Customer Identification for Electricity Retailers Based on Monthly Demand Profiles by Activity Sectors and Locations

📝 Abstract

The increasing competition in the electric sector is challenging retail companies as they must assign its commercial efforts to attract the most profitable customers. Those are whose energy demand best fit certain target profiles, which usually depend on generation or cost policies. But, even when the demand profile is available, it is in an anonymous way, preventing its association to a particular client. In this paper, we explore a large dataset containing several millions of monthly demand profiles in Spain and use the available information about the associated economic sector and location for an indirect identification of the customers. The distance of the demand profile from the target is used to define a key performance indicator (KPI) which is used as the main driver of the proposed marketing strategy. The combined use of activity and location has been revealed as a powerful tool for indirect identification of customers, as 100,000 customers are uniquely identified, while about 300,000 clients are identifiable in small sets containing 10 or less consumers. To assess the proposed marketing strategy, it has been compared to the random attraction of new clients, showing a reduction of distance from the target of 40% for 10,000 new customers.

💡 Analysis

The increasing competition in the electric sector is challenging retail companies as they must assign its commercial efforts to attract the most profitable customers. Those are whose energy demand best fit certain target profiles, which usually depend on generation or cost policies. But, even when the demand profile is available, it is in an anonymous way, preventing its association to a particular client. In this paper, we explore a large dataset containing several millions of monthly demand profiles in Spain and use the available information about the associated economic sector and location for an indirect identification of the customers. The distance of the demand profile from the target is used to define a key performance indicator (KPI) which is used as the main driver of the proposed marketing strategy. The combined use of activity and location has been revealed as a powerful tool for indirect identification of customers, as 100,000 customers are uniquely identified, while about 300,000 clients are identifiable in small sets containing 10 or less consumers. To assess the proposed marketing strategy, it has been compared to the random attraction of new clients, showing a reduction of distance from the target of 40% for 10,000 new customers.

📄 Content

URING the last decades, many countries have liberalized in some extent their electricity markets [1]. From a situation where only one or a few state-owned companies were operating, the new regulation process has driven to a scenario with many actors playing different roles along the power delivery chain. Among them, it can be found several production companies that own and manage generation plants. Usually a single transmission system operator (TSO) manages the highvoltage grid. Later, a few distribution system operators (DSOs) are in charge of medium-and low-voltage networks [2]. Finally, the electricity retailers are who carry out marketing, billing, maintenance and customer service activities [3]. An overview of this structure in Europe can be found in [4], [5].

Regarding the retailer level, the competition is especially tough as an increasing number of new companies are being introduced. For instance, Spain has 257 active nationwide retailers (2019 data), becoming the European country with the high number of them and which had experienced the highest increase in this number within the European Union [6].

Electricity retailing can be characterized as a managing risk activity. For medium to long-term (for instance, one year), retailers have to balance two strategic decisions: the procurement of energy in the future trading and the selling price offered to their clients. Also, in the short term, retailers must make decisions associated to the pool trading. As there are uncertainties associated to the prices in the future while the selling prices are fixed, the management of risk is the corner stone of electricity retailing [7]. Then, the actors operating in the electricity retailing arena, focus on optimize their trading portfolio based on a risk assessment, and combining the procurement and selling of energy [8].

Retailers have some alternatives to obtain the electricity, such as spot market, forward contracts, call options or even selfproduction. They can also finance the investment required for some customers to embrace self-consumption, for instance by installing solar panels, and to tie up that customer for the whole amortization period [9]. With these inputs, retailers can employ several algorithmics methods to decide the optimal selling prices that maximize the benefits in the short and long term [10]. These prices are offered to the customers in several structured tariffs, which consider the cost of producing and distributing the energy and, additionally, send signals to foster more convenient consumptions (for instance, off-peak or greener electricity demand) [11].

The decision-making process of the electricity retailer includes long-term retail load forecasting, power procurement strategies, retail pricing schemes and risk management in the retail market. A survey of these activities and their evolution in recent years can be found in [12]. Several initiatives have also been proposed for an energy retail market strategy that engage companies and consumers in a joint compromise for the transition to a net-zero energy systems, where the electricity is fully produced from renewable and zero-emission sources [13].

Several analytics methods have been proposed to help retailers in their decision-making process, such as electricity customer profiling, demand response and dynamic pricing, or peer-to-peer electricity trading. It is also common to use modelling tools for power system planning, power market simulation, power system simulation, and power consumption analysis [14].

In an increasingly competitive retailing market, the electricity trading companies mainly contend adjusting prices, but also by diversifying products, offering differential prices, expanding channels and deepening cooperation, and using big data analysis to promote users’ electricity consumption behavior. The impact of these strategies can be mathematically modelled and assessed [15].

In the retailer’s decisions not only technical or economic issues have to be considered. Also, psychological behavior of electricity customers must be anticipated, such as the emotional drivers for selecting a certain retailer company, the reasons for its loyalty to that company, the motivations for leaving, or the willingness to interrupt certain loads under peak demand conditions [16], [17].

Despite the theoretical advantages of a liberalized electricity sector, the retailing market have also shown certain problems. Many newcomers broke, abandoned the market, were taken over, or evolved towards integration into generation for market hedging purposes [18]. The remaining retailers have been forced to dedicate increasing resources to marketing, selling and customer services. This cost is also greater if the current non-negligible churn rate (between 10% to 28% in the Spanish market, depending on the type of customer) [19], is included in the equation. And finally, these costs are partly or fully charged to customers which suffers the contra-int

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