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The challenge of integrating renewable energies in medium voltage grids has been the subject of a number of researches and various discussions. The rationale behind this is to establish rules giving to customers the right to debit their excess energy in the network, especially, those with high potential of solar power production. However, the use of biomass energy in the national distribution network has been established successfully in a medium voltage feeder in the Oujda region in Morocco. Another one will see the light in the Fez region in the same country. In this study, we will develop an active management algorithm to predict the need in terms of the power to generate by a transformer HV/MV for a medium voltage feeder. This feeder will contain a biomass source, a photovoltaic installation and a storage system. This will allow us to manage the bidirectional energy flow without influencing the save behavior of the grid. Likewise, help us define the new peak of the feeder and economize or delay future investments.

An efficient and economic scheduling of power plants relies on an accurate demand forecast especially for the short-term due to its tight relation to power markets and trading operations in interconnected power systems. A slight deviation of load prediction from real demand could engender the start-up of a conventional power station which could be either time-consuming or requiring expensive combustible, a deviation that could interfere as well with renewables intermittency and demand response strategies. Hence, load forecasting still a challenging subject because of the various transformations that the energy sector undergoes and that directly impact the demand profile shape. Therefore, conceiving dynamic load demand forecast approaches will permit utilities save money in different vertical structures and regulation schemes. In this paper, we propose a novel approach for short-term demand prediction valid for normal and special days to address the impact of climate changes along with events occurrence on forecast accuracy. This approach is based on the prediction of hourly loads, established on the daily peak load prediction using backpropagation combined to chi-squared method for weighting historical data to enhance the training process. Obtained results from extensive testing on the Moroccan’s power system confirm the strength of the developed approach, that improved the forecast accuracy by a range of 1.1–4% compared to the existing methods.