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Cameron A. G. MacRae

Publications and source records attributed to Cameron A. G. MacRae.

3 recordsLinked to original sources

A hybrid Benders decomposition and bees algorithm matheuristic approach to transmission expansion planning considering energy storage

This paper introduces a novel hybrid optimisation algorithm that combines elements of both metaheuristic search and integer programming. This new matheuristic combines elements of Benders decomposition and the Bees Algorithm, to create the Bee-Benders Hybrid Algorithm (BBHA) which retains many of the advantages both of the methods. Specifically it is designed to be easily parallelizable, to produce good solutions quickly while still retaining a guarantee of optimality when run for a sufficiently long time. The algorithm is tested using a transmission network expansion and energy storage planning model, a challenging and very large scale mixed integer linear programming problem. Transmission network planning problems are already difficult on their own. When including the planning for storage systems in the network, the variation of demand over time has to be taken into account significantly increasing the size and difficulty of the optimization problem. The BBHA is shown to be highly effective hybrid matheuristic algorithm that performs at least as well as either Benders decomposition or the Bees Algorithm where these are effective on their own, and significantly improves upon the individual approaches where neither component part has a pronounced advantage. While the paper demonstrates the effectiveness in terms of the concrete electricity network planning problem, the algorithm could be readily applied to any mixed integer linear program, and is expected to work particularly well whenever this has a structure that is amenable to Benders decomposition.

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Transmission Expansion Planning Considering Energy Storage

In electricity transmission networks, energy storage systems (ESS) provide a means of upgrade deferral by smoothing supply and matching demand. We develop a mixed integer programming (MIP) extension to the transmission network expansion planning (TEP) problem that considers the installation and operation of ESS as well as additional circuits. The model is demonstrated on the well known Garver's 6-bus and IEEE 25-bus test circuits for two 24 hour operating scenarios; a short peak, and a long peak. We show optimal location and capacity of storage is sensitive not only to cost, but also variability of demand in the network.

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Multi-objective integer programming: An improved recursive algorithm

This paper introduces an improved recursive algorithm to generate the set of all nondominated objective vectors for the Multi-Objective Integer Programming (MOIP) problem. We significantly improve the earlier recursive algorithm of Özlen and Azizoğlu by using the set of already solved subproblems and their solutions to avoid solving a large number of IPs. A numerical example is presented to explain the workings of the algorithm, and we conduct a series of computational experiments to show the savings that can be obtained. As our experiments show, the improvement becomes more significant as the problems grow larger in terms of the number of objectives.

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