The penetration of intermittent Distributed Generation (DG) brought additional uncertainty to the system operation and planning. To cope with uncertainties the Distribution System Operator (DSO) could implement several strategies. These strategies range from the inclusion of smart technologies which will increment system’s flexibility and resiliency, to improvements in forecasting, modeling, and regulatory pledge that will facilitate the planning activity. Regardless of the nature of the solutions, they could be collected in a sort of toolbox. The planner will access the toolbox to conform cost effective plans, better able to deal with any uncertainty. The present work will address the problem of distribution system planning under uncertainties, considering smart solutions along with traditional reinforcements, in the short-term lead time up to 3 years ahead. The work will be focused on three aspects that are the cornerstones of this work:
• A planning facilitating strategy: Distribution Capacity Contracts (DCCs).
• A flexibility enabler technology: Energy Storage.
• A binding methodology: Multistage Stochastic Programming. Stochastic dual dynamic programming (SDDP).
Under the present directive of the European Parliament concerning common rules for the internal market in electricity, distribution companies are not allowed to own DG but entitled to include it as a planning option to differ investment in traditional grid reinforcements. An evaluation of the regulatory context will lead this work to consider DCCs as a planning alternative available in the toolbox. The impact of this type of contract on the remuneration of the DG owner will be assessed in order to provide insight on its willingness to participate. The DCCs might aid the DSO to defer grid i ii investments during planning stages and to control the network flows during operation.
Given that storage solutions help to match in time production from intermittent sources with load consumption, they will play a major role in dealing with uncertainties. A generic storage model (GSM) based on a future cost piecewise approximation will be developed. This model inspired by hydro-reservoirs will help assessing the impact of storage in planning decisions. This model will be tested by implementing it in short-term hydro scheduling and unit commitment studies.
To trace a path towards the future of this research work, a discussion on the planning problem formulation, under consideration of the lead time, the expansion options, the smart strategies, and the regulatory framework will be presented. Special focus will be given to multistage stochastic programming methods and in particular to the SDDP approach.