In the rapidly evolving landscape of renewable energy, a groundbreaking study published by researchers from Shandong University of Technology is set to redefine how photovoltaic (PV) parks and electric vehicles (EVs) interact, promising significant cost savings and operational efficiencies. Led by Li Chenzhao, along with Chen Jiajia and Wang Jinghua from the College of Electrical and Electronic Engineering, the research introduces a novel approach to energy storage configuration that leverages the principles of the Stackelberg game and Information Gap Decision Theory (IGDT).
As the penetration of photovoltaics continues to rise, so does the volatility and randomness in user net load, leading to intensified peak and valley fluctuations in electricity demand. Energy storage systems (ESS) offer a solution by smoothing out these fluctuations, but their high initial investment has been a barrier to widespread adoption. The researchers’ innovative method addresses this challenge head-on.
“The key to our approach is the integration of EV demand response with energy storage optimization,” explained Li Chenzhao. “By considering the uncertainties in grid conditions, time-of-use pricing, and EV charging patterns, we’ve developed a model that minimizes costs for both the PV park and EV users.”
The study proposes a two-tiered strategy. First, it constructs an energy storage configuration model based on IGDT and an optimized operation model for EV clusters. Then, it employs a Stackelberg game model, where the PV park acts as the leader and EVs as followers, to minimize costs on both sides. This model is then solved using Karush-Kuhn-Tucker conditions and the dual theorem of linear programming.
The results are impressive. The proposed strategy reduced the annual comprehensive cost of the PV park by 12.06% and the charging and discharging costs of EV users by 54.88%. Moreover, the participation of EVs in park scheduling led to a significant reduction in storage configuration capacity and power by 62.80%, and on-grid power by 1.32%, thereby improving the local consumption rate of PV.
Compared to a robust optimization model, the IGDT model proved to be more economical, with the park cost being 1.97% lower. This finding underscores the potential of IGDT in achieving cost-effective energy storage solutions.
The implications of this research are far-reaching. For the energy sector, it offers a blueprint for integrating renewable energy sources with emerging technologies like EVs, creating a more resilient and efficient grid. For commercial entities, it presents an opportunity to reduce operational costs and enhance service offerings.
As the world transitions towards a more sustainable energy future, innovations like these will be crucial. The study, published in Dianli jianshe, which translates to ‘Electric Power Construction,’ marks a significant step forward in this journey. By bridging the gap between theoretical models and practical applications, Li Chenzhao and his team have set the stage for a new era in energy management.
The research not only highlights the potential of game theory and IGDT in energy storage optimization but also paves the way for future developments in smart grid technologies. As EV adoption continues to grow, the integration of these vehicles into the energy ecosystem will become increasingly important. This study provides a roadmap for achieving this integration in a cost-effective and efficient manner.
For energy providers and stakeholders, the message is clear: the future of energy lies in the synergy between renewable sources, advanced storage solutions, and intelligent demand response strategies. And with pioneering work like this, that future is closer than ever.
