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1. Decentralized Voltage Control with Peer-to-peer Energy Trading in a Distribution Network

   Feng, Chen; Chen, Yihsu; Liu, Andrew L (January 2023, Proceedings of the 56th Hawaii International Conference on System Sciences)

   Utilizing distributed renewable and energy storage resources via peer-to-peer (P2P) energy trading has long been touted as a solution to improve energy system’s resilience and sustainability. Consumers and prosumers (those who have energy generation resources), however, do not have expertise to engage in repeated P2P trading, and the zero-marginal costs of renewables present challenges in determining fair market prices. To address these issues, we propose a multi-agent reinforcement learning (MARL) framework to help automate consumers’ bidding and management of their solar PV and energy storage resources, under a specific P2P clearing mechanism that utilizes the so-called supply-demand ratio. In addition, we show how the MARL framework can integrate physical network constraints to realize decentralized voltage control, hence ensuring physical feasibility of the P2P energy trading and paving ways for real-world implementations. 

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2. Cost-Optimized Microgrid Coalitions Using Bayesian Reinforcement Learning

   https://doi.org/10.3390/en14227481  

   Sadeghi, Mohammad; Mollahasani, Shahram; Erol-Kantarci, Melike (November 2021, Energies)

   Microgrids are empowered by the advances in renewable energy generation, which enable the microgrids to generate the required energy for supplying their loads and trade the surplus energy to other microgrids or the macrogrid. Microgrids need to optimize the scheduling of their demands and energy levels while trading their surplus with others to minimize the overall cost. This can be affected by various factors such as variations in demand, energy generation, and competition among microgrids due to their dynamic nature. Thus, reaching optimal scheduling is challenging due to the uncertainty caused by the generation/consumption of renewable energy and the complexity of interconnected microgrids and their interplay. Previous works mainly rely on modeling-based approaches and the availability of precise information on microgrid dynamics. This paper addresses the energy trading problem among microgrids by minimizing the cost while uncertainty exists in microgrid generation and demand. To this end, a Bayesian coalitional reinforcement learning-based model is introduced to minimize the energy trading cost among microgrids by forming stable coalitions. The results show that the proposed model can minimize the cost up to 23% with respect to the coalitional game theory model. 

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3. Peer-to-Peer Energy Trading and Grid Impact Studies in Smart Communities

   https://doi.org/10.1109/ICNC47757.2020.9049665  

   Elliott, Eric; Shanklin, Nicholas; Zehtabian, Sharare; Zhou, Qun; Turgut, Damla (February 2020, 2020 International Conference on Computing, Networking and Communications (ICNC))

   The rise of peer-to-peer (P2P) marketplace paradigms has transformed existing marketplace models, but the extent to which this approach can be applied to the energy marketplace has yet to be considered. In this paper, we examine existing approaches taken in the application of a P2P paradigm to the energy marketplace, further presenting an approach towards facilitating an online P2P energy marketplace, implementing a prototype P2P web application named SolTrade. Furthermore, we submit initial statistics based on simulated transactions facilitated through the platform, which illustrate the physical impact of marketplace transactions on the energy grid. In particular, these results show that, as the number of users rises, the chance of overloading the grid rises, but the chance of the grid being unable to sustain itself without an external source of energy falls. 

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4. Analysis and Optimization of Boost Converter Parameters in Internal Model Control for Voltage Source Converter-Based AC Microgrids

   Eyisi, Chiebuka; Li, Qifeng (September 2024, CSEE Journal of Power and Energy Systems)

   This paper investigates integration of distributed energy resources (DERs) in microgrids (MGs) through two-stage power conversion structures consisting of DC-DC boost converter and DC-AC voltage source converter (VSC) subsystems. In contrast to existing investigations that treated DC-link voltage as an ideal constant voltage, this paper considers the non-ideal dynamic coupling between both subsystems for completeness and higher accuracy, which introduces additional DC-side dynamics to the VSC. The analysis shows parameters of the boost converter's power model that impact stability through the DC-link. Carefully selecting these parameters can mitigate this effect on stability and improve dynamic performance across the DC-link. Hence, an optimization framework is developed to facilitate in selecting adequate boost converter parameters in designing a stable voltage source converter-based microgrid (VSC-MG). The developed optimization framework, based on particle swarm optimization, considers dynamic coupling between both subsystems and is also effective in avoiding inadequate boost converter parameters capable of propagating instability through the DC-link to the VSC. Simulations are performed with MATLAB/Simulink to validate theoretical analyses. 

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5. Voltage Stability Assessment of AC/DC Hybrid Microgrid

   https://doi.org/10.3390/en16010399  

   Chang, Fangyuan; O’Donnell, John; Su, Wencong (January 2023, Energies)

   AC/DC hybrid microgrids are becoming potentially more attractive due to the proliferation of renewable energy sources, such as photovoltaic generation, battery energy storage systems, and wind turbines. The collaboration of AC sub-microgrids and DC sub-microgrids improves operational efficiency when multiple types of power generators and loads coexist at the power distribution level. However, the voltage stability analysis and software validation of AC/DC hybrid microgrids is a critical concern, especially with the increasing adoption of power electronic devices and various types of power generation. In this manuscript, we investigate the modeling of AC/DC hybrid microgrids with grid-forming and grid-following power converters. We propose a rapid simulation technique to reduce the simulation runtime with acceptable errors. Moreover, we discuss the stability of hybrid microgrids with different types of faults and power mismatches. In particular, we examine the voltage nadir to evaluate the transient stability of the hybrid microgrid. We also design a droop controller to regulate the power flow and alleviate voltage instability. During our study, we establish a Simulink-based simulation platform for operational analysis of the microgrid. 

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