Dynamic Pricing Mechanism With the Integration of Renewable Energy Source in Smart Grid

IEEE Access

Nadeem Javaid¹  Muhammad Babar Rasheed²  Muhammad Awais Qureshi³  Thamer Alquthami⁴ 

[1] Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan;Department of Electronics and Electrical Systems, The University of Lahore, Lahore, Pakistan;Department of Technology, The University of Lahore, Lahore, Pakistan;Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah, Saudi Arabia;

关键词: Demand response;    optimization;    non-discriminatory prices;    individualized prices;    smart grid;    renewable energy;   

DOI  :  10.1109/ACCESS.2020.2967798

来源: DOAJ

【 摘 要 】

Day-ahead electricity pricing is an important strategy for electricity providers to improve grid stability through load scheduling. In this paper, we investigate a general framework for modelling electricity retail pricing based on load demand and market price information. Without any a priori knowledge, we have considered a finite time approach with dynamic system inputs. Our objective is to minimize the average system cost and rebound peaks through energy procurement price, load scheduling and renewable energy source (RES) integration. Initially, the energy consumption cost is calculated based on market clearing price and scheduled load. Then, through reformulation and subsequent modification of optimization problem, we utilize a day-ahead price information to construct individualized price profiles for each user, respectively. To analyse the applicability of proposed pricing policy, analytical solution is obtained which is further validated through comparison with solution obtained from genetic algorithm (GA). From results, it is observed that proposed price policy is non-discriminatory in nature and each user obtained a fair electricity tariff rather than a day-ahead price, which is based on load demand and consumption variation of other users. We also show that optimization problem is sequentially solved with bounded performance guarantee and asymptotic optimality. Finally, simulations are carried in different scenarios; aggregated load and market price, and aggregated load, individualized load, market price and proposed price. Results reveal that our proposed mechanism can charge the price to each user with 23.77% decrease or 5.12% increase based on system requirements.

【 授权许可】

Unknown