Characterisation and Flexibility Assessment of Aggregate Electrical Demand

The renewable energy sources (RES) are intermittent in their nature and their integration in electric power grid has introduced the mismatch between supply and demand. This mismatch can be leveled by using the flexibilities from the supply and the demand side. The demand side in a power system has key importance in the evolving context of the energy systems. Electrical load patterns that represent the consumption level are affected by different types of uncertainties associated with customer’s behavior and with keeping acceptable comfort level. The resulting aggregated load pattern indicates the system response that may be more or less flexible in different periods of time. The distribution system operator in a microgrid is responsible for its secure and economic operation. Enhancing the knowledge on the aggregated behavior of these customers is particularly important for the distribution system operator, also with the aim of determining the potential flexibility of the demand and setting up the economic terms of the electricity provision to the customers. Extra charges due to high energy demand and contract violation penalties can be avoided using demand side flexibility. Demand side flexibility has many benefits in normal as well as emergency conditions like less cost and quick response. The study of aggregate residential demand for flexibility measures is important due to the diverse energy usage behavior of individual residents and conceptually, its availability all around the year for load management. Exploitation of possible flexibilities of the group of residential customer’s behavior is considered as an important option to promote demand response programs and to achieve greater energy savings. As far as the residential sector is concerned, a reasonable work can be found in the literature to assess the flexibility for the individual appliances, the aggregation of selected appliances. However, little work is found on the aggregation of residential units. Also, despite of many discussions about the concept of flexibility, the few mathematical definitions of flexibility available do not address the variation in time of the overall demand aggregation. There is a need to develop a methodology to extract flexibility information from aggregate electricity consumption behavior of the residents and develop useful flexibility indices for the aggregate residential loads. For this purpose, the first action required is to augment availability of information about the characteristics of aggregate electricity demand. The analysis of aggregate demand patterns is carried out by considering the demand pattern data representing the average power determined from the energy referring to a given time step duration. This thesis contains a comprehensive statistical analysis to investigate the effect of time step duration and aggregation level on load variation profile. Then the customer behavior about the change is demand is modeled using the binomial probability distribution. This model has led towards some novel definitions of flexibility indices. A new method based on the Beta probability distribution has been developed to generate the time coupled aggregate residential demand patterns, whose evolution depends on the uncertainties associated with the customer’s behavior. The outcome of this research work has also led towards defining the role of customers in microgrid application. For this purpose, a structure of the business model for a smart (mini) grid is proposed. The data sets used for all kind of analysis are generated for the different aggregations of the extra-urban residential customers using a bottom-up approach. The tools presented in this research work can be helpful for a system operator or an aggregator to assess demand side flexibilities, manage resources and efficiently use demand response programs. The findings of this work are also supportive to determine the metering structure for a microgrid application in which, by using current ICT technologies, it is possible to decide a compromise solution between the aggregation level and time step duration for smart metering. On the other hand, the research findings also led to the conclusion that the flexibility level for the individual residential customers is not so high to give economic benefits that make it attractive to participate in DR programs. From the studies, it seems that the problem is not with the technical aspects but with the current business model of the smart grids. For the future extension of this work, a framework of a new smart business model for smart (mini) grids, centric to customers, is presented. It is expected that the developments using the proposed background of the business model can lead towards a different era in the development of the power systems with the new wave of research; as new tools are required to embed economic and social considerations in planning the proposed architecture.