challenging the capability of islanding detection schemes. On the other hand, for multiple feeders network, topological change of feeders and islanding at adjacent feeder increase the vulnerability of islanding detection devices. Furthermore, available islanding detection schemes are suffering from notable non-detection zones (NDZs) under reduced power mismatches. Therefore, to mitigate these issues, this paper proposes an effective methodology for building decision trees (DTs) based intelligent relay (IR). This methodology utilizes the NDZ boundaries of existing standard relays and applies a comprehensive training/testing strategy, which effectively reduces the NDZ while maintaining a superior dependability and security performance. To validate the applicability of the proposed methodology, the hardware-in-the-loop simulations are realized by programming the generated IR logics into a real commercial relay.

Electric vehicle (EV) is commonly considered as an electric load in a residential energy network. However, the large capacity EV battery can be used as electric storage when the EV is plugged in at home. While model predictive control (MPC) offers an efficient and reliable control mechanism for a home energy management system (HEMS), the uncertainty related to the availability of an EV at home poses an interesting challenge for the MPC optimization strategy. In this article, the thermal comfort and energy management performance of a centralized MPC-based HEMS is presented for such a scenario where an EV is used as a mobile energy storage unit in a home energy network. The MPC-based HEMS simultaneously controls the zone-based heating system consisting of a heat pump and baseboard units along with the energy flow among the different components of the home energy network. This network comprises the home baseload, charging and discharging of the home-battery and the EV battery, in-house solar energy generation and storage. The MPC-based HEMS optimizes the buying/selling of energy from/to the grid based on time of use electricity rates. A Monte-Carlo based uncertainty analysis is presented here to estimate the robustness of the MPC against the randomness in the EV arrival and departure schedule. Finally, the responsiveness of the centralized MPC is showcased using a vehicle to home communication scenario where the owner notifies the home network regarding their arrival within a short interval, along with the preferred indoor temperature upon arrival.

An improved dynamic state estimation scheme that performs estimation for the full plant, (states of generator, exciter field voltage and governor mechanical torque) using intermittent data from phasor measurement unit (PMU) connected at generator terminal is presented. Overall fourth order generator model is assumed in Extended-Kalman Filter (EKF) while first order governors and excitation systems are assumed for simplicity of large scale implementation. State estimation is performed using EKF with random PMU data dropouts and known inputs, i.e. secondary reference signals Pref and Vref provided to power plant by the network control center from economic dispatch. The state estimation scheme has been extended to all generators in network and DSE is performed using a computationally decentralized federation of EKFs at a centralized Phasor Data Concentrator where PMU data are aggregated while dealing with specified stochastic dropout rate. Required modifications have thus made to standard EKF formulation to account for communication channel interruption and inherent delays. Simulation studies performed on the benchmark IEEE 9 and 39 bus system demonstrated performance and resilience of proposed centralized EKF based estimation technique. We also found that Centralized Estimator can lead to improved wide-area instability indices derived from state estimates rather than PMU directly.

This paper presents a formulation to determine the appropriate power dispatch of an energy storage system, whose available energy is dependent on the charging/discharging pattern from previous time periods. The implementation structure is consistent with current dispatch algorithms used in microgrids, and the algorithm can be used in either grid-connected or islanded modes of operation. The proposed approach employs a backcasting algorithm to estimate the net stored energy value, against which the current cost of energy is compared to determine how the storage system should be used to perform arbitrage. The contribution of this paper is a means to include the time-dependent resource in traditional economic dispatch algorithms to reduce the cost of energy in a microgrid while enabling the arbitrage algorithm to continuously adapt to changing market conditions. Results show that the backcasting algorithm is able to reduce the average cost of energy by 8.14% and can reduce the average cost of energy by up to 72.3% of the ideal reduction, as determined by a perfect forecasting dispatch.

With reference to the newly released microgrid standards, design and real-time implementation of a centralized microgrid control system is presented in this article. In the grid-connected mode, the utility grid will provide the voltage and frequency reference at the point of connection. The assets within the microgrid will follow power command references provided by the control system. In the islanded mode, the energy storage system (ESS) can provide the voltage and frequency reference to all other generators. Based on the state-of-charge of the ESS, a rule-based dispatch is proposed, with priority given to diesel generator and then the storage in the middle state of charge range. To alleviate power fluctuations, meet smooth planned islanding requirement, and compensate for the feeder losses ignored in dispatch algorithm, a supplementary slack-bus power control based on closed-loop feedback and first-order filter is proposed. The potential of the storage system in firming short-time power fluctuation and providing long-term load shifting capabilities is exploited. An emergency dispatch function for unplanned islanding considering the speed of response limitation of a diesel generator is also proposed. The proposed control strategy is implemented and tested on a controller hardware-in-the-loop test bench. It demonstrates the capability of the control system to reduce load shedding and renewable curtailment, and to implement power management at the point of interconnection.