In restructured electricity markets, energy retailers are profit-based entities that purchase electricity from the wholesale market at volatile prices and sell it to the consumers with a specified tariff. Electricity price and demand level are both important factors for retailers, but also uncertain, as they depend on the the consumption pattern of household consumers and the use of appliances which have different characteristics. By improving the consumption pattern of household consumers, residential retailers can determine market strategies for increasing their profit. On the other side, consumers can experience a reasonable reduction in electricity bills (Golmohamadi, Keypour, 2017).
What are the needs of energy retailers?
As such, retailers need to gain better insight into their customers’ demand and consumption for formatting better pricing strategies and increasing their profit. But, customers intentionally modify their electricity consumption patterns as a response to price fluctuation. Moreover, they may face extra costs to compensate for possible imbalances between the forecasted energy consumption of their portfolio and the actual supply from the network. This requires the right tools to analyze relevant data for optimizing their portfolio management.
Also, it is proven that high demand peaks have an even higher energy generation cost, which the retailer has to consider in the final electricity price. Thus, energy retailers should develop effective global pricing strategies for customers characterized by different energy behavior. At the same time, retailers need to obtain knowledge about the elasticity of individual consumers/, and groups of them in varying energy prices. Such information will help them define implicit demand response strategies (price-based) so as to mitigate the anticipated imbalances and avoid high charges.
Are you an energy retailer? We’ve got a solution for you!
BEYOND platform will focus on the development of algorithms for the accurate demand forecasting of the retailers’ customers, including flexibility predictions. At the same time, the management of the portfolio will become more effective since the purpose is to cluster and segment the retailers’ portfolios according to the customers’ profiles, flexibility loads, etc. This will enable an increased forecasting accuracy, and at the same time will help retailers to implement effective management of their portfolio and make reductions in the imbalance costs.
References
Golmohamadi, H., & Keypour, R. (2017). Retail energy management in electricity markets: structure, challenges and economic aspects-a review. Technology and Economics of Smart Grids and Sustainable Energy, 2(1), 1-15
