A microgrid is considered to be a smart power system that can integrate local renewable energy effectively. However, the intermittent nature of renewable energy causes operating pressure and additional expense in maintaining the stable operation by the energy management system in a microgrid. The structure of multi-microgrids provides the possibility to construct flexible and various energy trading framework. In this paper, in order to reduce the adverse effects of uncertain renewable energy output, a distributed robust model predictive control (DRMPC)-based energy management strategy is proposed for islanded multi-microgrids. This strategy balances the robustness and economy of single-microgrid system operation by combining the advantages of robust optimization and model predictive control, while coping with the uncertainty of renewable energy sources. Furthermore, a dynamic energy trading market is formed among microgrids, which can enhance the overall economy of the multi-microgrids system. Simulation results verify the feasibility of the proposed DRMPC strategy.

As the penetration of PV generation increases, there is a growing operational demand on PV systems to participate in microgrid frequency regulation. It is expected that future distribution systems will consist of multiple microgrid clusters. However, interconnecting PV microgrids may lead to system interactions and instability. To date, no research work has been done to analyze the dynamic behavior and enhance the stability of microgrid clusters considering the dynamics of the PV primary sources and dc links. To fill this gap, this paper presents comprehensive modeling, analysis, and stabilization of PV-based multiple microgrid clusters. A detailed small-signal model for PV-based microgrid clusters considering local adaptive dynamic droop control mechanism of the voltage-source PV system is developed. The complete dynamic model is then used to access and compare the dynamic characteristics of the single microgrid and interconnected microgrids. In order to enhance system stability of the PV microgrid clusters, a tie-line flow and stabilization strategy is proposed to suppress the introduced interarea and local oscillations. Robustly selecting of the key control parameters is transformed to a multiobjective optimization problem which is solved by genetic algorithm (GA). The proposed damping controller can effectively damp the power oscillations and provide robust control performance under variable operating conditions. Theoretical analysis, simulation results under various scenarios are presented to verify the effectiveness of the proposed scheme.

In this paper, an islanded medium-voltage (MV) microgrid placed in Dongao Island is presented, which integrates renewable-energy-based distributed generations (DGs), energy storage system (ESS), and local loads. In an isolated microgrid without connection to the main grid to support the frequency, it is more complex to control and manage. Thus, in order to maintain the frequency stability in multiple time scales, a hierarchical control strategy is proposed. The proposed control architecture divides the system frequency in three zones: (A) stable zone, (B) precautionary zone, and (C) emergency zone. In this way, dynamic stability control that copes with disturbances in short-time scale is implemented by microgrid central controller within Zone B and Zone C. Meanwhile, steady-state stability control to solve the peaks and valleys problem of loads and DGs in long-time scale is executed by the microgrid energy management system within Zone A. Furthermore, based on the developed complete small-signal state-space model, sensitivity analysis of the eigenvalues is conducted in order to reveal the dynamic stability margin of the MV microgrid, and to identify the proper range of the control parameters of Zone B. Theoretical analysis, time-domain simulation, and field test results under various conditions and scenarios in the Dongao Island microgrid are presented to prove the validity of the introduced control strategy.

The multi-microgrids system of the island group is geographically dispersed with different ownership. A control strategy based on distributed model predictive control is proposed to optimize the economic scheduling of multi-microgrids on island group. The strategy is designed based on the dynamic non-cooperative game theory to regulate the trading behavior among microgrids belonging to different owners. The mechanism maximizes the economic benefits of the microgrids under the premise of ensuring the closed-loop stability of the single microgrid system. Only a minimum amount of communication information exchange is needed, which avoids the demands of the central controller and can help the microgrid to protect its privacy of operating information. The proposed strategy can maximize the benefits of power trading and significantly reduce the operating cost of the system while ensuring the balance between supply and demand. Simulation results are presented to prove the fairness and validity of the proposed control strategy.

With the decreasing reserves of fossil energy and the increasing capacity of renewable energy generation, the scale of microgrid based on distributed generations is expanding. However, more operation data and transaction information of microgrids will also bring several problems: the sufficient capacity of the server in the central management needed, the crises of trust among members, the transparency of transaction information and the confidentiality of data storage. In this paper, blockchain technology is used to deal with these problems as distributed data storage technology. A double-layer framework of energy transactions based on blockchain in multi-microgrids is proposed to provide decentralized trading, information transparency and mutual trust system of each node in the trading market. The central node within the microgrid collects the demand information of the trading market in lower layer and sends them to the trading market of multi-microgrids in higher layer to seek the energy transaction. The continuous double auction mechanism is used in the trading market to guarantee free and fair transactions among nodes. The proposed transaction framework effectively reduces the transaction volume with the main grid which improves energy utilization. Comprehensive simulation results are presented to prove the feasibility of the proposed transaction framework.

With the large-scale integration of wind power, the frequency stability of the power system is facing a significant challenge due to the wind power generation system typically operating in maximum power point tracking (MPPT) mode. Traditional frequency support control methods for wind turbines (WTs) do not consider the state of WTs and the frequency changes simultaneously. This paper proposes a fuzzy adaptive frequency support control strategy for WTs with improved rotor speed recovery. Based on fuzzy logic control, the proportional gain is adaptively adjusted using the rotor speed and frequency deviation to support the grid frequency. It can fully utilize the frequency support capability of WTs with more available rotor kinetic energy while averting the over-deceleration of WTs with low rotor speed. Moreover, as the frequency deviation increases, the output power of WTs is increased by a larger proportional gain to alleviate the excessive frequency degradation. In response to the shortcomings of traditional rotor speed recovery methods, an improved rotor speed recovery method is presented by integral control, which can restore the rotor speed in stages without affecting the frequency support performance of WTs and can avoid secondary frequency drop (SFD). Simulation results verify the effectiveness of the proposed scheme.