Ph.D. (Engg):: Development of Approaches for Optimal Shared Utilization of Spatially Distributed Resources Under Sparse Connectivity in Energy Internet

The global shift towards sustainable power generation has led to a significant rise in distributed energy resources (DERs), particularly from renewable sources. These localized generation systems, when combined with energy storage, form microgrids—self-contained units capable of managing generation and consumption. While energy storage enables time-shifted usage of intermittent renewable power, limitations in storage capacity and dynamic load variations still result in curtailment of generated energy. Peer-to-Peer (P2P) power trading among geographically adjacent prosumers offers a more energy-efficient and cost-effective alternative to grid feed-in. P2P trading enhances local energy utilization, optimizes resource use, and improves resilience in interconnected communities of microgrids. However, achieving full connectivity among all peers is infrastructure-intensive, while relying on sparse connectivity with indirect power exchange through intermediaries—facilitated via an Energy Internet (EI)—presents a scalable and feasible alternative. Within this context, the challenge becomes the optimal utilization of spatially distributed generation under connectivity constraints.
This thesis addresses this challenge by modelling realistic microgrid behaviour using multiple real-world electrical load datasets. Initially, internal power scheduling within a grid connected microgrid equipped with solar generation and storage is formulated as a mixed integer nonlinear optimization problem, later relaxed to a mixed-integer linear formulation to reduce computational complexity. Predictive scheduling is employed to enable time-shifted energy usage. The resulting surplus and deficit data form the basis for simulating P2P power exchange within a connected community. To evaluate trading under constrained infrastructure, the thesis introduces the Connectivity and Preference-constrained Hop-regulated P2P Trading (CPHPT) approach. CPHPT models P2P trading as a linear optimization problem that schedules energy exchange along shortest paths while respecting capacity and predefined hop limits. The internal microgrid scheduling and inter-microgrid trading are coordinated using a distributed control architecture, enhancing scalability and preserving data privacy. Graph theory is leveraged to avoid explicit route computation during hop-constrained scheduling. Theoretical analysis demonstrates that while full connectivity maximizes P2P power transfer, increasing the allowable hop count in sparsely connected communities enables near-optimal performance, albeit with higher routing complexity.
Building on this, the Optimal Multi-Path Power Routing (OMPR) algorithm is developed. OMPR uses graph-theoretic principles to determine the connectivity structure and identifies all feasible routes between peers deterministically. The power scheduling among the routes is formulated and solved as both a linear and nonlinear multi-path power scheduling optimization problem. The OMPR approach divides each multi-hop P2P exchange into multiple individual hop exchanges and solves each step-by-step. While routing multiple concurrent P2P exchanges, the order in which each P2P exchange is routed affects the optimal solution, as each route increases the power flow routing constraints. To overcome this limitation, a Hop Optimized Multi-Exchange Routing and Scheduling (HOMERS) algorithm that solves all concurrent multi-hop P2P exchanges simultaneously to obtain the optimal routing paths has been developed. HOMERS formulates the routing and scheduling of all concurrent P2P exchanges into a single-step mixed-integer nonlinear programming optimization problem. This approach efficiently identifies all feasible routes and schedules each power exchange, ensuring conflict-free power flow from the source to the destination in the predetermined number of hops.
Recognizing the limitations of assuming uniform node distribution and connectivity in the CPHPT and random connectivity for OMPR, and HOMERS models—the thesis proposes a more realistic framework named as a Spatial and Renewable resource Distribution Informed Network for Energy exchange (SRDINE). SRDINE accounts for non-homogeneous node spacing and variable power generation capabilities across the community. It identifies optimal connectivity topologies based on geographic and resource distribution, yielding improved connectivity efficiency. The resulting community specific connectivity model from SRDINE is shown to have better connectivity utilization and has improved efficiency compared to the ideal full connectivity. Through the development of CPHPT, OMPR, HOMERS, and STRIDE, this thesis makes substantial contributions to the field of distributed energy systems and P2P power trading. The integration of predictive scheduling, hop-constrained routing, and spatial-connectivity modelling offers a comprehensive and scalable framework for the future deployment of Energy Internet architectures. The research establishes a practical and theoretically grounded foundation for resilient, intelligent, and energy-efficient microgrid communities.
Speaker :  Neethu M

Research Supervisor : Prof Suresh Sundaram