Generation is shifting from a centralized power generating facility having large synchronous generators to distributed generation involving sources of smaller capacity. Most of these sources require inverters on the front end while being connected to the grid. Lower available kinetic energy, coupled with less short-circuit current ratio compared to large synchronous generators, compromises the transient stability of the microgrid when isolated from the main grid. Sources in the microgrid use droop control to share power according to their capacity without any form of communication. This paper proposes a novel controller for inverters to improve the frequency response of microgrid under disturbances involving large frequency deviations. It also discusses design of various parameters defined for the proposed control. The microgrid, which has two inverters and two synchronous generators, is simulated using Simulink/MATLAB software to test the proposed control strategy.

Microgrid is a combination of distributed generators, storage systems, and controllable loads connected to low-voltage network that can operate either in grid-connected or in island mode. High penetration of power at distribution level creates such multiple microgrids. This paper proposes a two-level architecture for distributed-energy-resource management for multiple microgrids using multiagent systems. In order to match the buyers and sellers in the energy market, symmetrical assignment problem based on naíve auction algorithm is used. The developed mechanism allows the pool members such as generation agents, load agents, auction agents, grid agents, and storage agents to participate in market. Three different scenarios are identified based on the supply-demand mismatch among the participating microgrids. At the end of this paper, two case studies are presented with two and four interconnected microgrids participating in the market. Simulation results clearly indicate that the agent-based management is effective in resource management among multiple microgrids economically and profitably.

Hybrid ac-dc microgrid architecture is attracting special attention since it combines the benefits of both ac and dc systems. Control of hybrid microgrid presents a significant research and engineering challenge and hence needs increased research efforts. This paper attempts to review control strategies that are reported in the literature for the hybrid ac-dc microgrid. At first, typical and emerging hybrid microgrid power topologies are presented briefly. Various types of interlinking converters proposed for connecting ac and dc subgrids are discussed subsequently. Following this, a detailed discussion on control strategies for satisfying various control objectives is taken up. Control strategies have been systematically reviewed focusing on each of the broad aspects of control namely modeling, power management, coordinated control, stability analysis, power quality, and protection strategies. Finally, the research gaps observed during the review process and possible solution approaches are outlined.

In this paper, an agent based intelligent energy management system is proposed to facilitate power trading among microgrids and allow customers to participate in demand response. The proposed intelligence uses demand response, and diversity in electricity consumption patterns of the customers and availability of power from distributed generators as the vital means in managing power in the system. A new priority index is proposed for customers participating in the market based on frequency and size of load participating in demand response. In order to validate the proposed method, a case study with two interconnected microgrids is simulated. Based on extensive simulation results of the system developed using Java Agent DEvelopment framework (JADE), it has been found that multi-agent based demand response is successful in reducing the system peak in addition to cost benefit for the customers with high priority index.

In this paper, an agent-based energy-management system is proposed to facilitate power trading among microgrids with demand response and distributed storage (DRaDS). The key focus of this approach is to utilize diversity in load consumption patterns of the customers and energy availability from the distributed energy resources, distributed storage, and demand response (DR) in reducing the peak demand and minimizing the cost of electricity. An index-based incentive mechanism is also proposed to encourage customers participating in DR based on frequency and size of the participation. The proposed strategies are validated using two case studies with interconnected microgrids. Based on extensive simulation results of the system being developed using the Java Agent DEvelopment framework (JADE), it has been found that multiagent-based energy management with DRaDS is successful in reducing the system peak and cost benefit for customers with a high priority index.

Several drawbacks of the conventional and inverse droop control based decentralized techniques for islanded microgrids are being addressed actively, over the years by various researchers. One of the prominent issues is the inaccurate power sharing (reactive power in the conventional droop and real power in the inverse droop) among the distributed generators (DGs) due to the feeder/line impedances. Virtual impedance (VI) is a popular technique to overcome impedance mismatches but the challenge is in the setting of the appropriate value. This paper presents an adaptive decentralized technique for adjusting the virtual impedance in the controller of a DG, based on its output current, without the need of communication, extra sensors or network parameter/load estimations. The methodology is tested through simulations for a wide variety of cases including unbalanced, harmonic, constant power and induction motor loads, DG plug and play functionality and performance in larger networks (modified 13 and 33 bus systems) under meshed configurations. The range for parameter stability is verified through modelling and small signal eigenvalue analyses. Further, improved power sharing performances using the proposed scheme is validated through implementation on a laboratory experimental setup.