Browsing by Author "Dehghanpour, Kaveh"

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Agent-Based Modeling of Retail Electrical Energy Markets with Demand Response
(2018-08-01) Nehrir, Hashem; Dehghanpour, Kaveh; Sheppard, John W.; Kelly, Nathan
In this paper, we study the behavior of a Day-Ahead (DA) retail electrical energy market with price-based Demand Response (DR) from Air Conditioning (AC) loads through a hierarchical multiagent framework, employing a machine learning approach. At the top level of the hierarchy, a retailer agent buys energy from the DA wholesale market and sells it to the consumers. The goal of the retailer agent is to maximize its profit by setting the optimal retail prices, considering the response of the price-sensitive loads. Upon receiving the retail prices, at the lower level of the hierarchy, the AC agents employ a Q-learning algorithm to optimize their consumption patterns through modifying the temperature set-points of the devices, considering both consumption costs and users' comfort preferences. Since the retailer agent does not have direct access to the AC loads' underlying dynamics and decision process (i.e., incomplete information) the data privacy of the consumers becomes a source of uncertainty in the retailer's decision model. The retailer relies on techniques from the field of machine learning to develop a reliable model of the aggregate behavior of the price-sensitive loads to reduce the uncertainty of the decision-making process. Hence, a multiagent framework based on machine learning enables us to address issues such as interoperability and decision-making under incomplete information in a system that maintains the data privacy of the consumers. We will show that using the proposed model, all the agents are able to optimize their behavior simultaneously. Simulation results show that the proposed approach leads to a reduction in overall power consumption cost as the system converges to its equilibrium. This also coincides with maximization in the retailer's profit. We will also show that the same decision architecture can be used to reduce peak load to defer/avoid distribution system upgrades under high penetration of Photo-Voltaic (PV) power in the distribution feeder.
Market-based power management and control of resilient smart grids and microgrids using a game theoretic multi-agent system approach
(Montana State University - Bozeman, College of Engineering, 2017) Dehghanpour, Kaveh; Chairperson, Graduate Committee: M. Hashem Nehrir; Hashem Nehrir, John Sheppard and Nathan Kelly were co-authors of the article, 'Agent-based modeling in electrical energy markets using dynamic bayesian networks' in the journal 'IEEE transaction on power systems' which is contained within this thesis.; Hashem Nehrir, John Sheppard and Nathan Kelly were co-authors of the article, 'Agent-based modeling of retail electrical energy markets with demand response' in the journal 'IEEE transaction on smart grid' which is contained within this thesis.; Hashem Nehrir were co-authors of the article, 'Intelligent microgrid power management using the concept of nash bargaining solution' in 'Intelligent System Applications to Power Systems (ISAP) Conference, 2017' which is contained within this thesis.; Hashem Nehrir was a co-author of the article, 'Real-time multiobjective microgrid power management using distributed optimization in an agent-based bargaining framework' in the journal 'IEEE transaction on smart grid' which is contained within this thesis.; Hashem Nehrir was a co-author of the article, 'An agent-based hierarchical bargaining framework for power management of multiple cooperative microgrids' in the journal 'IEEE transaction on smart grid' which is contained within this thesis.; Hashem Nehrir was a co-author of the article, 'A market-based resilient power management technique for distribution systems with multiple microgrids using a multi-agent system approach' submitted to the journal 'Electric power components and systems' which is contained within this thesis.
In this dissertation, we address the problem of optimal resource allocation through distributed market-based techniques in future power systems. Several connected problems are considered in different stages of this research project: 1) optimal decision making of generation companies in wholesale markets, 2) demand response and energy pricing in retail markets, 3) single and multiple microgrid power management within the retail sector, and 4) introducing a resiliency-aware power management for microgrid-based distribution systems. Hence, this work addresses the challenges that are connected to the economics of energy. All of these problems are concerned with optimizing the behavior of the participants in electricity markets (in wholesale and retail sectors) to achieve certain objectives in power system operation (e.g., profit maximization, peak shaving, resiliency improvement, extreme event awareness and preparedness, etc.). Solution concepts from the fields of machine learning and game theory have been employed to address these problems. Moreover, distributed agent-based frameworks are designed for modeling the interactive decision making processes and implementing control laws in electricity markets. The goal is to introduce automated 'intelligent' decision making capabilities into power systems to improve the efficiency of grid operation. This is closely related to the concept of 'smart grids'. The proposed solution strategies are verified through numerical simulations in MATLAB environment. The results demonstrate that using the proposed distributed decision tools and machine-learning-based techniques we can improve the performance of power systems to achieve higher levels of controllability and efficiency, while enhancing system resiliency.
A Market-Based Resilient Power Management Technique for Distribution Systems with Multiple Microgrids Using a Multi-Agent System Approach.
(2019-01-24) Dehghanpour, Kaveh; Nehrir, Hashem
In this paper, we present a market-based resilient power management procedure for electrical distribution systems consisting of multiple cooperative MiroGrids (MGs). Distributed optimization is used to find the optimal resource allocation for the multiple MG system, while maintaining the local and global constraints, including keeping the voltage levels of the micro-sources within bounds. The proposed method is based on probabilistic reasoning in order to consider the uncertainty of the decision model in preparation for expected extreme events and in case of unit failure, to improve the resiliency of the system. Basically, the power management problem formulation is a multiobjective optimization problem, which is solved using the concept of Nash Bargaining Solution (NBS). The simulation results show that the proposed method is able to improve the resiliency of the system and prepare it for extreme events and unit failure, by increasing power reserve and modifying the operating point of the system to maintain voltage and power constraints across the MGs.
Real-Time Multiobjective Microgrid Power Management Using Distributed Optimization in an Agent-Based Bargaining Framework
(2018-11-01) Dehghanpour, Kaveh; Nehrir, Hashem
In this paper, we present a market-based resilient power management procedure for electrical distribution systems consisting of multiple cooperative MiroGrids (MGs). Distributed optimization is used to find the optimal resource allocation for the multiple MG system, while maintaining the local and global constraints, including keeping the voltage levels of the micro-sources within bounds. The proposed method is based on probabilistic reasoning in order to consider the uncertainty of the decision model in preparation for expected extreme events and in case of unit failure, to improve the resiliency of the system. Basically, the power management problem formulation is a multiobjective optimization problem, which is solved using the concept of Nash Bargaining Solution (NBS). The simulation results show that the proposed method is able to improve the resiliency of the system and prepare it for extreme events and unit failure, by increasing power reserve and modifying the operating point of the system to maintain voltage and power constraints across the MGs.
A Survey on Smart Agent-Based Microgrids for Resilient/Self-Healing Grids
(2017-05-01) Dehghanpour, Kaveh; Colson, Christopher; Nehrir, Hashem
This paper presents an overview of our body of work on the application of smart control techniques for the control and management of microgrids (MGs). The main focus here is on the application of distributed multi-agent system (MAS) theory in multi-objective (MO) power management of MGs to find the Pareto-front of the MO power management problem. In addition, the paper presents the application of Nash bargaining solution (NBS) and the MAS theory to directly obtain the NBS on the Pareto-front. The paper also discusses the progress reported on the above issues from the literature. We also present a MG-based power system architecture for enhancing the resilience and self-healing of the system.