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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. P2P Energy Trading through Prospect Theory, Differential Evolution, and Reinforcement Learning

   https://doi.org/10.1145/3603148  

   Timilsina, Ashutosh; Silvestri, Simone (May 2023, ACM Transactions on Evolutionary Learning and Optimization)

   Peer-to-peer (P2P) energy trading is a decentralized energy market where local energy prosumers act as peers, trading energy among each other. Existing works in this area largely overlook the importance of user behavioral modeling, assume users’ sustained active participation, and full compliance in the decision-making process. To overcome these unrealistic assumptions, and their deleterious consequences, in this paper we propose an automated P2P energy trading framework that specifically considers the users’ perception by exploiting prospect theory . We formalize an optimization problem that maximizes the buyers’ perceived utility while matching energy production and demand. We prove that the problem is NP-hard and we propose a Differential Evolution-based Algorithm for Trading Energy ( DEbATE ) heuristic. Additionally, we propose two automated pricing solutions to improve the sellers’ profit based on reinforcement learning. The first solution, named Pricing mechanism with Q-learning and Risk-sensitivity ( PQR ), is based on Q-learning. Additionally, the given scalability issues of PQR , we propose a Deep Q-Network-based algorithm called ProDQN that exploits deep learning and a novel loss function rooted in prospect theory. Results based on real traces of energy consumption and production, as well as realistic prospect theory functions, show that our approaches achieve \(26\% \) higher perceived value for buyers and generate \(7\% \) more reward for sellers, compared to recent state-of-the-art approaches. 

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3. A Blockchain based Framework for Secure and Decentralized Energy Trading in a Community

   https://doi.org/10.1109/ICIT58465.2023.10143101  

   Sachdeva, Saurabh; Fatehaj, Latif; Tan, Stefan; James, Joel; Ajayi, Oluwaseyi; Saadawi, Tarek (April 2023, IEEE)

   Blockchain is a decentralized, digital, and distributed ledger which allows transparent and secure information sharing among the peer-to-peer network. It eliminates the need for a centralized trusted party and, though it was introduced as the backbone technology for cryptocurrencies but has proved to be a promising and revolutionary technology for almost all global industries. The application of blockchain technology in the energy sector proposes a paradigm of solutions to problems of different levels of complexity in the traditional energy ecosystem. Extensive research has been proposed to exploit the inherent benefits of blockchain technology for the integration of distributed energy sources and facilitate peer-to-peer energy trading. This paper proposes a blockchain-based architecture to facilitate secure and decentralized energy trading generated from renewable energy sources. The solution utilizes the Ethereum blockchain and Smart Contracts for energy trading among the members of a small community without any trusted third entity and adopts features to achieve data integrity and confidentiality, and user identity privacy. 

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4. PV-EV Integrated Home Energy Management Considering Residential Occupant Behaviors

   https://doi.org/10.3390/su132413826  

   Liu, Xuebo; Wu, Yingying; Wu, Hongyu (December 2021, Sustainability)

   Rooftop photovoltaics (PV) and electrical vehicles (EV) have become more economically viable to residential customers. Most existing home energy management systems (HEMS) only focus on the residential occupants’ thermal comfort in terms of indoor temperature and humidity while neglecting their other behaviors or concerns. This paper aims to integrate residential PV and EVs into the HEMS in an occupant-centric manner while taking into account the occupants’ thermal comfort, clothing behaviors, and concerns on the state-of-charge (SOC) of EVs. A stochastic adaptive dynamic programming (ADP) model was proposed to optimally determine the setpoints of heating, ventilation, air conditioning (HVAC), occupant’s clothing decisions, and the EV’s charge/discharge schedule while considering uncertainties in the outside temperature, PV generation, and EV’s arrival SOC. The nonlinear and nonconvex thermal comfort model, EV SOC concern model, and clothing behavior model were holistically embedded in the ADP-HEMS model. A model predictive control framework was further proposed to simulate a residential house under the time of use tariff, such that it continually updates with optimal appliance schedules decisions passed to the house model. Cosimulations were carried out to compare the proposed HEMS with a baseline model that represents the current operational practice. The result shows that the proposed HEMS can reduce the energy cost by 68.5% while retaining the most comfortable thermal level and negligible EV SOC concerns considering the occupant’s behaviors. 

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5. Flexible PV-cell Modeling for Energy Harvesting in Wearable IoT Applications

   https://doi.org/https://doi.org/10.1145/3126568  

   Park, Jaehyun; Joshi, Hitesh; Lee, Hyung Gyu; Kiaei, Sayfe; Ogras, Umit Y. (October 2017, ACM transactions on embedded computing systems)

   Wearable devices with sensing, processing and communication capabilities have become feasible with the advances in internet-of-things (IoT) and low power design technologies. Energy harvesting is extremely important for wearable IoT devices due to size and weight limitations of batteries. One of the most widely used energy harvesting sources is photovoltaic cell (PV-cell) owing to its simplicity and high output power. In particular, flexible PV-cells offer great potential for wearable applications. This paper models, for the first time, how bending a PV-cell significantly impacts the harvested energy. Furthermore, we derive an analytical model to quantify the harvested energy as a function of the radius of curvature. We validate the proposed model empirically using a commercial PV-cell under a wide range of bending scenarios, light intensities and elevation angles. Finally, we show that the proposed model can accelerate maximum power point tracking algorithms and increase the harvested energy by up to 25.0%. 

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