Scholarly Work - Electrical & Computer Engineering
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/8814
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Item A Survey on Smart Agent-Based Microgrids for Resilient/Self-Healing Grids(2017-05-01) Dehghanpour, Kaveh; Colson, Christopher; Nehrir, HashemThis 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.Item Agent-Based Modeling of Retail Electrical Energy Markets with Demand Response(2018-08-01) Nehrir, Hashem; Dehghanpour, Kaveh; Sheppard, John W.; Kelly, NathanIn 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.Item Real-Time Multiobjective Microgrid Power Management Using Distributed Optimization in an Agent-Based Bargaining Framework(2018-11-01) Dehghanpour, Kaveh; Nehrir, HashemIn 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.Item 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, HashemIn 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.Item An Agent-Based Hierarchical Bargaining Framework for Power Management of Multiple Cooperative Microgrids(2019-01) Deghganpour, Kaveh; Nehrir, HashemIn this paper, we propose an agent-based hierarchical power management model in a power distribution system composed of several microgrids (MGs). At the lower level of the model, multiple MGs bargain with each other to cooperatively obtain a fair, and Pareto-optimal solution to their power management problem, employing the concept of Nash bargaining solution and using a distributed optimization framework. At the highest level of the model, a distribution system power supplier, e.g., a utility company, interacts with both the cluster of the MGs and the wholesale market. The goal of the utility company is to facilitate power exchange between the regional distribution network consisting of multiple MGs and the wholesale market to achieve its own private goals. The power exchange is controlled through dynamic energy pricing at the distribution level, at the day-ahead and real-time stages. To implement energy pricing at the utility company level, an iterative machine learning mechanism is employed, where the utility company develops a price-sensitivity model of the aggregate response of the MGs to the retail price signal through a learning process. This learned model is then used to perform optimal energy pricing. To verify its applicability, the proposed decision model is tested on a system with multiple MGs, with each MG having different load/generation data.