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Renewable-energy-based grids development needs new methods to maintain thebalance between the load and generation using the efficient energy storagesmodels. Most of the available energy storages models do not take into accountsuch important features as the nonlinear dependence of efficiency on lifetimeand changes in capacity over time horizon, the distribution of load betweenseveral independent storages. In order to solve these problems the Volterraintegral dynamical models are employed. Such models allow to determine thealternating power function for given/forecasted load and generation datasets.In order to efficiently solve this problem, the load forecasting models wereproposed using deep learning and support vector regression models. Forecastingmodels use various features including average daily temperature, load valueswith time shift and moving averages. Effectiveness of the proposed energybalancing method using the state-of-the-art forecasting models is demonstratedon the real datasets of Germany's electric grid.

Energy balancing using charge/discharge storages control and load forecasts in a renewable-energy-based grids