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Data reduction for mixed integer linear programming in complex energy systems

Luc Girardin, Julia Granacher, Natalija Ljubic, François Maréchal, Luise Middelhauve

The task of optimally designing and scheduling energy systems with a high share of renewable energies is complex and computationally demanding. A widespread method for tackling this task is to apply mixed integer linear programming (MILP). Even though the branch-and-bound algorithms used for solving these programs have seen significant improvements in the last years, many problems cannot be solved without further time series aggregation (TSA) methods. State of the art approaches tackle TSA by using well known machine learning techniques to cluster yearly input data to typical periods. However, latter algorithms are usually evaluated by indicators on the performance of the algorithms themselves rather than the MILP optimization model. Furthermore, the selection of the optimal number of typical periods is commonly a subjective imposition of thresholds on these performance indicators. The issue of computational effort is eased by this generally accepted algorithm, but is still limited by the size of the problem, especially the number of integer decisions. This paper aims at proposing a algorithm for systematically reducing the input data for MILP optimization models and choosing the appropriate size. Contrary to most existing studies, the focus is on the impact on the objective function as well as the integer decision rather than on the quality of the clustering algorithm. The subject is addressed by exploiting the two-stage character of optimal design and scheduling of the system by sequentially performing k-medoids clustering. The demonstration of the algorithm on two case studies shows that a few typical periods are sufficient to achieve near optimal decisions. Multi objective optimization (MOO) is performed to assess the quality of the data reduction. The proposed approach is outperforming state of the art algorithms for TSA by reducing CPU time of more than 40%. The case study furthermore reveals that the runtime of the MOO can be reduced by approximately 90% while diverting less than 2 % on Pareto optimal solutions.

2021

How Smart is the Grid?

Luc Girardin, Ermanno Lo Cascio, François Maréchal

Ancient Romans called urbs the set of buildings and infrastructures, and civitas the Roman citizens. Today instead, while the society is surfing the digital tsunami, urbs and civitas tend to become much closer, almost merging, that we might attempt to condensate these into a single concept: smart grid. Internet of things, artificial intelligence, blockchain, and quantum cryptography are only a few of the technologies that are likely to contribute to determining the final portrait of the future smart grid. However, to understand the effective sustainability of complex grids, specific tools are required. To this end, in this article, a new taxonomic framework has been developed starting from a general analysis of the emerging solutions, identifying intersectoral synergies and limitations with respect to the ‘smart grid’ concept. Finally, from the scenario portrayed, a set of issues involving engineering, regulation, security, and social frameworks have been derived in a theoretical fashion. The findings are likely to suggest the urgent need for multidisciplinary cooperation to address engineering and ontological challenges gravitating around the smart grid concept.

2021

Potential of Photovoltaic Panels on Building Envelopes for Decentralized District Energy Systems

Francesco Baldi, Luc Girardin, François Maréchal, Luise Middelhauve

The expected increase of the penetration of distributed renewable energy technologies into the electricity grid is expected to lead to major challenges. As a main stakeholder, authorities often lack the appropriate tools to frame and encourage the transition and monitor the impact of energy transition policies. This paper aims at combining relatively detailed modeling of the PV generation potential on the building's envelope while retaining the energy system optimization approach. The problem is addressed as a multiobjective, mixed-integer linear programming problem. Compared to the existing literature in the field, the proposed approach combines advanced modeling of the energy generation potential from PV panels with detailed representation of the district energy systems, thus allowing an accurate representation of the interaction between the energy generation from PV and the rest of the system. The proposed approach was applied to a typical residential district in Switzerland. The results of the application of the proposed method show that the district can achieve carbon neutrality based on PV energy alone, but this requires covering all the available district's rooftops and part of the district's facades. Whereas facades are generally disregarded due to their lower generation potential, the results also allow concluding that facade PV can be economically convenient for a wide range of electricity prices, including those currently used by the Swiss grid operators. Achieving self-sufficiency at district scale is challenging: it can be achieved by covering approximately 42-100% of the available surface when the round-trip efficiency decreases from 100 to 50%. The results underline the importance of storage for achieving self-sufficiency: even with 100%, round-trip efficiency for the storage, very large capacities are required. However, energy demand reduction through renovation would allow reaching self-sufficiency with half of the PV and storage capacity required.

2021