Convergence of Renewable Energy with Grids: A Pricing Approach

• Published in: 2023 International Conference on Power Energy, Environment & Intelligent Control (PEEIC) pp. 1190 - 1194
• Main Authors: Eti, Muktha, Sharma, Neeraj, Kumar, Sanjay
• Format: Conference Proceeding
• Language: English
• Published: IEEE 19.12.2023
• Subjects: Costs; Demand response; Electricity; Genetic algorithms; individualized prices; non-discriminatory prices; Optimization; Pricing; smart grid; Stability analysis; Sustainable development
• DOI: 10.1109/PEEIC59336.2023.10449971

For power suppliers hoping to improve grid stability by optimizing load scheduling, day-ahead electricity price is an essential tactic. This paper explores a general framework that incorporates load demand + market price data for simulating retail pricing of energy. The goal is to reduce average total system expenses while limiting peak rebounding via energy procurement pricing, load timetables, and the inclusion of sources of renewable energy (RES) using a finite-time technique with variable inputs but no a priori information. The first step is to compute the cost of energy consumption using the planned load and market clearing price. Day-ahead pricing information is then used to create unique price profiles for every user by rewriting and altering the optimization issue. An analytical answer is developed and verified by contrasting it with a solution created from a genetic algorithm, or GA, in order to evaluate the feasibility of the suggested pricing strategy. The findings show that, as opposed to a day-ahead price linked to other customers, the suggested pricing approach is non-discriminatory, guaranteeing that every user gets a fair power cost based on their load consumption as well as demand variance. In addition, the optimization problems is asymptotically optimum and has a limited performance guarantee when it is addressed sequentially. Experiments carried out in multiple scenarios-aggregated load and market cost, individual load, aggregated load, market price, along with proposed price-show that, depending on system requirements, the suggested mechanism can lead to a 23.77 % or 5.12% reduction in the price billed to each user.