Sining Hu

Addressing the imperative for energy transition amid depleting fossil fuels, distributed generation (DG) is increasingly integrated into distribution networks (DNs). This integration necessitates low-carbon dispatching solutions that reconcile economic and environmental objectives. To bridge the gap between conventional “electricity perspective” optimization and emerging “carbon perspective” requirements, this research integrated Carbon Emission Flow (CEF) theory to analyze spatiotemporal carbon flow characteristics within DN. Recognizing the limitations of the single-objective approach in balancing multifaceted demands, a multi-objective optimization model was formulated. This model could capture the spatiotemporal dynamics of nodal carbon intensity for low-carbon dispatching while comprehensively incorporating diverse operational economic costs to achieve collaborative low-carbon and economic dispatch in DG-embedded DN. To efficiently solve this complex constrained model, a novel Q-learning enhanced Moth Flame Optimization (QMFO) algorithm was proposed. QMFO synergized the global search capability of the Moth Flame Optimization (MFO) algorithm with the adaptive decision-making of Q-learning, embedding an adaptive exploration strategy to significantly enhance solution efficiency and accuracy for multi-objective problems. Validated on a 16-node three-feeder system, the method co-optimizes switch configurations and DG outputs, achieving dual objectives of loss reduction and carbon emission mitigation while preserving radial topology feasibility.

Addressing the imperative for energy transition amid depleting fossil fuels, distributed generation (DG) is increasingly integrated into distribution networks (DN). This integration necessitates low-carbon dispatching solutions that reconcile economic and environmental objectives. To bridge the gap between conventional “electricity perspective” optimization and emerging “carbon perspective” requirements, this research integrated Carbon Emission Flow (CEF) theory to analyze spatiotemporal carbon flow characteristics within DN. Recognizing the limitations of single-objective approached in balancing multifaceted demands, a multi-objective optimization model was formulated. This model could capture the spatiotemporal dynamics of nodal carbon intensity for low-carbon dispatching while comprehensively incorporating diverse operational economic costs to achieve collaborative low-carbon and economic dispatch in DG-embedded DN. To efficiently solve this complex constrained model, a novel Q-learning enhanced Moth Flame Optimization (QMFO) algorithm was proposed. QMFO synergized the global search capability of the Moth Flame Optimization (MFO) algorithm with the adaptive decision-making of Q-learning, embedding an adaptive exploration strategy to significantly enhance solution efficiency and accuracy for multi-objective problems. Validated on a 16-node three-feeder system, the method co-optimizes switch configurations and DG outputs, achieving dual objectives of loss reduction and carbon emission mitigation while preserving radial topology feasibility.

Optimizing the operation of active distribution networks (ADN) has become more challenging because of the uncertainty created by the high penetration level of distributed photovoltaic (PV). From the convex optimization perspective, this paper proposes a two-layer optimization model to simplify the solution of the ADN optimal operation problem. Firstly, to pick out the ADN "key" nodes, a "key" nodes selection approach that used improved K-means clustering algorithm and two indexes (integrated voltage sensitivity and reactive power balance degree) is introduced. Then, a two-layer ADN optimization model is built using various time scales. The upper layer is a long time scale model with on-load tap changer transformer (OLTC) and Capacitor Bank (CB), and the lower layer is a short time scale optimization model with PV inverters and distributed energy storages (ES). To take into account the PV users’ interests, maximizing PV active power output is added to the objective. Afterwards, under the application of the second-order cone programming (SOCP) power flow model, a linearization method of OLTC model and its tap change frequency constraints are proposed. The linear OLTC model, together with the linear models of the other equipment, constructs a mixed-integer second-order cone convex optimization (MISOCP) model. Finally, the effectiveness of the proposed method is verified by solving the IEEE33 node system using the CPLEX solver.