Strategic planning of the distribution network is essential to efficiently integrate Renewable Energy Sources (RES), reduce costs, and ensure a secure grid operation. Traditionally, grid planning is based on expensive infrastructure expansion, which does not adapt to rapidly evolving flexibility markets or available flexibility resources.

New OPENTUNITY Network Planning Tool

Within the OPENTUNITY project, ICCS is developing an advanced Network Planning Tool that integrates best planning practices with novel research methodologies and very fast algorithms to address the challenges of modern distribution grids. The tool supports Distribution System Operators (DSOs) in making strategic and informed decisions by integrating sophisticated programming techniques such as Mixed-Integer Linear Programming (MILP) and Mixed-Integer Second-Order Cone Programming (MISOCP) for performing network planning, a fast power flow algorithm for critical scenario identification and timeseries clustering for scenarios extraction. Different objectives can be set, such as cost optimization, investments deferral, or maximizing RES capacity, allowing DSOs to quickly design and compare alternative investment options and operation scenarios.

A user interface provides graphical representations of key results. For example, in a cost optimization scenario (figure on the right side), the planning tool graphically represents the impact of strategic decisions, such as the optimal time to invest in infrastructure and its corresponding impact on power loss costs. The interface can alternatively illustrate scenarios with a focus on delaying expenditures, highlighting how strategic use of flexibility resources can defer costly investments (figure on the left side).

 

 

 

 

 

 

Power flow results can also be presented in all scenarios, e.g. for RES capacity maximization (Figure 3), identifying optimal locations and capacities of new RES power plants for given budgetary constraints. The tool ensures that RES capacity expansion is closely aligned with existing grid constraints. The computed RES capacity at each substation in this case is represented in map-based visualizations.