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This paper develops a resilience analysis framework to study the fault ride-through capability of direct current (DC) microgrids in unknown denial of service (DoS) cyber incidents. DoS can be a frequent threat to DC microgrids with advanced controllers that hinge on active information exchanges: it can paralyze data communications and cause control ineptness or even system instability. Furthermore, we show that temporal DoS incidents render the DC microgrid cyber-physical topology and parameters time-varying and cause them to jump between faulty conditions. As existing DC microgrid stability analysis results are primarily developed from time-invariant systems, they might not be valid for the DoS interrupted systems. In this paper, we seek to study whether a DC microgrid is resilient against time-varying unknown DoS incidents. The problem is formulated as a stability analysis problem for a system with randomly switching dynamics. Scalable conditions are developed to provide provable resilience guarantees. In addition, we exploit the special structure of the conditions to yield quantified resilience measure by convex optimization techniques. The measure can be used for the evaluation of the resilience of different DC microgrid design against DoS incidents. We demonstrate the effectiveness of the proposed work using simulation case studies. [ABSTRACT FROM AUTHOR]

In view of Internet of Energy, advanced operational optimization in microgrid energy management system (MEMS) is expected to be scalable to accommodate various participants, support plug-and-play, and optimize energy utilization. Based on potential game, this paper presents a fully distributed operational optimization for MEMS with high penetration of renewable energy and demand response. After analyzing the microgrid and potential game, the establishment and proof of an exact potential game model is presented in detail. Then the best-strategy-response iterative algorithm is proposed to find the game Nash equilibrium in a distributed way. Finally, the proposed approach is simulated in an islanded microgrid to verify its efficiency and feasibility. The significances of this paper are as follows. First, the scheme and algorithm are fully distributed. All heterogeneous individuals are treated as independent decision-making entities without a central coordinator/processor. Second, the optimization process is open and dynamic. The convergence is achieved with no limits to type or number of players, even if the process of execution is corrupted with delay or losses in communication information. Third, the consistency between individual rationality and overall importance is guaranteed, and individual best-strategy-response behaviors necessarily improve overall optimality. [ABSTRACT FROM AUTHOR]

This paper focuses on developing an appropriate combinatorial optimization technique for solving the optimal sizing problem of hybrid ac–dc microgrids. A novel two-stage iterative approach is proposed. In the first stage, a meta-heuristic technique based on a tailor-made genetic algorithm is used to tackle the optimal sizing problem, while, in the second, a non-linear solver is deployed to solve the operational problem subject to the obtained design/investment decisions. The proposed approach, being able to capture technical characteristics such as voltage and frequency through a detailed power flow algorithm, provides accurate solutions and, therefore, can address operational challenges of microgrids. Its capability to additionally capture contingencies ensures that the proposed sizing solutions are suitable both during normal operation and transient states. Finally, the genetic algorithm provides convergence of the model with relative computational simplicity. The proposed model is applied to a generalizable microgrid comprising of ac and dc generators and loads, as well as various types of storage technologies in order to demonstrate the benefits. The load and natural resources data correspond to real data. [ABSTRACT FROM AUTHOR]

The all-electric ship (AES) usually employs battery energy storage systems (ESSs) in the shipboard microgrid. However, the battery-only storage usually experiences frequent deep discharging or charging to meet the sudden load variations in a voyage, which may lead to significant degradation of battery lifetime. This paper, hybridizes two types of ESSs and proposes a two-step multi-objective optimization method for hybrid ESS (HESS) management. The first step regulates the HESS with the onboard diesel generators to simultaneously optimize both the economic and environmental objectives, and the second step is to split the active power of HESS into two individual ESSs for minimizing the battery cycle degradation. The first step is formulated as a bi-level optimization model through constraint decomposition. Then, a normal boundary intersection method combining with the column-and-constraint generation algorithm is developed to solve the proposed model. Extensive simulations demonstrate that the HESS can effectively resolve the power-density shortage of the battery-only system, and its integration into AES is able to extend the battery lifetime and improve both the economic and environmental indices. [ABSTRACT FROM AUTHOR]

This paper proposes a new optimization tool based on the cross-entropy (CE) method to assess security-constrained optimal power flow (SCOPF) solutions. First, the corresponding SCOPF stochastic problem is defined so that the optimum solution is interpreted as a rare event to be reached by a random search. Second, the CE method solves this new problem efficiently by making adaptive changes to the probability density function according to the Kullback–Leibler distance, creating a sequence of density functions that guides the search in the direction of the theoretically degenerate density at the optimal point. Different types of density functions are tested in order to cope with discrete variables present in the SCOPF problem. Two test systems, namely the IEEE 57 bus and the IEEE 300 bus, are used to evaluate the effectiveness of the proposed method in terms of solution quality and computational effort. Comparisons carried out with reference algorithms in the literature demonstrate that the CE method is capable of finding better solutions for the SCOPF problem with fewer evaluations. [ABSTRACT FROM AUTHOR]

It is essential to improve the resiliency of power distribution systems (PDS) given the increase in extreme weather events, number of malicious threats, and consumers’ need for higher reliability. This paper provides a formal approach to evaluate the operational resiliency of PDS and quantify the resiliency of a system using a code-based metric. A combination of steady state and dynamic simulation tools is used to determine the resiliency metric. Dynamic simulation tools help analyzing the impact of short-term events, which might affect operational resiliency in the long term. A dynamic optimization algorithm for changing operating criteria to increase the sustainability of the most critical loads has been proposed. The proposed theoretical approach is validated using a simple PDS model and simulation results demonstrate the ability to quantify the resiliency using the proposed code-based metric. The time-dependent quantification of resiliency has been demonstrated on a test system of two connected Consortium of Electric Reliability Technology Solutions (CERTS) microgrids. [ABSTRACT FROM AUTHOR]

To reduce energy costs and emissions of microgrids, daily operation is critical. The problem is to commit and dispatch distributed devices with renewable generation to minimize the total energy and emission cost while meeting the forecasted energy demand. The problem is challenging because of the intermittent nature of renewables. In this paper, photovoltaic (PV) uncertainties are modeled by a Markovian process. For effective coordination, other devices are modeled as Markov processes with states depending on PV states. The entire problem is Markovian. This combinatorial problem is solved using branch-and-cut. Beyond energy and emission costs, to consider capital and maintenance costs in the long run, microgrid design is also essential. The problem is to decide device sizes with given types to minimize the lifetime cost while meeting energy demand. Its complexity increases exponentially with the problem size. To evaluate the lifetime cost including the reliability cost and the classic components such as capital and fuel costs, a linear model is established. By selecting a limited number of possible combinations of device sizes, exhaustive search is used to find the optimized design. The results show that the operation method is efficient in saving cost and scalable, and microgrids have lower lifetime costs than conventional energy systems. Implications for regulators and distribution utilities are also discussed. [ABSTRACT FROM AUTHOR]

This paper proposes a new formulation for the optimum reconfiguration of islanded microgrid (IMG) systems. The reconfiguration problem is casted as a multi-objective optimization problem, in order to: 1) minimize the IMG fuel consumption in the operational planning horizon for which islanded operation is planned; 2) ensure the IMG capability to feed the maximum possible demand by enhancing its voltage instability proximity index taken over all the states at which the islanded system may reside; and 3) minimize the relevant switching operation costs. The proposed problem formulation takes into consideration the system's operational constraints in all operating conditions based on the consideration of the uncertainty associated with renewable resources output power and load variability. Moreover, the proposed formulation accounts for droop controlled IMG special operational characteristics as well as the availability/unavailability of a supervisory microgrid central controller (MGCC). The formulated problem is solved using non-dominated sorting genetic algorithm II (NSGA-II). MATLAB environment has been used to test and validate the proposed problem formulation. The results show that the implementation of appropriate IMG reconfiguration problem formulations will enhance the performance of IMG systems and facilitate a successful integration of the microgrid concept in distribution networks. [ABSTRACT FROM AUTHOR]

The uncertainty and intermittency of renewable energy sources pose a challenge to generation scheduling of microgrids. This paper presents a hierarchical framework to handle the uncertainty and realize an economic generation schedule of microgrids. The lower level combines a battery energy-storage system (BESS) with renewable energy sources, targeting maximal utilization of renewable power and minimal deviation from the schedule, to provide an optimal generation plan in the day-ahead market. The upper level minimizes the total cost of the microgrid by the genetic algorithm (GA) to yield an economic generation plan of dispatchable distributed generators (DGs) based on the lower level. Two stages of such hierarchical scheduling before and in the day gradually reduce the uncertainty, and lead the overall schedule to evolve toward a stable and economic one. The method is tested on a 14-bus microgrid system. The simulation indicates that the wind turbine and photovoltaic are gradually stabilized by BESS Besides, the operation is scheduled economically, and cheap DGs are always arranged in priority. [ABSTRACT FROM AUTHOR]

There are two operation modes of microgrids: grid-connected mode and stand-alone mode. Normally, a microgrid will be connected to the main grid for the majority of time, i.e., operates in the grid-connected mode. In the stand-alone mode, a microgrid is isolated from the main grid; the highest priority for microgrids is to keep a reliable power supply to customers instead of economic benefits. So, the objectives and energy management strategies are different in two modes. In this paper, a novel double-layer coordinated control approach for microgrid energy management is proposed, which consists of two layers: the schedule layer and the dispatch layer. The schedule layer obtains an economic operation scheme based on forecasting data, while the dispatch layer provides power of controllable units based on real-time data. Errors between the forecasting and real-time data are resolved through coordination control of the two layers by reserving adequate active power in the schedule layer, then allocating that reserve in the dispatch layer to deal with the indeterminacy of uncontrollable units. A typical-structure microgrid is studied as an example, and simulation results are presented to demonstrate the performance of the proposed double-layer coordination control method in both grid-connected mode and stand-alone mode. [ABSTRACT FROM PUBLISHER]

Seamless formation and robust control of distributed generation microgrids are essential requirements to facilitate powerful and flexible control infrastructure in future smart power grids. Motivated by this objective, this paper presents a control structure for microgrid converters based on direct-voltage control and optimized dynamic power sharing. The salient features of the proposed scheme are 1) minimum switching actions between grid-connected and isolated microgrids systems to minimize internal microgrid formation disturbances; 2) active damping control performance in the converter control voltage vector to effectively reject both voltage magnitude disturbances and power angle swings associated with mode transition and load disturbances; and 3) high bandwidth direct voltage control loop in both grid-connected and isolated microgrid modes to improve the dynamic response and disturbance rejection performance. Theoretical analysis and comparative experimental results are presented to validate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]