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In energy-harvesting-enabled networks, the intermittent and randomly distributed renewable energy imposes severe challenges in reliably supplying the time-varying mobile traffic. To tackle this issue, we reshape the spatial renewable energy and mobile traffic by exploiting the approach of energy sharing and load shifting, with the objective of minimizing the grid energy expenditure of cellular networks powered by both grid and renewable energy. We formulate this problem as a mixed-integer nonlinear programming, which is proved to be NP-hard. For centralized networks, we first devise a cost-efficient centralized algorithm leveraging the univariate search technique, which can find the near-optimal solutions with the advantages of low complexity and fast convergence. Specifically, by jointly optimizing the spatial distribution of renewable energy and mobile traffic, the centralized algorithm achieves a good match …