Market Strategy Evaluation for Prosumers in Local Electricity Markets

• URL: http://arxiv.org/abs/2607.18272v1
• Date: Fri, 12 Jun 2026 09:41:39 GMT
• Title: Market Strategy Evaluation for Prosumers in Local Electricity Markets
• Abstract summary: Prosumers equipped with distributed generation and flexible loads form autonomous cyber-physical energy systems that control local resources and participate in local energy markets with minimal human intervention.<n>This work develops and evaluates an agent-based simulation platform in which agents represent prosumer households with photovoltaic systems, battery storage systems, electric vehicles, and heat pumps.<n>The effect of individual bidding strategies on community-level efficiency and prosumer-level financial outcomes is incompletely understood, particularly when prosumers with heterogeneous portfolios interact in one market.
• Score: 0.12744523252873352
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prosumers equipped with distributed generation and flexible loads form autonomous cyber-physical energy systems that control local resources and participate in local energy markets with minimal human intervention. This work develops and evaluates an agent-based simulation platform in which agents, representing prosumer households with photovoltaic systems, battery storage systems, electric vehicles, and heat pumps, participate in a uniform-price double-sided call auction. The effect of individual bidding strategies on community-level efficiency and prosumer-level financial outcomes is incompletely understood, particularly when prosumers with heterogeneous portfolios interact in one market. Four market strategies of increasing complexity are compared: a zero-intelligence constrained baseline, a boundary-price strategy, an extended storage cascade, and a market-adaptive pricing strategy. The simulation is conducted on a community of 33 prosumers at 15-minute resolution, spanning summer, winter, and spring to characterize seasonal variation. Results show that rule-based resource control substantially reduces community energy expenditure: the extended storage cascade achieves a total cost of 39.06 EUR compared to 62.38 EUR under the zero-intelligence baseline, a reduction of 37.4 %. The market-adaptive strategy yields the highest aggregate community financial gain through local energy market participation (14.40 EUR vs. 10.28 EUR for the baseline, a gain of 40.1 %) under summer conditions. Strategy effectiveness depends on both portfolio composition and seasonal supply conditions, requiring joint evaluation of resource control and pricing decisions.

Related papers

• Hybrid Kolmogorov-Arnold Network and XGBoost Framework for Week-Ahead Price Forecasting in Australia's National Electricity Market [3.1887815332625764]
  This paper investigates week-ahead electricity price forecasting in the Australian National Electricity Market.<n>It proposes a hybrid KAN+XGBoost framework that integrates Kolmogorov-Arnold Networks (KAN) with tree-based learning.<n>The proposed approach combines the global nonlinear representation capability of KAN with the local robustness of XGBoost to capture both long-term dependencies and short-term price fluctuations.
  arXiv  Detail & Related papers  (2026-05-21T12:19:58Z)
• EcoGym: Evaluating LLMs for Long-Horizon Plan-and-Execute in Interactive Economies [61.267836443398124]
  We introduce EcoGym, a benchmark for continuous plan-and-execute decision making in interactive economies.<n>EcoGym is released as an open, testbed for transparent long-horizonity agent evaluation and for studying controllability-utility trade-offs in realistic economic settings.
  arXiv  Detail & Related papers  (2026-02-10T08:12:23Z)
• Scalable Fairness Shaping with LLM-Guided Multi-Agent Reinforcement Learning for Peer-to-Peer Electricity Markets [3.7321070110102075]
  Peer-to-peer (P2P) energy trading is becoming central to modern distribution systems.<n>A fairness-aware multiagent reinforcement learning framework, FairMarket-RL, is proposed.<n>The framework shifts exchanges toward local P2P trades, lowers consumer costs relative to grid-only procurement, sustains strong fairness across participants, and preserves utility viability.
  arXiv  Detail & Related papers  (2025-08-26T02:25:17Z)
• VAE-GAN Based Price Manipulation in Coordinated Local Energy Markets [3.498661956610689]
  This paper introduces a model for coordinating prosumers with heterogeneous distributed energy resources (DERs) in a local energy market (LEM)<n>The proposed LEM scheme utilizes a data-driven, model-free reinforcement learning approach based on the multi-agent deep deterministic policy gradient (MADDPG)<n>We investigate a price manipulation strategy using a variational auto encoder-generative adversarial network (VAE-GAN) model, which allows utilities to adjust price signals in a way that induces financial losses for the prosumers.
  arXiv  Detail & Related papers  (2025-07-26T07:38:27Z)
• ML-Based Bidding Price Prediction for Pay-As-Bid Ancillary Services Markets: A Use Case in the German Control Reserve Market [45.62331048595689]
  This paper presents a methodology for forecasting bidding prices in pay-as-bid ancillary service markets, focusing on the German control reserve market.<n>We evaluate various machine learning models, including Support Vector Regression, Decision Trees, and k-Nearest Neighbors, and compare their performance against benchmark models.<n>Our analysis demonstrates that the proposed approach improves potential revenues by 27.43 % to 37.31 % compared to baseline models.
  arXiv  Detail & Related papers  (2025-03-21T15:21:43Z)
• Evaluating the Impact of Multiple DER Aggregators on Wholesale Energy Markets: A Hybrid Mean Field Approach [2.0535683313855055]
  The integration of distributed energy resources into wholesale energy markets can greatly enhance grid flexibility, improve market efficiency, and contribute to a more sustainable energy future.<n>We study a wholesale market model featuring multiple DER aggregators, each controlling a portfolio of DER resources and bidding into the market on behalf of the DER asset owners.<n>We propose a reinforcement learning (RL)-based method to help each agent learn optimal strategies within the MFG framework, enhancing their ability to adapt to market conditions and uncertainties.
  arXiv  Detail & Related papers  (2024-08-27T14:56:28Z)
• StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solving [76.5322280307861]
  StrategyLLM allows LLMs to perform inductive reasoning, deriving general strategies from specific task instances, and deductive reasoning, applying these general strategies to particular task examples, for constructing generalizable and consistent few-shot prompts. Experimental results demonstrate that StrategyLLM outperforms the competitive baseline CoT-SC that requires human-annotated solutions on 13 datasets across 4 challenging tasks without human involvement, including math reasoning (34.2% $rightarrow$ 38.8%), commonsense reasoning (70.3% $rightarrow$ 72.5%), algorithmic reasoning (73.7% $rightarrow$ 85.0
  arXiv  Detail & Related papers  (2023-11-15T09:18:09Z)
• Neural Fitted Q Iteration based Optimal Bidding Strategy in Real Time Reactive Power Market_1 [16.323822608442836]
  In real time electricity markets, the objective of generation companies while bidding is to maximize their profit. Similar studies in reactive power markets have not been reported so far because the network voltage operating conditions have an increased impact on reactive power markets. The assumption of a suitable probability distribution function is unrealistic, making the strategies adopted in active power markets unsuitable for learning optimal bids in reactive power market mechanisms.
  arXiv  Detail & Related papers  (2021-01-07T09:44:00Z)
• Exploring market power using deep reinforcement learning for intelligent bidding strategies [69.3939291118954]
  We find that capacity has an impact on the average electricity price in a single year. The value of $sim$25% and $sim$11% may vary between market structures and countries. We observe that the use of a market cap of approximately double the average market price has the effect of significantly decreasing this effect and maintaining a competitive market.
  arXiv  Detail & Related papers  (2020-11-08T21:07:42Z)
• A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Grids [58.666456917115056]
  This paper presents a Reinforcement Learning based energy market for a prosumer dominated microgrid. The proposed market model facilitates a real-time and demanddependent dynamic pricing environment, which reduces grid costs and improves the economic benefits for prosumers.
  arXiv  Detail & Related papers  (2020-09-23T02:17:51Z)
• 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)
• A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding [69.37299910149981]
  A key component for the successful renewable energy sources integration is the usage of energy storage. We propose a novel modelling framework for the strategic participation of energy storage in the European continuous intraday market. An distributed version of the fitted Q algorithm is chosen for solving this problem due to its sample efficiency. Results indicate that the agent converges to a policy that achieves in average higher total revenues than the benchmark strategy.
  arXiv  Detail & Related papers  (2020-04-13T13:50:13Z)

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.