High-capacity and large-sized batteries are widely employed in electric vehicles and energy storage systems. The surface temperature (ST) field of these batteries is usually maldistributed and unmeasurable in practice, which brings great challenges to temperature safety monitoring. Thus, this paper rebuilds the lumped thermal model and proposes a KF (Kalman filter)-MLP (multi-layer perception) joint estimation algorithm to reconstruct the two-dimensional (2D) ST field of lithium-ion batteries (LIBs).
In high renewable energy sources penetrated multiple microgrids (MMGs), conventional fast timescale load frequency control (LFC) and slow timescale economic dispatch become much less economically efficient. In this paper, the economy-oriented LFC problem of nonlinear MMGs is framed and a distributed optimal disturbance rejection control method is proposed to address this problem. In particular, this method has excellent anti-disturbance capability in dealing with power imbalances, critical parameter variations, and measurement noises.
Renewable energy is boosting the deployment of microgrids (MGs) with stochastic and low inertia nature. To improve the operational efficiency of MGs while guaranteeing frequency security, a three-stage stochastic unit commitment problem is proposed, where renewable energy can be deloaded. In the first stage, the diesel generators (DGs) are scheduled, responding to uncertainties of loads and photovoltaic generator output. In the second stage, the outputs of DGs are optimized to reduce the operational cost under uncertain disturbances.