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Renewables, such as solar and wind generation, combined with storage are becoming a key part of modern grid. This paper develops probabilistic tools for analysis of grid reliability with such variable generation resources. The developed tools improve speed and accuracy of the reliability analysis compared to usual Monte Carlo methods. This is achieved by using an extension of well known convolution method applicable to interdependent variables. The interdependent distributions are obtained from historical data using Machine Learning of quantile models. The paper presents a novel approach to the analysis of reliability contribution of storage based on these models and related to Information Theory. The developed tools are demonstrated in several example scenarios for ISO-New England service area. [ABSTRACT FROM AUTHOR]

To analyze the grid integration effect of a virtual synchronous generator (VSG), this paper proposes a simple power system modeling method. It is shown that for a specific disturbance, by simplifying the original network to a radical n generators single-load model, its state-space small-signal model can be obtained using a unified formula, as long as the single generator infinite bus model of each generator is given. Case studies based on the IEEE 9-bus test system as well as the New York New England test system show high efficiency of the proposed modeling to represent slow power and frequency deviations. Modal analyses give insight into how system poles are synthesized by individual generator poles, and how different designs of VSGs affect the system oscillation modes. Potential errors due to the topology simplification and the measures to avoid them are discussed. The time-domain results and modal analyses highlight some important features of VSG, which are overlooked in previous studies. [ABSTRACT FROM AUTHOR]

This paper proposes a data-driven cooperative method for load frequency control (LFC) of the multi-area power system based on multi-agent deep reinforcement learning (MA-DRL) in continuous action domain. The proposed method can nonlinearly and adaptively derive the optimal coordinated control strategies for multiple LFC controllers through centralized learning and decentralized implementation. The centralized learning is achieved by MA-DRL based on a global action-value function to quantify overall LFC performance of the power system. To solve the MA-DRL problem, multi-agent deep deterministic policy gradient (DDPG) is derived to adjust control agents’ parameters considering the nonlinear generator behaviors. For implementation, each individual controller only needs local information in its control area to deliver optimal control signals. Numerical simulations on a three-area power system and the fully-modeled New-England 39-bus system demonstrate that the proposed method can effectively minimize control errors against stochastic frequency variations caused by load and renewable power fluctuations. [ABSTRACT FROM AUTHOR]

Power flow formulated with a distributed slack bus involves modeling the active-power output of each generator with three elements: a nominal injection modulated by a fraction of the net-load imbalance allocated via a participation factor. This setup acknowledges generator dynamics and system operations, but it has long been plagued by ambiguous and inconsistent interpretations of its constituent elemental quantities. In this paper, we establish that, with the: i) nominal active-power injections set to be the economic dispatch setpoints, ii) participation factors fixed to be the ones used in automatic generation control, and iii) net-load imbalance considered to be the total load and loss unaccounted in economic dispatch, the power flow solution best matches results from a simulation unto steady state of the system differential algebraic equation (DAE) model. Numerical case studies tailored to the New England test system validate the analysis by demonstrating that solutions obtained from a distributed slack formulation offer lower errors (with respect to DAE-model simulations) compared to the exhaustive set of all single slack bus choices. [ABSTRACT FROM AUTHOR]

This paper proposes an approach to obtain dynamic versions of static distribution factors, such as power-transfer, line-outage, and outage-transfer distribution factors. With the proposed dynamic distribution factors (DDFs), one can predict line flows over the post-contingency transient period with the same computational effort as obtaining static distribution factors. Our development centers on deriving closed-form expressions that approximate generator outputs through the post-contingency transient period with a reduced-order aggregate dynamical model to recover dynamic generator participation factors. The full suite of DDFs can then be derived by combining these dynamic generator participation factors with injection shift factors, i.e., static linear sensitivities of line active-power flows with respect to nodal active-power injections, computed at the pre-disturbance steady-state operating point. We illustrate the accuracy and computational benefits of the proposed DDFs via numerical case studies involving the New England test system. [ABSTRACT FROM AUTHOR]