A methodology based on demand side management approaches for an optimal planning of MV-LV grid expansion in highly congested urban areas

Nowadays, a renewed and growing interest is involving the Demand Side Management (DSM) techniques, since they are regarded as suitable tools in Distribution Networks planning. The following drivers trigger off this revitalisation: • The electric demand in urban contexts is growing beyond the expectations, owing to correspondingly increased needs of comfort (air-conditioning, indoor air quality, housekeeping, communication, etc.), which can be fulfilled through the electric end-uses with high quality, compliance with environmental regulations and competitive economic benefits. • This sudden higher pressure of the demand on the local Electric System is bound to force the Distributors to make huge short-term and unforeseen investments for expanding their urban MVLV grids • The use of DSM-based tools and methodologies can slow down the growth rate of the yearly peak load and energy demand. Then, they can supply the planner with cheaper (and likewise efficient) alternatives with respect to a plain grid expansion. The present paper describes the ECORET methodology, the architectures and techniques adopted for it, as well as the obtained results for a case study. ECORET supplies the planners of urban grids with tools for a rational choice among several possible alternative mixes of investments and DSM measures, in order to fulfil the increased demand in an optimal way. The considered approach involves the use of two software packages devoted to grid planning. It is developed through a preliminary screening (which uses a model-generated grid) and of a further analysis on the real grid. Avoided costs for grid expansion are then obtained from this computation and used in the classical DSM economic tests, together with other costs and benefits, to assess the advantages of the considered DSM programmes. The procedure was applied on a case study relevant to a portion of a real urban distribution grid.