The co-optimization of energy and reserves has become a standard requirement in integrated markets. This is due to the inverse relationship that exists between energy and reserves. The provision of reserves generally reduces the amount of primary energy a generating unit can produce and vice versa. This suggests that these products should be procured through a simultaneous auction to ensure optimal procurement and pricing. Furthermore, forward markets dictate that this co-optimization of energy and reserves be done over a multi-period planning horizon. This dissertation addresses the problem of optimal scheduling and pricing of energy and reserves over a multi-period planning horizon using an optimal power flow formulation. The extension of the problem from a static optimization problem to a dynamic optimization problem is presented. Price definitions for energy and reserves in terms of shadow prices emanating from the optimization algorithm are provided. It is shown that the proposed formulation of prices leads to the cascading of reserve prices and eliminates the problem of “price reversal” where lower quality reserves are priced higher than higher ii quality reserves. Pricing conditions are also established for the downward substitution of higher quality reserves for lower quality reserves. The proposed pricing formulations are tested on the IEEE 24 Bus Reliability Test System and on the South African power network. The simulated results show that cascading of reserve prices does occur and that prices of different types of reserves are equal when downward substitution of reserves occurs. Zonal reserve requirements result in higher energy and reserve prices, which in term result in higher procurement costs to the system operator and higher profits to market participants. Congestion on the network also results in higher procurement costs to the system operator and higher profits to market participants in the case of zonal pricing of reserves. / Dissertation (MEng)--University of Pretoria, 2013. / gm2014 / Electrical, Electronic and Computer Engineering / unrestricted

This thesis concerns the interdisciplinary field of combinatorial auctions, combining the fields of computer science, optimization and economics. A combinatorial auction is an auction where many items are sold simultaneously and where bidders may submit indivisible combinatorial bids on groups of items. It is commonly believed that good solutions to the allocation problem can be achieved by allowing combinatorial bidding. Determining who wins in a combinatorial auction is fundamentally different from a traditional single-item auction because we are faced with a hard and potentially intractable optimization problem. Also, unless we are limited to truthful mechanisms, game theoretic analysis of the strategic behavior of bidders is still an open problem. We have chosen primarily to study the first-price combinatorial auction, a natural auction widely used in practice. Theoretical analysis of this type of auction is difficult and little has been done previously. In this thesis we investigate and discuss three fundamental questions with significant practical implications for combinatorial auctions. First, because the number of possible bids grows exponentially with the number of items, limitations on the number of bids are typically required. This gives rise to a problem since bidders are unlikely to choose the "correct" bids that make up the globally optimal solution. We provide evidence that an expressive and compact bidding language can be more important than finding the optimal solution. Second, given a first-price (sealed-bid) combinatorial auction, the question of equilibrium bidding strategies remains an open problem. We propose a heuristic for finding such strategies and also present feasible strategies. And finally, is a first-price combinatorial auction worth pursuing compared to the simpler simultaneous (single-item) auction? We prove, through a model capturing many fundamental properties of multiple items scenarios with synergies, that the first-price combinatorial auction produces higher revenue than simultaneous single-item auctions. We provide bounds on revenue, given a significantly more general model, in contrast to previous work.