Establishing a coordinated relationship among a distribution system operator (DSO) agent, electric vehicle integrator (EVA) agents, and electric vehicle (EV) agents is crucial for efficient charging scheduling conside...
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Establishing a coordinated relationship among a distribution system operator (DSO) agent, electric vehicle integrator (EVA) agents, and electric vehicle (EV) agents is crucial for efficient charging scheduling considering the rapid expansion of the electric vehicle market. In light of the challenges posed by a large number of EVs with diverse physical parameters and stochastic charging behaviors, we introduce a virtual group (VG) approach based on deadlines to aggregate EV arrivals at charging stations. Subsequently, we develop a novel hierarchicaldistributed multi-agent system (HDMAS), where the coordination problem among a DSO agent, EVA agents, and VG agents is transformed into an exchange problem and effectively solved in a distributed manner using the alternating direction method of multipliers (ADMM). To address the charging issue for each EV, we propose a policy called deadline-differentiated threshold charging (DDTC), which ensures that the charging rate of EVs within each VG is solely determined by their VG threshold. Finally, we provide simulation results using an independently developed simulator for NEU-ITS (Northeastern University -Intelligent Transmission System) to demonstrate the effectiveness and applicability of our proposed model. The experimental results demonstrate that the algorithm's complexity exhibits a linear relationship with the number of EVAs and VGs, while the complexity of the comparative models is linearly associated with the number of EVs.
This article presents a three-layer hierarchicaldistributed framework for optimal electric vehicle charging scheduling (EVCS). The proposed hierarchical EVCS structure includes a distribution system operator (DSO) at...
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This article presents a three-layer hierarchicaldistributed framework for optimal electric vehicle charging scheduling (EVCS). The proposed hierarchical EVCS structure includes a distribution system operator (DSO) at the top layer, electric vehicle aggregators (EVAs) at the middle layer, and electric vehicles (EVs) charging stations at the bottom layer. A single-loop iterative algorithm is developed to solve the EVCS problem by combining the alternating direction method of multipliers (ADMM) and the distribution line power flow model (DistFlow). Using the single-loop structure, the primal variables of all agents are updated simultaneously at every iteration resulting in a reduced number of iterations and faster convergence. The developed framework is employed to provide charging cost minimization at the EV charging stations level, peak load shaving at the EVAs level, and voltage regulation at the DSO level. In order to further improve the performance of the optimization framework, a neural network-based load forecasting model is implemented to include the uncertainties related to non-EV residential load demand. The efficiency and the optimality of the proposed EVCS framework are evaluated through numerical simulations, conducted for a modified IEEE 13 bus test feeder with different EV penetration levels.
One key aspect of power system operation is the minimization of line losses while maintaining voltages within certain limits. Doing so can be achieved through the use of voltage regulators, tap changing transformers, ...
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One key aspect of power system operation is the minimization of line losses while maintaining voltages within certain limits. Doing so can be achieved through the use of voltage regulators, tap changing transformers, and shunt capacitors. However, both tap changing transformers and/or shunt capacitors are operated in a discrete manner, i.e. the inputs can only take finitely many discrete values. Thus, the problem of minimizing line losses while maintaining voltage stability can be cast as a mixed-integer optimization problem. For large grids or cases where independent entities have concurrent control of the system, distributedoptimization provides a solution. The present paper discusses a hierarchically distributed approach;i.e. the problem is partitioned into local subproblems and solved using a mixed-integer extension of the Augmented Lagrangian based Alternating Direction Inexact Newton (ALADIN) algorithm. We present results for the IEEE 14- and 30-bus systems and compare them with previous approaches, such as particle swarm optimization and genetic algorithms. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
In this paper, a distributed trilayer multi-agent framework is proposed for optimal electric vehicle charging scheduling (EVCS). The framework reduces the negative effects of electric vehicle charging demand on the el...
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In this paper, a distributed trilayer multi-agent framework is proposed for optimal electric vehicle charging scheduling (EVCS). The framework reduces the negative effects of electric vehicle charging demand on the electrical grids. To solve the scheduling problem, a novel hierarchicaldistributed EV charging scheduling (HDEVCS) is developed as the exchange problem, where the agents are clustered based on their coupling constraints. According to the separability of the agents' objectives and the clusters' coupled constraints, HDEVCS is solved efficiently in a distributed manner by the alternating direction method of multipliers (ADMM). Comparing to the exiting trilayer methods, HDEVCS reduces the convergence time and the iteration numbers since its structure allows the agents to update their primal optimization variable simultaneously. The performance of HDEVCS is evaluated by numerical simulation of two small- and large-scale case studies consisting of 306 and 9051 agents, respectively. The results verify the scalability and efficiency of the proposed method, as it reduces the convergence time and iteration numbers by 60% compared to the state-of-the-art methods, flattens the load profile and decreases the charging cost considerably without violating the grid feeders' capacity. The significant outcome of our method is the accommodation of a large EV population without investment in grid expansion.
This investigation proposes an energy management system for large multizone commercial buildings that combines distributedoptimization with the adaptive learning. While the distributedoptimization provides scalabili...
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This investigation proposes an energy management system for large multizone commercial buildings that combines distributedoptimization with the adaptive learning. While the distributedoptimization provides scalability and models the fresh-air infusion as ventilation constraints, the learning algorithm simultaneously captures the influences of occupancy and user interactions. The approach employs a hierarchical architecture and uses a service-oriented framework to propose a distributedoptimization method for commercial buildings. In addition, it also includes operational constraints required for optimizing the building energy consumption not studied in the literature. We show that our hierarchical architecture provides much better scalability and minimal performance loss comparable to the centralized approach. We illustrate that the influences of operational constraints on chiller, duct, damper, and ventilation are important for studying the energy savings. The energy saving potential of the proposed approach is illustrated on a 10-zone building, while its scalability is shown via simulations on a 500-zone building. To study the robustness of the approach meeting cancellations or other events that influence zone thermal dynamics, the resulting energy savings are studied. The results demonstrate the advantages of the proposed algorithm in terms of scalability, energy consumption, and robustness.
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