In this letter, a novel autonomous control framework “Grid Mind” is proposed for the secure operation of power grids based on cutting-edge artificial intelligence (AI) technologies. The proposed platform provides a data-driven, model-free and closed-loop control agent trained using deep reinforcement learning (DRL) algorithms by interacting with massive simulations and/or real environment of a power grid. The proposed agent learns from scratch to master the power grid voltage control problem purely from data. It can make autonomous voltage control (AVC) strategies to support grid operators in making effective and timely control actions, according to the current system conditions detected by real-time measurements from supervisory control and data acquisition (SCADA) or phasor measurement units (PMUs). Two state-of-the-art DRL algorithms, namely deep Q-network (DQN) and deep deterministic policy gradient (DDPG), are proposed to formulate the AVC problem with performance compared. Case studies on a realistic 200-bus test system demonstrate the effectiveness and promising performance of the proposed framework.

Battery storage is usually employed in Photovoltaic (PV) system to mitigate the power fluctuations due to the characteristics of PV panels and solar irradiance. Control schemes for PV-battery systems must be able to stabilize the bus voltages as well as to control the power flows flexibly. This paper proposes a comprehensive control and power management system (CAPMS) for PV-battery-based hybrid microgrids with both AC and DC buses, for both grid-connected and islanded modes. The proposed CAPMS is successful in regulating the DC and AC bus voltages and frequency stably, controlling the voltage and power of each unit flexibly, and balancing the power flows in the systems automatically under different operating circumstances, regardless of disturbances from switching operating modes, fluctuations of irradiance and temperature, and change of loads. Both simulation and experimental case studies are carried out to verify the performance of the proposed method.

Fault detection in photovoltaic (PV) arrays becomes difficult as the number of PV panels increases. Particularly, under low irradiance conditions with an active maximum power point tracking algorithm, line-to-line (L-L) faults may remain undetected because of low fault currents, resulting in loss of energy and potential fire hazards. This paper proposes a fault detection algorithm based on multiresolution signal decomposition for feature extraction, and two-stage support vector machine (SVM) classifiers for decision making. This detection method only requires data of the total voltage and current from a PV array and a limited amount of labeled data for training the SVM. Both simulation and experimental case studies verify the accuracy of the proposed method.

This paper presents a detection scheme for DC side short-circuit faults of photovoltaic (PV) arrays that consist of multiple PV panels connected in a series/parallel configuration. Such faults are nearly undetectable under low irradiance conditions, particularly, when a maximum power point tracking algorithm is in-service. If remain undetected, these faults can considerably lower the output energy of solar systems, damage the panels, and potentially cause fire hazards. The proposed fault detection scheme is based on a pattern recognition approach that employs a multiresolution signal decomposition technique to extract the necessary features, based on which a fuzzy inference system determines if a fault has occurred. The presented case studies (both simulation and experimental) demonstrate the effective and reliable performance of the proposed method in detecting PV array faults.

In this paper, a reinforcement-learning-based online optimal (RL-OPT) control method is proposed for hybrid energy storage system (HESS) in AC/DC microgrids involving photovoltaic (PV) systems and diesel generators (DG). Due to the low system inertia, conventional unregulated charging and discharging (C&D) of energy storages in microgrids may introduce disturbances that degrade the power quality and system performance, especially in fast C&D situations. Secondary and tertiary control levels can optimize the state of charge (SOC) reference of HESS; however, they are lacking the direct controllability of regulating the transient performance. Additionally, the uncertainties in practical systems greatly limit the performance of conventional model-based controllers. In this study, the optimal control theory is applied to optimize the C&D profile and to suppress the disturbances caused by integrating HESS. Neural networks (NN) are devised to estimate the nonlinear dynamics of HESS based on the input/output measurements, and to learn the optimal control input for bidirectional-converter-interfaced HESS using the estimated system dynamics. Because the proposed RLOPT method is fully decentralized, which only requires the local measurements, the plug & play capability of HESS can be easily realized. Both islanded and grid-tied modes are considered. Extensive simulations and experiments are conducted to evaluate the effectiveness of the proposed method.

The concept of peer-to-peer (P2P) trading, or transactive energy (TE), is gaining momentum as a future grid restructure. It has the potentials to utilize distributed energy resources (DERs), proactive demand side management (DSM), and the infusion in information and communication technologies (e.g., blockchain and internet of things (IoT)) for promoting the technical and economic efficiency of the system in its entirety. An efficient market framework is vital for the successful and sustainable implementation of such a concept. This paper proposes a P2P energy trading framework enabled by blockchain. It consolidates bilateral contracts, an electronic-commerce platform, a double-auction Vickrey-Clarke-Groves (VCG) mechanism, and trading functionalities with the main grid. Through these multi-layer mechanisms, various trading preferences and attributes of electricity generation and/or consumption are accommodated. Meanwhile, the VCG mechanism eliminates any potential for market power exercise via incentivizing truthful bidding of participants. Different remedies are proposed to overcome the drawback of VCG, i.e., the lack of balanced-budget property. Accordingly, the proposed trading framework is described as multi-settlement and quasi-ideal. Case studies are conducted to analyze and evaluate the proposed trading framework and demonstrate the effectiveness of the proposed remedies in handling probable market deficiencies.