DOI: 10.14457/tu.the.2024.544
title: Energy storage-driven hierarchical multi-communities energy sharing management system

description: Recent advancements in renewable energy (RE) technologies, along with reduced installation costs, have led to their widespread adoption. However, challenges arise due to the intermittent nature of RE influenced by variable weather conditions, impacting its reliability. Irregular human behavior and the widespread adoption of high-demand loads, like electric vehicles (EVs), lead to significant energy demand fluctuations. To address these challenges, the integration of energy storage systems (ESS) emerges as a crucial solution. ESS provides fast response times, managing surplus energy during off-peak periods and discharging stored energy during peaks. Strategic deployment of ESS, combined with energy sharing and demand-side management, enables smart grid communities to balance supply and demand, mitigating fluctuations, and reduce reliance on costly and environmentally harmful peak power plants. This dissertation aims to explore the integration of various forms of ESS into the hierarchical multiple levels of the electrical grid by proposing a framework called “hierarchical multi-communities energy-sharing management framework (hMESH) to provide efficient energy sharing management through three schemes: energy-sharing management for non-moving energy storage (eNMES), critical hour energy sharing management for partially moving energy storage (ePMES), and inter-community energy sharing management for fully moving energy storage (eFMES). The hMESH framework presents a structured analysis of energy-sharing approaches that consider different mobility characteristics of storage systems at distinct grid levels. Each scheme addresses specific operational and economic challenges within its respective layer, supported by original optimization models and equilibrium algorithms, thereby contributing to the development of flexible and adaptive smart grid solutions.The first scheme, energy-sharing management for non-moving energy storage (eNMES), focuses on a smart home environment, comprising multiple REs, home appliances, and multiple ESS units. This chapter proposes a novel scheme to address the challenge of minimizing energy loss in ESSs and optimal ESS capacity design. It utilizes distributed power-flow assignment combined with a load-shifting algorithm, where the optimal energy storage capacity is determined using linear programming techniques. The proposed scheme introduces new SPFA and MPFA algorithms to assign power flow, reduce energy loss, and minimize storage capacity. Its effectiveness is validated using real smart home data. The second scheme, critical hour energy sharing management for partially moving energy storage (ePMES), focuses on EVs with predictable usage patterns, specifically electric school buses (ESBs), often deployed at specific times and remaining idle for extended periods, making them practical for delivering vehicle-to-grid (V2G) ancillary services. It introduces a V2G model centered on ESBs in various schools within a single community, formulating the problem as a noncooperative game where the utility company (UC) determines the optimal incentive price for schools to discharge energy, minimizing additional costs during the peak demand period. Schools negotiate for the optimal discharged energy to maximize benefits during the peak period. The optimal energy price (OEP) algorithm is also introduced to achieve equilibrium, which is proven to be unique and always existent. Additionally, the model determines the optimal ESB battery capacity and discharge schedule during peak periods, demonstrating the practical value of coordinated V2G services from partially mobile storage in reducing peak loads and supporting grid reliability at the community level. The third scheme, inter-community energy sharing management for fully moving energy storage (eFMES), proposes a three-level energy-sharing model: utility company (UC) level, community energy aggregators (CEAs) level, and electric vehicles (EVs) level. In the smart grid, multiple communities exist, each with EVs inside. EVs possess the unique capability to travel between communities and engage in energy sharing through charge/discharge activities. The model is a three-level game, where UC at the upper level, supplies/buys electricity to/from the multi-community system and sets the multi-communities energy sharing price. CEAs, in the middle level, set optimal community energy sharing prices within their community. At the bottom level, each EV determines optimal charging and discharging energy, responding to energy sharing prices. All players aim to maximize their utility functions by choosing their best strategies. The scheme presents the optimal three-level energy-price (3OEP) algorithm to obtain an equilibrium that is proven to be unique and always existent. This contribution introduces a novel hierarchical multi-community model with two distinct pricing layers and incorporates EVs as mobile energy storage units, capable of transacting energy across community boundaries. The model provides a theoretical foundation for dynamic cross-community trading and pricing, supporting scalable coordination strategies in smart grids with high EV integration. The evaluation studies for the proposed three schemes of the hMESH framework were performed through simulations using MATLAB. The simulation results demonstrate the effectiveness of the proposed framework. The simulation on eNMES shows a reduction in energy loss and a significant decrease in energy storage capacity. Furthermore, the simulation on ePMES shows a reduction in the peak-to-average ratio and the bills for schools possessing ESBs, which help discharge energy to the grid during peak periods. Finally, the results for the eFMES scheme indicate a reduction in the peak caused by charging EVs, with a significant decrease in the peak-to-average ratio and the electricity bills of EV owners. This also leads to a much flatter load profile compared to the original charging profile, where there is no multi-communities energy sharing management system. The framework’s flexible and scalable architecture allows for its application to various grid sizes and regional contexts. The findings of this research not only advance the theoretical and algorithmic foundation of energy-sharing management but also offer actionable strategies for enhancing the reliability, efficiency, and economic viability of smart grids. By facilitating the integration of renewable resources and flexible storage technologies, the hMESH framework supports the transition toward resilient, sustainable, and economically optimized future energy systems.