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1. Transactive energy and solarization: assessing the potential for demand curve management and cost savings

   https://doi.org/10.1145/3445034.3460510  

   Neema, Himanshu ; Phillips, Scott ; Lee, Dasom ; Hess, David J. ; Threet, Zachariah ; Roth, Thomas ; Nguyen, Cuong ( May 2021 , DESTION ’21: Design Automation for CPS and IoT)

   null (Ed.)

   Utilities and local power providers throughout the world have recognized the advantages of the "smart grid" to encourage consumers to engage in greater energy efficiency. The digitalization of electricity and the consumer interface enables utilities to develop pricing arrangements that can smooth peak load. Time-varying price signals can enable devices associated with heating, air conditioning, and ventilation (HVAC) systems to communicate with market prices in order to more efficiently configure energy demand. Moreover, the shorter time intervals and greater collection of data can facilitate the integration of distributed renewable energy into the power grid. This study contributes to the understanding of time-varying pricing using a model that examines the extent to which transactive energy can reduce economic costs of an aggregated group of households with varying levels of distributed solar energy. It also considers the potential for transactive energy to smooth the demand curve. 

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2. A Deep Reinforcement Learning Based Approach for Home Energy Management System

   https://doi.org/10.1109/ISGT45199.2020.9087647  

   Li, Hepeng ; Wan, Zhiqiang ; He, Haibo ( February 2020 , IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT 2020))

   Home energy management system (HEMS) enables residents to actively participate in demand response (DR) programs. It can autonomously optimize the electricity usage of home appliances to reduce the electricity cost based on time-varying electricity prices. However, due to the existence of randomness in the pricing process of the utility and resident's activities, developing an efficient HEMS is challenging. To address this issue, we propose a novel home energy management method for optimal scheduling of different kinds of home appliances based on deep reinforcement learning (DRL). Specifically, we formulate the home energy management problem as an MDP considering the randomness of real-time electricity prices and resident's activities. A DRL approach based on proximal policy optimization (PPO) is developed to determine the optimal DR scheduling strategy. The proposed approach does not need any information on the appliances' models and distribution knowledge of the randomness. Simulation results verify the effectiveness of our proposed approach. 

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3. Automatic Power Exchange for Distributed Energy Resource Networks: Flexibility Scheduling and Pricing

   https://doi.org/10.1109/CDC.2018.8619741  

   Qin, Junjie ; Mether, Jonathan ; Joo, Jhi-Young ; Rajagopal, Ram ; Poolla, Kameshwar ; Varaiya, Pravin ( December 2018 , IEEE Conference on Decision and Control, Miami)

   This paper proposes an Automatic Power Ex- change (APEX) that enables monetization of underutilized distribution system energy resources. APEX features an open- gate forward market design to incorporate uncertainty from variable resources, and an explicit flexibility market that schedules flexible resources based on information submitted by users through a simple yet expressive order format. We study the non-convex non-preemptive scheduling problem in APEX, proposing polynomial time algorithms with finite and asymptotic performance guarantees. We then analyze the prop- erties of marginal pricing, generalized to fit the APEX context with forward markets and distribution network constraints. We establish that it is revenue adequate but may lead to inadmissible prices for flexible orders. We then suggest a simple pricing mechanism that provably produces admissible prices for users and adequate revenue for APEX if implemented together with the proposed scheduling algorithms. 

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4. Pricing Conditional Value at Risk-Sensitive Economic Dispatch

   https://doi.org/10.1109/PESGM46819.2021.9637845  

   Ndrio, Mariola ; Madavan, Avinash N. ; Bose, Subhonmesh ( January 2021 , 2021 IEEE Power & Energy Society General Meeting (PESGM))

   There are growing concerns over the ability of current electricity market designs to adequately model and optimize against the stochastic nature of renewable resources such as wind and solar. In this paper, we consider an economic dispatch problem that explicitly accounts for said uncertainty and enforces network and generation limits using conditional value at risk. Our key contribution is the definition and analysis of risk-sensitive locational marginal prices (risk-LMPs) derived from such a market clearing problem. Risk-LMPs extend conventional LMPs to the uncertain setting. Settlements defined via risk-LMPs compensate resources for both energy and reserve schedules. We study these prices via sample average approximation (SAA) on example power networks to demonstrate their viability for electricity pricing with large-scale integration of renewables. 

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5. Price-Aware Deep Learning for Electricity Markets

   Dvorkin, Vladimir ; Fioretto, Ferdinando ( February 2024 , NeurIPS 2023 Workshop: Tackling Climate Change with Machine Learning)

   While deep learning gradually penetrates operational planning, its inherent prediction errors may significantly affect electricity prices. This letter examines how prediction errors propagate into electricity prices, revealing notable pricing errors and their spatial disparity in congested power systems. To improve fairness, we propose to embed electricity market-clearing optimization as a deep learning layer. Differentiating through this layer allows for balancing between prediction and pricing errors, as oppose to minimizing prediction errors alone. This layer implicitly optimizes fairness and controls the spatial distribution of price errors across the system. We showcase the price-aware deep learning in the nexus of wind power forecasting and short-term electricity market clearing. 

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