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In this paper, supermarkets take the role of active consumers that fit into a Smart Grid context, as they have the potential flexibility to shift their energy consumption over time. The paper reviews a selection of promising cost and energy optimal control strategies potentially enabling supermarkets to become flexible consumers. The strategies are categorized either as indirect or direct depending the intended market commitment level. Further, a number of associated challenges are identified that need to be addressed in order to make a proposed strategy into a viable solution for large deployment. The intent is to promote a transition towards more intelligent use of energy in supermarkets and thus to help integrate more intermittent renawables in the electricity grid.

Due to the high penetration of renewable energy in the distribution network, the exponential increase in the amount of data collected and variables makes it difficult for centralized control methods to achieve real-time voltage regulation. Besides, hardware conditions (e.g., communication equipment) limit its application in practice. Therefore, a model-free multi-agent deep reinforcement learning (MADRL) voltage control strategy is developed in this paper. The proposed MADRL control strategy is carried out with a framework of centralized training and distributed execution. We apply deep deterministic policy gradient algorithm to help each agent control its corresponding PV inverter in a distributed manner. However, during the training process, the agent could observe other agents' information to improve training. The simulation on a 33-bus distribution network is carried out to illustrate the effectiveness of the proposed method, and its superiority is also validated by comparing with traditional methods.

Transient instability is an issue for grid-following converters operating under grid faults when complying with low-voltage ride-through requirements. This has initiated much research with the aim to understand, model, and prevent loss of synchronization for synchronous-reference frame phase-locked loop (SRF-PLL)-synchronized systems. However, as the majority of grid faults are asymmetrical, a more complex synchronization unit is needed for the extraction of voltage sequences and phase tracking. This paper proposes a method for enhanced transient stability during severe grid faults for more complex synchronization structures designed to deal with asymmetrical fault conditions. This is done by freezing the frequency of a stationary-reference frame frequency-locked loop. The global asymptotic stability of the method is mathematically proven, and its performance is experimentally verified. Based on the mathematical equivalence between frequency-locked loops and phase-locked loops, it is shown that the presented method can be generalized to both stationary-reference and synchronous-reference frame structures and can, therefore, be a suitable solution in a wide range of applications.

Smart grid applications belong to a diverse set of technologies, which combine behaviors and actions of all grid actors. Building such application is a challenging task requiring modeling, integrating, and validating different grid aspects efficiently. The design flow should adapt to the grid's requirements ranging from basic appliance functions to complex load scheduling algorithms and their integration's consequences. This paper tackles such challenges by proposing a framework to describe smart grid elements formally along with defining their interactions. The framework provides a smooth way for upgrading grid components independently and evaluate their performance. A case study utilizing an infrastructure of electric vehicles is demonstrated to validate the framework and prove its applicability in modeling smart grid applications. The case study enables a demand response service that provides a solution to the coordinated charging scheduling problem of electric vehicles.