Supervisor:
Zhe Chen

Co-Supervisor:
Yanbo Wang

Assessment Committee:
Sanjay Chaudhary (Chair)
Ioannis Lestas, Department of Engineering, University of Cambridge
Fengqi You, Cornell University

Moderator:
Sanjay Chaudhary

Abstract:

Over the past few decades, rapid industrialization, urbanization, and economic growth have driven increasing fossil fuel consumption, significantly exacerbating environmental and climate challenges. Regional integrated energy system (RIES) offers one promising platform to achieve the environmental goal of net zero emissions (NZE) through the efficient control technologies of multi-energy. In this way, it is crucial to develop advanced energy management strategies to facilitate the collaboration of different energies, particularly emerging ones such as hydrogen.

Hence, this project investigates the optimal energy management strategies of RIES. At first, the RIES integrated with hydrogen energy is modelled, which includes the electricity-hydrogen conversion process, energy storage units, demand response (DR) program, and operation constraints for distributed units and networks. And a day-ahead optimal scheduling model is established to maximize the economic benefits of the RIES operator.

Following this, an energy bidding mechanism within a market environment is further developed for RIES. The transaction mode of the RIES is presented, involving key stakeholders such as the electricity-hydrogen operator (EHO), regional electricity-hydrogen prosumer (REHP), and load aggregator (LA). Then, a Stackelberg-equilibrium-based optimization approach is proposed to implement the electricity-hydrogen management in a RIES, which is solved by differential evolutionary algorithm (DEA) combined with quadratic programming (QP). Demonstration cases validate the proposed strategy, showing its capacity to analyze the business behaviors of market participants. The proposed energy management strategy enhances economic benefits for all the stakeholders and improves hydrogen utilization.

Additionally, a collaborative charging management strategy for EVs-HVs is proposed using a Stackelberg game model. This approach establishes a transaction framework for electricity-hydrogen exchange between the vehicle fleet aggregator (VFA) and the RIES subsystem operators. A bi-layer Stackelberg game optimization model is established to obtain the electricity-hydrogen Distribution Locational Marginal Price (DLMP), which is solved using the DEA-MILP approach. The proposed strategy is validated through simulations conducted on an IEEE 33-bus power system and a 6-node hydrogen system. Simulation results demonstrate that the bidding mechanism effectively determines DLMP and enables collaborative EV-HV charging management. And the energy cost of VFA is optimized and the power congestion in RIES is alleviated.

Finally, to address the real-time energy management challenge in systems with high penetration of multiple flexible loads (FLs), a cooperative scheduling strategy for RIES is proposed under a multi-agent system (MAS) consistency framework. A hierarchical optimal operation framework is first designed, followed by the development of a distributed control framework. Then, the optimization models for these two operation modes are established, aiming to minimize total operation costs and optimize the distribution of electrical and heat power. To ensure the consistent convergence of agents, the operational rule based on the following heat load (FHL) strategy is introduced for the hierarchical distributed control mode (HDCM), while the hill-climbing search (HCS) model is proposed for the distributed control mode (DCM). Simulation results demonstrate the strategy's effectiveness in optimizing cooperative operations and ensuring real-time balancing between energy generation and load demand, showcasing its efficiency and adaptability to random load variations.