A Multi-Temporal–Spatial Power and Energy Balancing Method Considering Energy Complementarity

Traditional power and energy balance methods suffer from several limitations, such as inadequate coordination across long-term and short-term temporal scales, confinement to single-region spatial boundaries, and insufficient exploitation of multi-energy complementarity. This paper proposes a multi-temporal, multi-spatial power, and energy balance framework that integrates cross-regional energy sharing and leverages the complementarity among diverse power sources. A two-level feedback optimization model is formulated, coupling the medium- to- long-term energy balance with short-term power balance. The model comprehensively incorporates constraints, including the characteristics of various power sources, unit operating status, dynamic power flow on cross-regional tie-lines, as well as renewable energy curtailment minimization and power supply reliability requirements. This hierarchical structure enables coordination optimization across both the long-term and short-term temporal dimension and cross-regional mutual aid in the spatial dimension. A hierarchical solution strategy is employed, which integrates an improved particle swarm optimization algorithm with the Gurobi solver. Case studies on realistic power systems demonstrate that the proposed method effectively exploits the potential of multi-energy coordination and cross-regional mutual aid, achieving improved renewable energy accommodation, enhanced cross-regional resource utilization efficiency, and robust power and energy balance across multi-temporal and spatial scales.