Author
Ionela Prodan
Other affiliations: Grenoble Institute of Technology, Supélec, École Centrale Paris
Bio: Ionela Prodan is an academic researcher from University of Grenoble. The author has contributed to research in topics: Model predictive control & Microgrid. The author has an hindex of 13, co-authored 86 publications receiving 738 citations. Previous affiliations of Ionela Prodan include Grenoble Institute of Technology & Supélec.
Papers
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TL;DR: In this article, a framework for reliable microgrid energy management based on receding horizon control is proposed, which allows taking into consideration cost values, power consumption and generation profiles, and specific constraints.
165 citations
TL;DR: In this paper, a predictive control strategy for UAVs in the presence of bounded disturbances is proposed to prove the feasibility of such a real-time optimization-based control design and demonstrate its tracking capabilities for the nonlinear dynamics with respect to a reference trajectory.
63 citations
TL;DR: In this paper, an extension of a Model Predictive Control (MPC) approach for microgrid energy management is presented, which takes into account electricity costs, power consumption, generation profiles, power and energy constraints as well as uncertainty due to variations in the environment.
62 citations
TL;DR: Improvements in constraints handling for mixed-integer optimization problems with a novel element is the reduction of the number of binary variables used for expressing the complement of a convex (polytopic) region.
Abstract: This paper is concerned with improvements in constraints handling for mixed-integer optimization problems. The novel element is the reduction of the number of binary variables used for expressing the complement of a convex (polytopic) region. As a generalization, the problem of representing the complement of a possibly not connected union of such convex sets is detailed. In order to illustrate the benefits of the proposed improvements, a typical control application, the control of multiagent systems using receding horizon optimization techniques, is considered.
44 citations
TL;DR: This paper addresses a predictive control strategy for a particular class of multi-agent formations with a time-varying topology to guarantee tracking capabilities with respect to a reference trajectory which is pre-specified for an agent designed as the leader.
Abstract: This paper addresses a predictive control strategy for a particular class of multi-agent formations with a time-varying topology. The goal is to guarantee tracking capabilities with respect to a reference trajectory which is pre-specified for an agent designed as the leader. Then, the remaining agents, designed as followers, track the position and orientation of the leader. In real-time, a predictive control strategy enhanced with the potential field methodology is used in order to derive a feedback control action based only on local information within the group of agents. The main concern is that the interconnections between the agents are time-varying, affecting the neighborhood around each agent. The proposed method exhibits effective performance validated through some illustrative examples.
34 citations
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TL;DR: This paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies which are adaptive, distributed, asynchronous, and verifiably correct.
Abstract: This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.
2,198 citations
1,492 citations
TL;DR: A comparative and critical analysis on decision making strategies and their solution methods for microgrid energy management systems are presented and various uncertainty quantification methods are summarized.
617 citations
TL;DR: In this article, a robust optimization approach is adopted for considering forecast errors in load, variable renewable generation, and market prices, and the microgrid islanding is further treated as a source of uncertainty.
Abstract: This paper presents a model for the microgrid planning problem with uncertain physical and financial information. The microgrid planning problem investigates the economic viability of microgrid deployment and determines the optimal generation mix of distributed energy resources (DERs) for installation. Net metering is considered for exchanging power with the main grid and lowering the cost of unserved energy and DER investments. A robust optimization approach is adopted for considering forecast errors in load, variable renewable generation, and market prices. The microgrid islanding is further treated as a source of uncertainty. The microgrid planning problem is decomposed into an investment master problem and an operation subproblem. The optimal planning decisions determined in the master problem are employed in the subproblem to examine the optimality of the master solution by calculating the worst-case optimal operation under uncertain conditions. Optimality cuts sent to the master problem will govern subsequent iterations. Numerical simulations exhibit the effectiveness of the proposed model and further analyze the sensitivity of microgrid planning results on variety levels of uncertainty.
315 citations