Fuzzy Logic Dynamic Electricity Price Forecasting Model for Smart Grid

Abstract

Under restructuring of electric power industry, different participants namely generation companies and consumers of electricity need to meet in a marketplace to decide on the electricity price. Electricity price forecasting is an inherently difficult problem due to its special characteristics of dynamicity and nonstationarity. These characteristics can be attributed to the following reasons, which distinguish electricity from other commodities: non-storable nature of electrical energy, the requirement of maintaining constant balance between demand and supply, inelastic nature of demand over short time period, and oligopolistic generation side. In addition to these, market equilibrium is also influenced by both load and generation side uncertainties. Further deployment of the smart grid for power distribution enables a two-way communication between the supplier and the consumer. This enables the supplier to price the energy based on the consumption feedback from the consumer, and the consumer can schedule their consumption behavior to achieve optimal utilization. This thesis presents a solution methodology using fuzzy logic approach and rough set approach for hourly price forecasting, it is implemented on historical weather sensitive data of temperature and historical load data. The datasets used were of Khartoum state for the year 2013 collected in national control centre in Soba, Khartoum by NEC Sudan. The results obtained from the two different approaches were evaluated and compared, the results were satisfactory.