Invited Session Wed.3.H 3003A

Wednesday, 15:15 - 16:45 h, Room: H 3003A

Cluster 5: Constraint programming [...]

Computational sustainability


Chair: Alan Holland



Wednesday, 15:15 - 15:40 h, Room: H 3003A, Talk 1

Alan Holland
Optimising the economic efficiency of monetary incentives for renewable energy investment

Coauthor: Barry O'Sullivan


Many governments have instituted policies to support the increased generation of electricity using renewable energy devices, and there is compelling need to ensure that publicly funded subsidy schemes are operated in a manner that maximizes societal benefit.
We consider the mechanism design problem associated with the rollout of an auction for monetary incentives to support the increased deployment of renewable energy devices. We assume a game-theoretic model with self-interested agents that behave strategically in order to maximize their expected utility. We seek to develop algorithms for the assignment of investment subsidies and determination of payoff that are resilient to the possibility that agents will lie in order to manipulate the outcome for their own benefit. We seek to minimize the maximum cost imposed on any single agent thus ensuring that a wide distribution of subsidies can be expected. This problem is analogous to solving a makespan minimization problem and has associated algorithmic design challenges when we require a mechanism that can support the elicitation of preferences from potentially tens of thousands of agents in public auctions.



Wednesday, 15:45 - 16:10 h, Room: H 3003A, Talk 2

Rene Schönfelder
Stochastic routing for electric vehicles

Coauthor: Martin Leucker


The development of electric vehicles (EV) using regenerative energy sources introduces various new algorithmic challenges. One aspect is to find efficient driving directions in order to consume less energy in general and to account for special properties of EVs in particular. Besides the length of the route, one could take various parameters into account, such as altitude maps, congestion probabilities, the weather forecast, multi-modality, the energy consumption of a fleet or of the overall traffic. We present some models to account for stochastic elements, such as congestion, traffic lights or similar uncertainties. Two particular models will be used to optimize either the success probability (i.e. the chance to reach your destination with the current battery charge) or the conditional expectation value of the energy use (given that a minimal success rate is satisfied). By adapting an algorithm from Uludag et al., we developed an algorithm to approach the mentioned energy-optimal path problems. Furthermore we provide a unified routing model to account for time-dependency and energy-constraints as well as stochasticity.



Wednesday, 16:15 - 16:40 h, Room: H 3003A, Talk 3

Marco Gavanelli
Simulation and optimization for sustainable policy-making

Coauthors: Michela Milano, Fabrizio Riguzzi


Policy-making for European regions is becoming more and more challenging.
Good policies should take into account environmental
sustainability, economic factors and social acceptance of the policy.
Optimization promises to improve currently adopted, hand-made
solutions and may mean large savings for the taxpayers and lower
depletion of limited resources, given the scale of regional planning.
On the other hand, the effectiveness of a policy depends strongly on
the response of the population, which cannot be easily foreseen. In
fact, it is the emerging behavior of a complex system, for which one
can, at most, exploit a simulator. From this, one wishes to extract
mathematical relationships to be modeled as constraints.
In order to extract significant information from simulations, they
should be run a statistically significant number of times, and the
results aggregated through statistical analysis or machine learning.
We show how optimization has been applied in the regional energy plan of the Emilia-Romagna region, in Italy.
We propose an approach for the combination of simulation and
combinatorial optimization that we evaluate experimentally.


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