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A receding-horizon approach to short-term electricity markets


J. Warrington

Computational Management Science, Imperial College, London, UK

The incorporation of uncertain and rapidly-changing quantities of intermittent renewable energy is arguably the major challenge of the coming decades for the operators of electrical grids in those countries where the renewable share is expanding. This is because ever greater flexibility from the other market participants is needed in order for power production and consumption to be shaped around the exogenous power infeed. The difficulty of ensuring this flexibility is provided efficiently is compounded by the need for the solution to be compatible with market-based operation of the power system. A strategy explored in this work is the re-negotiation of prices and power volumes on a receding horizon basis, borrowing from predictive control principles. The aim of this approach is to ensure that the newest forecasts of exogenous power injections into the network are available at all times. This should improve efficiency by allowing market participants to adjust power consumption or production plans at the earliest possible stage. We report on the application of Lagrangian relaxation techniques to finite horizon (multi-period) power flow problems with diverse market participants, such as generators, storage devices, aggregated household appliances, and other consumers. The methods are demonstrated in the presence of network constraints, firstly for linearised "DC-approximated" grid models, and then on full AC grid models via a tight semidefinite relaxation of the power flow problem. We discuss the computational issues arising from attempting to solve the multi-period optimization using distributed methods. Finally, since these optimization techniques respect principles such as privacy of information and can therefore be interpreted as iterative market clearing mechanisms, their potential adaptation for use in real intraday electricity markets is discussed.


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M. Morari

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