This paper focusses on the economic and socio-political preconditions and implications of low energy scenarios (LES) in the UK. It can be considered in two parts. The first section develops a generalised methodology for classifying and assessing alternative energy scenarios. Three scenario archetypes are identified: the conservation scenario; the renewable scenario; and the post-materialist scenario. The second part involves an examination of the various illustrative scenarios which have been advocated by the so-called low energy groups. In addition, since an assessment only has meaning if it allows for a comparison of a whole range of scenarios, a reference scenario (RES) has also been included. All these scenarios are subject to considerable uncertainties. These risks can be categorized as techno-economic, macro-economic, and socio-political uncertainties. LES in general are shown to have a greater techno-economic risk of failure than the RES since they incorporate a larger contribution from 'untried' energy supply and utilisation technologies. More importantly however, both the RES and LES would encounter the most serious obstacles in the socio-political spectrum. Since all LES embody a 'conservation scenario' they would be faced with a serious problem of providing an adequate incentive (whether carrot or whip) to ensure necessary take up of investments. This rests upon the assumption that individuals are myopic. A second fundamental socio-political risk concerns the acceptability of the energy supply facilities. In particular the expansion programmes for nuclear, synfuel and coal developments in the RES are not expected to be socially acceptable. On a less serious scale the 'renewable' LES would encounter obstacles in deploying wind, wave, solar and biomass technologies. With no evidence to suggest the development of post-materialist values the post-materialist scenarios must be considered the most implausible of all the scenarios.

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The thesis studies the subjects of bad data detection and state estimation in electrical power systems which are the processes whereby voltage, power flow and switch status measurements gathered continuously in real-time are used in conjunction with a model of the system to calculate the voltage levels at every node in the system. Traditionally the state estimation process requires two stages. The first stage is the pre-processing of the measurements by a bad data detector in an attempt to remove all the measurements which are grossly in error. The second is the calculation of the voltage levels by a state estimator from the remaining measurements which are likely to contain small random errors. Conventional state estimation algorithms are very sensitive to measurement errors, especially switch status errors, and unfortunately it is not possible to ensure that all the measurement errors are removed by the bad data detector. The thesis presents a new algorithm for state estimation utilising linear programming which is able to function in the presence of not only bad analogue measurements but also switch status measurement errors, thus removing the need for a bad data detector. The proposed method of state estimation is also able to include in its model of the system the individual busbars and bus-couplers within a substation. This feature enables the state estimation algorithm to process and provide additional network information thus leading to a more useful and reliable data base.

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