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Optimal scheduling is a requirement for microgrids to participate in current and future energy markets. Although the number of research articles on this subject is on the rise, there is a shortage of papers containing detailed mathematical modeling of the distributed energy resources available in a microgrid. To address this gap, this paper presents in detail how to mathematically model resources such as battery energy storage systems, solar generation systems, directly controllable loads, load shedding, scheduled intentional islanding, and generation curtailment in the microgrid optimal scheduling problem. The proposed modeling also includes a methodology to determine the availability cost of battery and solar systems assets. Simulations were carried out considering energy prices from an actual time-of-use tariff, costs based on real market data, and scenarios with scheduled islanding. Simulation results provide support to validate the proposed model. Data illustrate how energy arbitrage can reduce microgrid costs in a time-of-use tariff. Results also show how the microgrid’s self-sufficiency and the storage system’s capacity can impact the microgrid’s energy bill. The findings also bring out the need to consider the scheduled islanding event in the day-ahead optimization for microgrids.

Intentional islanding is a corrective procedure that aims to protect the stability of the power system during an emergency, by dividing the grid into several partitions and isolating the elements that would cause cascading failures. This paper proposes a deep learning method to solve the problem of intentional islanding in an end-to-end manner. Two types of loss functions are examined for the graph partitioning task, and a loss function is added on the deep learning model, aiming to minimise the load-generation imbalance in the formed islands. In addition, the proposed solution incorporates a technique for merging the independent buses to their nearest neighbour in case there are isolated buses after the clusterisation, improving the final result in cases of large and complex systems. Several experiments demonstrate that the introduced deep learning method provides effective clustering results for intentional islanding, managing to keep the power imbalance low and creating stable islands. Finally, the proposed method is dynamic, relying on real-time system conditions to calculate the result.

An islanding operation of distributed generations (DGs) in emergencies due to a fault in distribution systems can be a means of power supply for important loads in outage areas by facilitating the self-sufficient capability of DGs forming microgrids. This paper presents an optimization-based intentional islanding scheme to derive a near-optimal service restoration (SR) plan. The anti-parallel operation of DGs is considered a new constraint that avoids more than two DGs in an island thereby, enabling simpler control and operation of the distribution system in an emergency. Each island is created by an island partitioning scheme based on the tree representation of the network and fast searching scheme for the tree structure considering load importance, and a genetic algorithm (GA) is utilized to explore possible SR solutions. Case studies on IEEE 69-bus distribution system according to various fault locations are conducted, and the simulation results show that the proposed scheme can restore more loads with higher priority in outage areas by the intentional islanding of DGs. Furthermore, the time for deriving the optimal solution can be reduced since the evaluations for infeasible solutions are not performed.

Participation of distributed energy resources in the load restoration procedure, known as intentional islanding, can significantly improve the distribution system reliability. Distribution system reconfiguration can effectively alter islanding procedure and thus provide an opportunity to supply more demanded energy and reduce distribution system losses. In addition, high-impact events such as hurricanes and earthquake may complicate the procedure of load restoration, due to disconnection of the distribution system from the upstream grid or concurrent component outages. This paper presents a two-level method for intentional islanding of a reconfigurable distribution system, considering high impact events. In the first level, optimal islands are selected according to the graph model of the distribution system. In the second level, an optimal power flow (OPF) problem is solved to meet the operation constraints of the islands by reactive power control and demand side management. The proposed problem in the first level is solved by a combination of depth first search and particle swarm optimization methods. The OPF problem in the second level is solved in DIgSILENT software. The proposed method is implemented in the IEEE 69-bus test system, and the results show the validity and effectiveness of the proposed algorithm.

Crow search optimizer is considered as the latest meta-heuristic algorithm that is influenced by crow’s behavior. The proposed oppositional crow search optimizer (OCSO) is intended here, for solving the intentional islanding problem. This paper has proposed a novel two-step method by considering the most significant factors such as the constraints of line capacity, bus voltage, load priority, load controllability, problem occurred due to spacing of solutions as well as the capability of integrating the islands to produce higher intentional islands. Initially, the tree knapsack problem is considered as an intentional islanding issue and therefore the OCSO algorithm is employed in solving such shortcomings. In OCSO approach, the opposition-based generation jumping and population initialization concept are used in crow search optimizer for improving the convergence profile and computational speed. In next process, island feasibility is verified by means of conducting power flow computation and providing significant modifications. Six distributed generations containing IEEE 69-bus test system are employed in validating experimentally the efficiency of the proposed approach, and comparison was done for the obtained results with the other existing approaches. The comparative analysis is evaluated to enhance the level of reliability, particularly critical load.

Renewable sources and Distributed Generation (DG) have been generating a growing economic interest given the increase in electricity consumption. For the end consumer, the lower environmental impact, easy-to-install and quick payback are great alternatives to traditional connections. DG growth drives new studies to predict different results in the electrical grid. The IEEE 1547 technical guidelines bring the possibility that in case of any failure that causes a shutdown, the operation is possible through intentional islanding provided by the electrical utility, continuing service, and maintaining customer satisfaction. For a more in-depth analysis of the impacts of this scenario, this paper contributes with a proposal to modify the strategy for identifying possible intentional islanding. A hybrid relay was modeled using passive techniques along with a suggestion for the operation of the newly formed Microgrid (MG), presenting a control philosophy of the regulators connected to the grid or being islanded, the latter defining the functions of the DGs as master–slave. The results obtained by the experiments proved to be excellent when inserting loads while the intentional islanding event occurs, confirming that the model is within the technical guidelines of IEEE 1547. This paper brings exciting conclusions about this operation, and the power quality of the electrical generator in the MG, according to the criteria established by ANEEL’s Distribution Procedures Guidelines (PRODIST).

Distributed generation plays a significant role in power generation, but the standalone system has some limitations like excess power generation and sudden increment in load. Grid interconnected DG system mitigates all this type of problems but some different questions arise in this interconnection. How to synchronize the DG system with the grid? If any change in voltage or frequency they lead to disconnect the grid from DG system. But due to sudden loose of grid supply, the phase angle is changed in filter terminal voltage and leads same change in load voltage because when the grid is connected the load receives power from both DG and grid. In grid-connected system current controller is generally used, to maintain the constant current in load side, so in islanding conditions, the voltage profile will get damage and power factor is also dropped. This paper proposes a strategy of two controllers for both grid-connected and intentional islanding modes. PI controller based constant current regulator for grid-connected mode, while ANN based VC controller for intentional islanding mode. These two controllers are operated according to changes occurred at Point of Common Coupling (PCC).

This study presents voltage magnitude and frequency control of a three-phase voltage source inverter for distributed generations to achieve a seamless transfer between grid-tied mode and intentional islanding mode. When the grid is normal, the inverter works in grid-tied mode. On the contrary, when the grid fault occurs, the breaker connecting the inverter to the grid must be turned off and the inverter just supplies the power for local loads. By varying the frequency and the magnitude of the inverter output voltage, an output power control, with which the output active and reactive power can be precisely controlled, is presented. To improve the transient response, a virtual inductor with high-pass filter in synchronous d–q frame is proposed. The effectiveness of the virtual inductor is explained by the frequency response of the inverter. Finally, experimental results are given to verify the effectiveness of the scheme.

A power grid island is a self-sufficient subnetwork in a large-scale power system. In weakly connected islands, limited inter-island power flows are allowed. Intentional islanding of a power grid is helpful for the analysis of distributed generation systems connected to a power grid, and valuable for power system reliability of extreme emergency states. In this paper, we use graph partitioning methods to form islands in a power grid and formulate these problems as mixed integer programs. Our models are based the optimal power flow model to minimize the load shedding cost. With these mathematical programming models, optimal formation of islands can be obtained and the different approaches can be compared. Through experiment on IEEE-30-Bus system, computational results are analyzed and compared to provide insight for power grid intentional islanding.

In this study, a novel intentional island model of a distribution system with distributed generations (DGs) is presented and the improved Dijkstra algorithm is used to solve this model. This paper abstracts the distribution network with DGs to the layered directed tree according to its radial structure and power restoration process. In consideration of grade, controllability, capacity, level and electrical betweenness of load, the model weights load and maximizes total load weight in the island. The proposed model considers power balance, node voltage, phase angle and transmission capability of the branch, and network connectivity to meet practical engineering requirements. The improved Dijkstra algorithm formulates a search rule to select the load that can be divided into an island in descending order of the shortest path between the load node and DG node. An optimal island partition scheme is achieved through three stages: origin island, baby island and mature island. Meanwhile, scheme adjustment and constraint checking are used alternately to balance objective functions and constraints. The improved IEEE 43-bus distribution network is applied to verify the validity of the algorithm. A comparison of two island methods shows that the proposed algorithm can generate a reasonable scheme for island partitioning.