The power system and microgrid protection-a review

In recent years, power grid infrastructures have been changing from a centralized power generation model to a paradigm where the generation capability is spread over an increasing number of small power stations relying on renewable energy sources. A microgrid is a local network including renewable and non-renewable energy sources as well as distributed loads. Microgrids can be operated in both grid-connected and islanded modes to fill the gap between the significant increase in demand and storage of electricity and transmission issues. Power electronics play an important role in microgrids due to the penetration of renewable energy sources. While microgrids have many benefits for power systems, they cause many challenges, especially in protection systems. This paper presents a comprehensive review of protection systems with the penetration of microgrids in the distribution network. The expansion of a microgrid affects the coordination and protection by a change in the current direction in the distribution network. Various solutions have been suggested in the literature to resolve the microgrid protection issues. The conventional coordination of the protection system is based on the time delays between relays as the primary and backup protection. The system protection scheme has to be changed in the presence of a microgrid, so several protection schemes have been proposed to improve the protection system. Microgrids are classified into different types based on the DC/AC system, communication infrastructure, rotating synchronous machine or inverter-based distributed generation (DG), etc. Finally, we discuss the trend of future protection schemes and compare the conventional power systems

Optimizing Energy Management in Microgrids Based on Different Load Types in Smart Buildings

This paper presents an energy management strategy (EMS) based on the Stackelberg game theory for the microgrid community. Three agents or layers are considered in the proposed framework. The microgrid cluster (MGC) refers to the agent that coordinates the interactions between the microgrids and the utility grid. The microgrid agent manages the energy scheduling of its own consumers. The third agent represents the consumers inside the microgrids. The game equilibrium point is solved between different layers and each layer will benefit the most. First, an algorithm performs demand response in each microgrid according to load models in smart buildings and determines the load consumption for each consumer. Then, each microgrid determines its selling price to the consumers and the amount of energy required to purchase from the utility grid to achieve the maximum profit. Finally, the balance point will be obtained between microgrids by the microgrid cluster agent. Moreover, the proposed method uses various load types at different times based on real-life models. The result shows that considering these different load models with demand response increased the profit of the user agent by an average of 22%. The demand response is implemented by the time of use (TOU) model and real-time pricing (RTP) in the microgrid

Impact of Recloser on Protecting Blind Areas of Distribution Network in the Presence of Distributed Generation

The protection relay issues increase with the penetration of distributed generation (DG) units in distribution networks. Blind protection, sympathetic tripping and failure of reclosing are the frequent problems that are created by adding DG to the distribution networks. This research focuses on the impact of reclosers on overcurrent relay blind protection areas with DGs embedded in the distribution network. The protection blinding in overcurrent relays leads to a delay or non-tripping of the relay. The conventional solution for the blinding issue is to increase the sensitivity in the relay settings. The variation of relay settings may cause problems in the distribution network. This paper investigates the effect of reclosers on eliminating blind areas and improves the reliability in the presence of DG. In this way, possible blind points of the case study are detected, and recloser relays are then installed in appropriate locations. The case study is based on realistic data of a distribution network. The DG consists of two generators with a capacity of 4 MW. The simulation is performed in DIgSILENT software