New possibilities are developing in the infrastructure of electrical systems to meet the new demand of more volatile power generation. This study focuses on German household reactions to price changes and their economic and renewable utilization effects.   In order to model the effects of flexible prices in the Freiamt region, the basic research – including interviews and data collecting – is carried out in the fields of economics and renewable energy. An elasticity model based on the Spees and Lave study in used to simulate consumer behaviour to changing prices.   Two pricing structures with daily and hourly changing prices are found to lower the average electrical prices in both cases. These benefits are larger overall with the hourly price changes when all other variables are kept constant. This study finds that the changes to load patterns also seem to correlate with the local renewable energy production. Results suggest that this specific form of energy generation benefits from consumer reactions to changing prices during 2007 and 2008.   In order to validate these results the model should be expanded to include a more differentiated load from different sectors and to include a wider range of the electrical prices advertised to the consumer. However, under given circumstances, this study concludes that using more renewable power generation is possible both generally with daily price changes and also more specifically with hourly changing prices at a more competitive market price.

Radical changes to present day power systems will lead to power systems with a significant penetration of renewable energy sources and smartness, expressed in an extensive utilization of novel sensors and cyber secure Information and Communication Technology. Although these renewable energy sources prove to contribute to the reduction of CO2 emissions into the environment, its high penetration affects power system dynamic performance as a result of reduced power system inertia as well as less flexibility with regards to dispatching generation to balance future demand. These pose a threat both to the security and stability of future power systems. It is therefore very important to develop new methods through which power system security and stability can be maintained. This research investigated the development of methods through which the contributions of on-load tap changing transformers/transformer clusters could be assessed with the intent of developing real time adaptive voltage controlled demand response schemes for power systems. The development of such a scheme enables more active system components to be involved in the provision of frequency control as an ancillary service and deploys a new frequency control service with low infrastructural investment, bearing in mind that OLTC transformers are already very prevalent in power systems. In this thesis, a novel online adaptive supervisory controller for ensuring optimal dispatch of voltage-controlled demand response resources is developed. This novel controller is designed using the assessment results of OLTC transformer impacts on steady-state frequency and was tested for a variety of scenarios. To achieve the effective performance of the adaptive supervisory controller, the extensive use of statistical techniques for assessing OLTC transformer contributions to voltage controlled demand response is presented. This thesis also includes the use of unsupervised machine learning techniques for power system partitioning and the further use of statistical methods for assessing the contributions of OLTC transformer aggregates.