This paper presents a supervised multi-agent safe policy learning (SMAS-PL) method for optimal power management of networked microgrids (MGs) in distribution systems. While unconstrained reinforcement learning (RL) algorithms are black-box decision models that could fail to satisfy grid operational constraints, our proposed method considers AC power flow equations and other operational limits. Accordingly, the training process employs the gradient information of operational constraints to ensure that the optimal control policy functions generate safe and feasible decisions. Furthermore, we have developed a distributed consensus-based optimization approach to train the agents’ policy functions while maintaining MGs’ privacy and data ownership boundaries. After training, the learned optimal policy functions can be safely used by the MGs to dispatch their local resources, without the need to solve a complex optimization problem from scratch. Numerical experiments have been devised to verify the performance of the proposed method.

This paper presents an approximate Reinforcement Learning (RL) methodology for bi-level power management of networked Microgrids (MG) in electric distribution systems. In practice, the cooperative agent can have limited or no knowledge of the MG asset behavior and detailed models behind the Point of Common Coupling (PCC). This makes the distribution systems unobservable and impedes conventional optimization solutions for the constrained MG power management problem. To tackle this challenge, we have proposed a bi-level RL framework in a price-based environment. At the higher level, a cooperative agent performs function approximation to predict the behavior of entities under incomplete information of MG parametric models; while at the lower level, each MG provides power-flow-constrained optimal response to price signals. The function approximation scheme is then used within an adaptive RL framework to optimize the price signal as the system load and solar generation change over time. Numerical experiments have verified that, compared to previous works in the literature, the proposed privacy-preserving learning model has better adaptability and enhanced computational speed.

This paper proposes a service restoration model for unbalanced distribution systems and inverter-dominated microgrids (MGs), in which frequency dynamics constraints are developed to optimize the amount of load restoration and guarantee the dynamic performance of system frequency response during the restoration process. After extreme events, the damaged distribution systems can be sectionalized into several isolated MGs to restore critical loads and tripped non-black start distributed generations (DGs) by black start DGs. However, the high penetration of inverter-based DGs reduces the system inertia, which results in low-inertia issues and large frequency fluctuation during the restoration process. To address this challenge, we propose a two-level simulation-assisted sequential service restoration model, which includes a mixed integer linear programming (MILP)-based optimization model and a transient simulation model. The proposed MILP model explicitly incorporates the frequency response into constraints, by interfacing with transient simulation of inverter-dominated MGs. Numerical results on a modified IEEE 123-bus system have validated that the frequency dynamic performance of the proposed service restoration model are indeed improved.

This paper proposes an asynchronous distributed leader-follower control method to achieve conservation voltage reduction (CVR) in three-phase unbalanced distribution systems by optimally scheduling smart inverters of distributed energy resources (DERs). One feature of the proposed method is to consider integrated primary-secondary distribution networks and voltage dependent loads. To ease the computational complexity introduced by the large number of secondary networks, we partition a system into distributed leader-follower control zones based on the network connectivity. To address the non-convexity from the nonlinear power flow and load models, a feedback-based linear approximation using instantaneous power and voltage measurements is proposed. This enables the online implementation of the proposed method to achieve fast tracking of system variations led by DERs. Another feature of the proposed method is the asynchronous implementations of the leader-follower controllers, which makes it compatible with non-uniform update rates and robust against communication delays and failures. Numerical tests are performed on a real distribution feeder in Midwest U. S. to validate the effectiveness and robustness of the proposed method.