The energy sector is undergoing a transformative shift, driven by advancements in Distributed Energy Resources (DERs), the digitization of the energy supply chain and decarbonization policy objectives across the world. This paradigm shift has led to the emergence of Local Energy Markets (LEMs), which enable small-scale prosumers to actively participate in the energy market, trade power, and leverage their flexible resources. To ensure the success and acceptance of LEMs, this paper proposes a cooperative game-theoretic approach that fosters prosumer engagement and fair profit allocation. We utilize prospect theory from behavioural economics to examine the decision-making process of prosumers and incorporate their preferences for changes in wealth status. By adopting a cooperative game structure, prosumers can pool their resources, reduce transaction costs, and enhance data utilization. The paper introduces a novel pricing algorithm inspired by prospect theory that incentivizes prosumer participation and accounts for the uncertainty involved in LEM operations. Additionally, a computationally efficient method for profit allocation based on the variation of the Shapley value is proposed to ensure scheme stability. A use case evaluation is conducted on a real-world low-voltage network, demonstrating the effectiveness of the proposed approach in terms of economic efficiency and market characteristics. The results highlight the benefits of the consumer-centric LEM, including improved local trading dynamics, fair profit distribution, and enhanced grid stability. Overall, this research contributes to the design and development of LEMs that prioritize prosumer engagement, community cooperation, financial inclusion and democratization of the energy market.

The upward trend of adopting Distributed Energy Resources (DER) reshapes the energy landscape and supports the transition towards a sustainable, carbon-free electricity system. The integration of Internet of Things (IoT) in Demand Response (DR) enables the transformation of energy flexibility, originated by electricity consumers/prosumers, into a valuable DER asset, thus placing them at the center of the electricity market. In this paper, it is shown how Local Energy Markets (LEM) act as a catalyst by providing a digital platform where the prosumers’ energy needs and offerings can be efficiently settled locally while minimizing the grid interaction. This paper showcases that the IoT technology, which enables control and coordination of numerous devices, further unleashes the flexibility potential of the distribution grid, offered as an energy service both to the LEM participants as well as the external grid. This is achieved by orchestrating the IoT devices through a Consumer Digital Twin (CDT), which facilitates the optimal adjustment of this flexibility according to the consumers’ thermal comfort level constraints and preferences. An integrated LEM-CDT platform is introduced, which comprises an optimal energy scheduler, accounts for the Renewable Energy System (RES) uncertainty, errors in load forecasting, Day-Ahead Market (DAM) feed in/out the tariff, and a fair price settling mechanism while considering user preferences. The results prove that IoT-enabled consumers’ participation in the energy markets through LEM is flexible, cost-efficient, and adaptive to the consumers’ comfort level while promoting both energy transition goals and social welfare. In particular, the paper showcases that the proposed algorithm increases the profits of LEM participants, lowers the corresponding operating costs, addresses efficiently the stochasticity of both energy demand and generation, and requires minimal computational resources.

Electricity price forecasting (EPF) has become an essential part of decision-making for energy companies to participate in power markets. As the energy mix becomes more uncertain and stochastic, this process has also become important for industrial companies, as their production schedules are greatly impacted by energy costs. Although various approaches have been tested with varying degrees of success, this study focuses on predicting day-ahead market (DAM) prices in different European markets and how this directly affects the optimal production scheduling for various industrial loads. We propose a fuzzy-based architecture that incorporates the results of two forecasting algorithms; a random forest (RF) and a long short-term memory (LSTM). To enhance the accuracy of the proposed model for a specific country, electricity market data from neighboring countries are also included. The developed DAM price forecaster can then be utilized by energy-intensive industries to optimize their production processes to reduce energy costs and improve energy-efficiency. Specifically, the tool is important for industries with multi-site production facilities in neighboring countries, which could reschedule the production processes depending on the forecasted electricity market price.

Data-driven machine learning-based methods have provided immense capabilities, revolutionizing sectors like the Buildings-to-grid (B2G) integrated system. Since the penetration rate of distributed energy resources increases towards a net-zero emissions power system, so does the need for advanced services that ensure B2G-integrated system reliability. The convergence of advancements in machine learning, computational resources at the entire cloud-edge continuum, and large datasets from sensing devices enable the development of these data-driven energy analytics services. This work conducts a systematic text-mining-based literature review to examine the diverse range and trends of machine learning methods used to enhance reliability in B2G-integrated systems. While traditional manual sampling and analysis approaches have limited the effectiveness of previous literature review papers in this field, this systematic literature review work aims to synthesize and summarize the existing body of research more efficiently and effectively. To achieve this, this study collected almost 10,500 papers from Scholar and Scopus databases. It employed text-mining-assisted BERTopic-based topic modelling and statistical trend analysis techniques to uncover semantic patterns and explore the temporal evolution of research themes. A two-dimensional taxonomy was derived to analyze the technical papers from a business and machine learning-related perspective. By quantifying the temporal trends within these topics, the study unveiled insights about the state-of-the-art analytics that ensure reliability in the B2G-integrated system domain while proposing future research directions.

An accurate prediction of imbalance prices is crucial for making well-informed decisions within short-term energy markets. This study proposes a two-stage probabilistic framework for the prediction of system imbalance and imbalance price. In the first stage, the quantiles of system imbalance are predicted employing the Quantile Regression Forest algorithm. In the second stage, the quantiles are leveraged to predict the imbalance prices, given the observed correlation between the system imbalance and the imbalance prices. The corresponding imbalance prices for each 15-minute interval are then predicted utilizing a novel methodology that combines Gradient Boosting with Markov switching models. The proposed methodology was evaluated using real data from the Greek balancing market. Comparative analysis with alternative models including a persistence model, Quantile Linear Regression, Long Short-Term Memory, and Quantile Regression Forest reveals that the proposed approach consistently outperforms the rest in terms of forecast accuracy. Specifically, the results demonstrate that the proposed approach outperforms all alternative methods, achieving an 9% improvement in nMAE and a 10.8% reduction in CRPS with respect to the second best method (Quantile Regression Forest). The findings suggest that this approach can significantly improve the decision-making process for market participants, helping mitigate the financial risks associated with imbalance prices.