A Fair Pricing Mechanism in Smart Grids for Low Energy Consumption Users

Abstract

Based on energy demand, consumers can be broadly categorized into low energy consumers (LECs) and high energy consumers (HECs). HECs use heavy load appliances, e.g., electric heaters and air conditioners, and LECs do not use heavy load appliances. Thus, HECs demand more energy compared to LECs. The usage of high energy consumption appliances by HECs leads to peak formation in various time intervals. Different pricing schemes, i.e., time of use (ToU), real time pricing (RTP), inclined block rate (IBR), and critical peak pricing (CPP), have been proposed previously. In ToU, an energy tariff is divided into three blocks, i.e., on-peak (high rates), off-peak (low rates), and mid-peak (between on-peak and offpeak rates) hours, and these rates are applied to all electricity users without distinction. The high energy demand by HECs causes the high peak formation; thus, higher rates should be applied to only HECs rather than all consumers, which is not the case in existing billing mechanisms. LECs are also charged higher rates in on-peak intervals and this billing mechanisms are unjustified. Thus, in this paper, a fair pricing scheme (FPS) based on power demand forecasting is developed to reduce extra bills of LECs. First, we developed a machine learning-based electricity load forecasting method, i.e., an extreme learning machine (ELM), in order to differentiate LECs and HECs. With the proposed FPS, electricity cost calculations for LECs and HECs are based on the actual energy consumption; thus, LECs do not subsidize HECs. Simulations were conducted for performance evaluation of our proposed FPS mechanism, and the results demonstrate LECs can reduce electricity cost up to 11.0075%, and HECs are charged relatively higher than previous pricing schemes as a penalty for their contribution to the on-peak formation. INDEX TERMS Smart grid; low energy consumers; pricing tariff; load forecasting; extreme learning machines; time of use; fair pricing scheme;

Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid

“…It will ensure that energy output and use are in order. The consumer engages in SG services by shifting demand from maximum to off-hours and conserving resources and saving money on energy [3]. DSM allows customers to monitor their energy usage patterns based on the price set by the utility.…”

An Incentive Based Dynamic Pricing in Smart Grid: A Customer’s Perspective

“…The authors identified a trade-off between cost reduction and comfort maximization and developed a Game theoretic-based algorithm to overcome this trade-off. In [32], the authors proposed a new pricing mechanism for low and high energy consumption users based on a time of use pricing policy. The price signal is further divided into different blocks and forecasted load is scheduled based on these prices to curtail the cost of end users.…”

An optimal home energy management system with integration of renewable energy and energy storage with home to grid capability

“…In order to benefit from the DR programs, a home energy management system (HEMS) must be installed in the home. 7 The scheduling of residential appliances is optimized, the ESS/EV is managed, and customers profit financially due to the adoption of HEMS. It also achieves a decent balance between power prices and the home's peak to average ratio (PAR).…”