Design for Optimized Multi-Lateral Multi-Commodity Markets

In this paper, we propose a design for an an economically efficient, optimized, centralized, multi-lateral, periodic commodity market that addresses explicitly three issues: (i) substantial transportation costs between sellers and buyers; (ii) non homogeneous, in quality and nature, commodities; (iii) complementary commodities that have to be traded simultaneously. The model allows sellers to offer their commodities in lots and buyers to explicitly quantify the differences in quality of the goods produced by each individual seller. The model does not presume that products must be shipped through a market hub. We also propose a multi-round auction that enables the implementation of the direct optimized market and approximates the behaviour of the "ideal" direct optimized mechanism. The process allows buyers and sellers to modify their initial bids, including the technological constraints. The proposed market designs are particularly relevant for industries related to natural resources. We present the models and algorithms required to implement the optimized market mechanisms, describe the operations of the multi-round auction, and discuss applications and perspectives. Nous présentons un concept de marché optimisé, centralisé, multilatéral et périodique pour l'acquisition de produits qui traite explicitement les trois aspects suivants: (i) des coûts de transport importants des vendeurs vers les acheteurs; (ii) des produits non homogènes en valeur et qualité; des complémentarités entre les divers produits qui doivent donc être négociés simultanément. Le modèle permet aux vendeurs d'offrir leurs produits groupés en lots et aux acheteurs de quantifier explicitement leur évaluation des lots mis sur le marché par chaque vendeur. Le modèle ne suppose pas que les produits doivent être expédiés par un centre avant d'être livrés. Nous proposons également un mécanisme de tâtonnement à rondes multiples qui approxime le comportement du marché direct optimisé et qui permet de mettre ce dernier en oeuvre. Le processus de tâtonnement permet aux vendeurs et aux acheteurs de modifier leurs mises initiales, incluant les contraintes technologiques. Les concepts proposés sont particulièrement adaptés aux industries reliées aux matières premières. Nous présentons les modèles et algorithmes requis à la mise en oeuvre du marché multi-latéral optimisé, nous décrivons le fonctionnement du processus de tâtonnement, et nous discutons les applications et perspectives reliées à ces mécanismes de marché.Market design, optimized multi-lateral multi-commodity markets, multi-round auctions, Design de marché, marché multi-latéraux optimisés, processus de tâtonnement

Distributed Energy Trading: The Multiple-Microgrid Case

In this paper, a distributed convex optimization framework is developed for energy trading between islanded microgrids. More specifically, the problem consists of several islanded microgrids that exchange energy flows by means of an arbitrary topology. Due to scalability issues and in order to safeguard local information on cost functions, a subgradient-based cost minimization algorithm is proposed that converges to the optimal solution in a practical number of iterations and with a limited communication overhead. Furthermore, this approach allows for a very intuitive economics interpretation that explains the algorithm iterations in terms of "supply--demand model" and "market clearing". Numerical results are given in terms of convergence rate of the algorithm and attained costs for different network topologies.Comment: 24 pages, 8 figures; new version answering reviewers' comments; the paper is now accepted for publication in the IEEE Transactions on Industrial Electronics; the paper is now publishe

Data-Driven Dynamic Robust Resource Allocation: Application to Efficient Transportation

The transformation to smarter cities brings an array of emerging urbanization challenges. With the development of technologies such as sensor networks, storage devices, and cloud computing, we are able to collect, store, and analyze a large amount of data in real time. Modern cities have brought to life unprecedented opportunities and challenges for allocating limited resources in a data-driven way. Intelligent transportation system is one emerging research area, in which sensing data provides us opportunities for understanding spatial-temporal patterns of demand human and mobility. However, greedy or matching algorithms that only deal with known requests are far from efficient in the long run without considering demand information predicted based on data. In this dissertation, we develop a data-driven robust resource allocation framework to consider spatial-temporally correlated demand and demand uncertainties, motivated by the problem of efficient dispatching of taxi or autonomous vehicles. We first present a receding horizon control (RHC) framework to dispatch taxis towards predicted demand; this framework incorporates both information from historical record data and real-time GPS location and occupancy status data. It also allows us to allocate resource from a globally optimal perspective in a longer time period, besides the local level greedy or matching algorithm for assigning a passenger pick-up location of each vacant vehicle. The objectives include reducing both current and anticipated future total idle driving distance and matching spatial-temporal ratio between demand and supply for service quality. We then present a robust optimization method to consider spatial-temporally correlated demand model uncertainties that can be expressed in closed convex sets. Uncertainty sets of demand vectors are constructed from data based on theories in hypothesis testing, and the sets provide a desired probabilistic guarantee level for the performance of dispatch solutions. To minimize the average resource allocation cost under demand uncertainties, we develop a general data-driven dynamic distributionally robust resource allocation model. An efficient algorithm for building demand uncertainty sets that compatible with various demand prediction methods is developed. We prove equivalent computationally tractable forms of the robust and distributionally robust resource allocation problems using strong duality. The resource allocation problem aims to balance the demand-supply ratio at different nodes of the network with minimum balancing and re-balancing cost, with decision variables on the denominator that has not been covered by previous work. Trace-driven analysis with real taxi operational record data of San Francisco shows that the RHC framework reduces the average total idle distance of taxis by 52%, and evaluations with over 100GB of New York City taxi trip data show that robust and distributionally robust dispatch methods reduce the average total idle distance by 10% more compared with non-robust solutions. Besides increasing the service efficiency by reducing total idle driving distance, the resource allocation methods in this dissertation also reduce the demand-supply ratio mismatch error across the city