Amir Noori

Amir Noori

This paper introduces some tools from graph theory and distributed consensus algorithms to construct an optimal, yet robust, hierarchical information sharing structure for large-scale decision making and control problems. The proposed method is motivated by the robustness and optimality of leaf-venation patterns. We introduce a new class of centrality measures which are built based on the degree distribution of nodes within network graph. Furthermore, the proposed measure is used to select the appropriate weight of the corresponding consensus algorithm. To this end, an implicit hierarchical structure is derived that control the flow of information in different situations. In addition, the performance analysis of the proposed measure with respect to other standard measures is performed to investigate the convergence and asymptotic behavior of the measure. Gas Transmission Network is served as our test-bed to demonstrate the applicability and the efficiently of the method.

Amir Noori, Babak Tavassoli, Alireza Fereidunian

Renewable prosumers face the complex challenge of balancing self-sufficiency with seamless grid and market integration. This paper introduces a novel prosumers network framework aimed at mitigating peak loads in Iran, particularly under the uncertainties inherent in renewable energy generation and demand. A cost-oriented integrated prediction and optimization approach is proposed, empowering prosumers to make informed decisions within a distributed contextual stochastic optimization (DCSO) framework. The problem is formulated as a bi-level two-stage multi-time scale optimization to determine optimal operation and interaction strategies under various scenarios, considering flexible resources. To facilitate grid integration, a novel consensus-based contextual information sharing mechanism is proposed. This approach enables coordinated collective behaviors and leverages contextual data more effectively. The overall problem is recast as a mixed-integer linear program (MILP) by incorporating optimality conditions and linearizing complementarity constraints. Additionally, a distributed algorithm using the consensus alternating direction method of multipliers (ADMM) is presented for computational tractability and privacy preservation. Numerical results highlights that integrating prediction with optimization and implementing a contextual information-sharing network among prosumers significantly reduces peak loads as well as total costs.

Amir Noori

This work proposes multi-agent systems setting for concurrent engineering system design optimization and gradually paves the way towards examining graph theoretic constructs in the context of multidisciplinary design optimization problem. The flow of the algorithm can be described as follow; generated estimates of the optimal (shared design) variables are exchanged locally with neighbor subspaces and then updated by computing a weighted sum of the local and received estimates. To comply with the consistency requirement, the resultant values are projected to local constraint sets. By employing the existing rules and results of the field, it has shown that the dual task of reaching consensus and asymptotic convergence of the algorithms to locally and globally optimal and consistent designs can be achieved. Finally, simulations are provided to illustrate the effectiveness and capability of the presented framework.

Amir Noori, Mohammad Bagher Menhaj, Masoud Shafiee

Gas Transmission Networks are large-scale complex systems, and corresponding design and control problems are challenging. In this paper, we consider the problem of control and management of these systems in crisis situations. We present these networks by a hybrid systems framework that provides required analysis models. Further, we discuss decision-making using computational discrete and hybrid optimization methods. In particular, several reinforcement learning methods are employed to explore decision space and achieve the best policy in a specific crisis situation. Simulations are presented to illustrate the efficiency of the method.