As the end-users increasingly can provide flexibility to the power system, it is important to consider how this flexibility can be activated as a resource for the grid. Electricity network tariffs are one option that can be used to activate this flexibility. Therefore, by designing efficient grid tariffs, it might be possible to reduce the total costs in the power system by incentivizing a change in consumption patterns. This paper provides a methodology for optimal grid tariff design under decentralized decision-making and uncertainty in demand, power prices, and renewable generation. A bilevel model is formulated to adequately describe the interaction between the end-users and a distribution system operator. In addition, a centralized decision-making model is provided for benchmarking purposes. The bilevel model is reformulated as a mixed-integer linear problem solvable by branch-and-cut techniques. Results for a deterministic example and a stochastic case study are presented and discussed.

Integrated local multi-energy systems are recognized as a promising option to achieve the ambitious energy and climate goals set by the European Commission for 2030. The nature of integrated systems requires a sound combination of interdisciplinary methodologies and complementary tools. Creating an efficient architecture capable of exploiting synergies between tools is therefore crucial for designing, analysing and operating integrated energy systems. The joint application of different tools needed for analysing a local system can be very demanding and time-consuming due to vastly different data structures and functionalities. To address the need for complementary tools, the aim of this paper is to establish and test an integrated modelling architecture allowing the interaction of tools into a modular toolbox for the optimal planning of integrated local multi-energy systems, and also present key preliminary outcomes.