Microgrids incorporated with distributed generation (DG) units and energy storage (ES) devices are expected to play more and more important roles in the future power systems. Yet, achieving efficient distributed economic dispatch in microgrids is a challenging issue due to the randomness and nonlinear characteristics of DG units and loads. This paper proposes a cooperative reinforcement learning algorithm for distributed economic dispatch in microgrids. Utilizing the learning algorithm can avoid the difficulty of stochastic modeling and high computational complexity. In the cooperative reinforcement learning algorithm, the function approximation is leveraged to deal with the large and continuous state spaces. And a diffusion strategy is incorporated to coordinate the actions of DG units and ES devices. Based on the proposed algorithm, each node in microgrids only needs to communicate with its local neighbors, without relying on any centralized controllers. Algorithm convergence is analyzed, and simulations based on real-world meteorological and load data are conducted to validate the performance of the proposed algorithm.

Blockchain is an immutable type of distributed ledger that is capable of storing data without relying on a third party. The blockchain technology has attracted significant interest in research areas, including its application in smart grid for cyber security. Although great efforts have been devoted in utilizing blockchain in smart grid for cyber security, there is no comprehensive survey on blockchain in smart grid for cyber resiliency in both application and technological perspectives. To fill this gap, we conducted a comprehensive survey on blockchain for smart gird cyber security. This conducted survey presents the latest insights of ideas, architectures, and techniques of implementation that are relevant to blockchain's application in smart grid for cyber security. This paper aims at providing helpful guidance and reference for future research efforts specific to blockchain for cyber security in smart grid.

The existing research on false data injection (FDI) attacks against state estimation in transmission systems cannot be trivially extended to distribution feeders. The main reason is that a strong condition that requires the attacker to know the estimated state of distribution systems is needed, which makes the traditional FDI attacks difficult to be implemented in practice. In this paper, we propose a practical FDI attack model against state estimation in distribution systems, without paying expensive cost for obtaining the system state. We show that the attacker can approximate the system state based on power flow or injection measurements without too much effort. For local FDI attacks, the strong condition can be further relaxed to the knowledge of local state, which can be approximated based on a small number of power flow or injection measurements. Simulation results based on the IEEE test feeder demonstrate that the proposed practical FDI attack, even with the approximated system state, is more likely to compromise the state estimation without being detected, in comparison with the traditional attacks. This paper provides a basis to study the attack behaviors in distribution systems and a theoretical guide to develop protective countermeasures.

Interval forecast is an efficient method to quantify the uncertainties in renewable energy production. In this work, the idea of prediction intervals (PIs) is employed to capture the uncertainty of wind power generation in power systems. The recurrent neural network (RNN) model is proposed to construct PIs with the lower upper bound estimation (LUBE) method, which is a powerful nonparametric forecast approach. In addition, a novel comprehensive cost function with a new PI evaluation index is designed with the purpose of enhancing the model training. To tune the parameters of RNN prediction model, the dragonfly algorithm (DA) with a linearly random weight update method is introduced as the optimization tool. The performance of the proposed prediction model is validated by a case study using a real world wind power dataset, and the comparative results show the superiority of the model.

With the development of smart grid, energy management becomes critical for reliable and efficient operation of power systems. In this work, we develop a chance-constrained (CC) energy management model for an islanded microgrid which includes distributed generators (DGs), energy storage system (ESS), and renewable generation such as wind power. The objective function of this model consists of generation cost, emission cost and ESS degradation cost. To capture the uncertainty of renewable generation, a novel ambiguity set is introduced without knowing its probability distribution or exact moment information. Based on the ambiguity set, the chance constraint can be processed with distributionally robust optimization (DRO) method and the energy management problem is reformulated as a tractable second-order conic programming (SOCP) problem. The proposed approach is tested with a case study and simulation results indicate that it is effective and reliable. Moreover, the comparison with the method based on known moment information and some other methods is also conducted to show the performance of the proposed method.

In this paper, the cyber-security of smart microgrids is thoroughly discussed. In smart grids, the cyber system and physical process are tightly coupled. Due to the cyber system’s vulnerabilities, any cyber incidents can have economic and physical impacts on their operations. In power electronics-intensive smart microgrids, cyber-attacks can have much more harmful and devastating effects on their operation and stability due to low inertia, especially in islanded operation. In this paper, the cyber–physical systems in smart microgrids are briefly studied. Then, the cyber-attacks on data availability, integrity, and confidentiality are discussed. Since a false data injection (FDI) attack that compromises the data integrity in the cyber/communication network is one of the most challenging threats for smart microgrids, it is investigated in detail in this paper. Such FDI attacks can target state estimation, voltage and frequency control, and smart microgrids’ protection systems. The economic and physical/technical impacts of the FDI attacks on smart microgrids are also reviewed in this paper. The defensive strategies against FDI attacks are classified into protection strategies, in which selected meter measurements are protected, and detection/mitigation strategies, based on either static or dynamic detection. In this paper, implementation examples of FDI attacks’ construction and detection/mitigation in smart microgrids are provided. Samples of recent cyber-security projects in the world, and critical cyber-security standards of smart grids, are presented. Finally, future trends of cyber-security in smart microgrids are discussed.