Estimating the Unobservable Components of Electricity Demand Response with Inverse Optimization

• URL: http://arxiv.org/abs/2410.02774v1
• Date: Tue, 17 Sep 2024 14:17:31 GMT
• Title: Estimating the Unobservable Components of Electricity Demand Response with Inverse Optimization
• Authors: Adrian Esteban-Perez, Derek Bunn, Yashar Ghiassi-Farrokhfal,
• Abstract summary: Understanding and predicting electricity demand responses to prices are critical activities for system operators, retailers, and regulators. The emergence of active consumers with flexible assets such as solar-plus-storage systems, and electric vehicles, introduces new challenges. We define the main components of net demand, as baseload, flexible, and self-generation, each having nonlinear responses to market price signals.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding and predicting the electricity demand responses to prices are critical activities for system operators, retailers, and regulators. While conventional machine learning and time series analyses have been adequate for the routine demand patterns that have adapted only slowly over many years, the emergence of active consumers with flexible assets such as solar-plus-storage systems, and electric vehicles, introduces new challenges. These active consumers exhibit more complex consumption patterns, the drivers of which are often unobservable to the retailers and system operators. In practice, system operators and retailers can only monitor the net demand (metered at grid connection points), which reflects the overall energy consumption or production exchanged with the grid. As a result, all "behind-the-meter" activities-such as the use of flexibility-remain hidden from these entities. Such behind-the-meter behavior may be controlled by third party agents or incentivized by tariffs; in either case, the retailer's revenue and the system loads would be impacted by these activities behind the meter, but their details can only be inferred. We define the main components of net demand, as baseload, flexible, and self-generation, each having nonlinear responses to market price signals. As flexible demand response and self generation are increasing, this raises a pressing question of whether existing methods still perform well and, if not, whether there is an alternative way to understand and project the unobserved components of behavior. In response to this practical challenge, we evaluate the potential of a data-driven inverse optimization (IO) methodology. This approach characterizes decomposed consumption patterns without requiring direct observation of behind-the-meter behavior or device-level metering [...]

Related papers

• Power Hungry Processing: Watts Driving the Cost of AI Deployment? [74.19749699665216]
  generative, multi-purpose AI systems promise a unified approach to building machine learning (ML) models into technology. This ambition of generality'' comes at a steep cost to the environment, given the amount of energy these systems require and the amount of carbon that they emit. We measure deployment cost as the amount of energy and carbon required to perform 1,000 inferences on representative benchmark dataset using these models. We conclude with a discussion around the current trend of deploying multi-purpose generative ML systems, and caution that their utility should be more intentionally weighed against increased costs in terms of energy and emissions
  arXiv  Detail & Related papers  (2023-11-28T15:09:36Z)
• Charge Manipulation Attacks Against Smart Electric Vehicle Charging Stations and Deep Learning-based Detection Mechanisms [49.37592437398933]
  "Smart" electric vehicle charging stations (EVCSs) will be a key step toward achieving green transportation. We investigate charge manipulation attacks (CMAs) against EV charging, in which an attacker manipulates the information exchanged during smart charging operations. We propose an unsupervised deep learning-based mechanism to detect CMAs by monitoring the parameters involved in EV charging.
  arXiv  Detail & Related papers  (2023-10-18T18:38:59Z)
• Equitable Time-Varying Pricing Tariff Design: A Joint Learning and Optimization Approach [0.0]
  Time-varying pricing tariffs incentivize consumers to shift their electricity demand and reduce costs, but may increase the energy burden for consumers with limited response capability. This paper proposes a joint learning-based identification and optimization method to design equitable time-varying tariffs.
  arXiv  Detail & Related papers  (2023-07-26T20:14:23Z)
• Machine learning applications for electricity market agent-based models: A systematic literature review [68.8204255655161]
  Agent-based simulations are used to better understand the dynamics of the electricity market. Agent-based models provide the opportunity to integrate machine learning and artificial intelligence. We review 55 papers published between 2016 and 2021 which focus on machine learning applied to agent-based electricity market models.
  arXiv  Detail & Related papers  (2022-06-05T14:52:26Z)
• Functional Model of Residential Consumption Elasticity under Dynamic Tariffs [0.0]
  One of the major barriers for the retailers is to understand the consumption elasticity they can expect from their contracted demand response (DR) clients. The elasticity of consumers demand behavior varies from individual to individual. This work proposes a functional model for the consumption elasticity of the DR contracted consumers.
  arXiv  Detail & Related papers  (2021-11-22T15:29:50Z)
• Investigating Underlying Drivers of Variability in Residential Energy Usage Patterns with Daily Load Shape Clustering of Smart Meter Data [53.51471969978107]
  Large-scale deployment of smart meters has motivated increasing studies to explore disaggregated daily load patterns. This paper aims to shed light on the mechanisms by which electricity consumption patterns exhibit variability.
  arXiv  Detail & Related papers  (2021-02-16T16:56:27Z)
• Activity Detection And Modeling Using Smart Meter Data: Concept And Case Studies [6.7336801526732755]
  This paper proposes a new and more effective approach, i.e., activity disaggregation. We develop a framework by leverage machine learning for activity detection based on residential load data and features. We show through numerical case studies to demonstrate the effectiveness of the activity detection method.
  arXiv  Detail & Related papers  (2020-10-26T02:36:35Z)
• Intelligent Residential Energy Management System using Deep Reinforcement Learning [5.532477732693001]
  This paper proposes a Deep Reinforcement Learning (DRL) model for demand response where the virtual agent learns the task like humans do. Our method outperformed the state of the art mixed integer linear programming (MILP) for load peak reduction.
  arXiv  Detail & Related papers  (2020-05-28T19:51:22Z)
• A Hierarchical Approach to Multi-Energy Demand Response: From Electricity to Multi-Energy Applications [1.5084441395740482]
  This paper looks into an opportunity to control energy consumption of an aggregation of many residential, commercial and industrial consumers. This ensemble control becomes a modern demand response contributor to the set of modeling tools for multi-energy infrastructure systems.
  arXiv  Detail & Related papers  (2020-05-05T17:17:51Z)
• Demand-Side Scheduling Based on Multi-Agent Deep Actor-Critic Learning for Smart Grids [56.35173057183362]
  We consider the problem of demand-side energy management, where each household is equipped with a smart meter that is able to schedule home appliances online. The goal is to minimize the overall cost under a real-time pricing scheme. We propose the formulation of a smart grid environment as a Markov game.
  arXiv  Detail & Related papers  (2020-05-05T07:32:40Z)
• Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems [87.4519172058185]
  An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
  arXiv  Detail & Related papers  (2020-02-20T04:58:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.