A Business Model-Oriented Platform with Applications for Developing Local Electricity Markets and Accelerating Clean Energy Transition (SMART-LEM)
Contract no. 43/2024
Project founded by UEFISCDI within PNCDI IV, total funding: 255.000 euro (budget 250.000 euro, self-funding 5.000 euro)
Coordinator: Academia de Studii Economice din Bucuresti (ASE), prof. Simona Oprea
Romanian Partner: Lara Business SRL, Dan Florea
Project progress
2024 – SCIENTIFIC REPORT ON DATA LEVEL MODELING AND BUSINESS MODELS FOR CON-/PROSUMERS
- Act 1.1 – Modeling the data level of the SMART-LEM platform • Definition of data model collection and integration requirements – LARA partner • Modeling the communication platform, data processing and integration flow. Big Data Storage Solution Modeling – ASE Coordinator
- Act 1.2 – Proposal of business models (BM) for con-/prosumers • Proposal of optimization models at consumer level – ASE coordinator
- Act 1.3 – Dissemination of results Activity
Summary:
A1.1 aimed to develop the data layer of the SMART-LEM platform, aiming at the integration and efficient processing of data from various sources to support business models in local energy markets (LEM). Defined requirements for data collection and integration, using IoT devices, sensors and other sources, with a focus on personalizing consumer comfort and optimizing energy resources. The proposed architecture includes two levels of processing: one local, based on edge computing, for initial data collection, and one in the cloud for aggregation and integration, the data being managed in a “data lake” connected to multiple sources. The Big Data storage solution allows ingesting and distilling data in real-time and in batch mode, preparing it for complex analysis. Business process models were developed using BPMN diagrams for prosumers, storage systems, EV charging stations, aggregators and local markets, optimizing transactions and resource utilization.
Activity A1.2 focused on the development of innovative business models for participants in local energy markets, including consumers, prosumers, aggregators and charging stations for electric vehicles. For prosumers, a monitoring and control module was proposed that optimizes the consumption and utilization of renewable energy through predictions, recommendations and optimizations. The models for the charging stations were oriented towards energy flexibility and smart charging, also addressing the use of batteries in the context of Vehicle-to-Grid. Aggregators are positioned as intermediaries that bring together multiple resources, acting as virtual powerhouses to provide service flexibility and limit the risks of price fluctuations. Business models for local markets provide a decentralized framework for trading renewable energy, reducing dependency on the main grid and maximizing resource efficiency.
An innovative component was proposed in the form of a virtual assistant, Energy Assistant (EA), which uses algorithms based on Artificial Intelligence (AI) to optimize consumption, transactions and cost reduction. This assistant has demonstrated, through simulations, significant improvements in energy self-sufficiency and financial savings for users.
The results of the 2024 stage were disseminated in 3 WoS indexed articles, 1 article in a BDI journal as well as in 3 international conferences.
| Journal papers published in 2024 |
| Irina Alexandra Georgescu, Simona-Vasilica Oprea, Adela Bâra, Investigating the relationship between macroeconomic indicators, renewables and pollution across diverse regions in the globalization era, Applied Energy, Volume 363, 1 June 2024, https://doi.org/10.1016/j.apenergy.2024.123077, WOS:001223392400001 |
| Adela Bâra, Simona-Vasilica Oprea, Electricity price forecast on day-ahead market for mid- and short terms: capturing spikes in data sequences using recurrent neural network techniques, Electrical Engineering, Volume 106, pages 6309–6338, Oct 2024 https://doi.org/10.1007/s00202-024-02393-w , WOS:001200309400001 |
| Simona-Vasilica Oprea, Adela Bâra, A Recommendation System for Prosumers Based on Large Language Models, Sensors, June 2024, https://doi.org/10.3390/s24113530, WOS:001245709800001 |
| Adela Bâra, Simona-Vasilica Oprea, Energy assistants for prosumers to enable trading strategies on local electricity markets, Knowledge-Based Systems, în curs de evaluare R2 |
| Anca Ioana Andreescu, Simona-Vasilica Oprea, Adela Bâra, Alin Gabriel Văduva, Andreea-Mihaela Niculae, Mapping Business Process Modeling with the Business Models of Several Energy Community Members, Database Systems Journal, Nov 2024, vol. XV, no. 1/2024, p. 23-37, ISSN 2069 – 3230 |
| International conferences in 2024 |
| Alexandra-Cristina-Daniela Ciuvercă, Sentiment Analysis of Research on AI Ethics: A Web-Based Study, The 7thInternational Conference on Economics and Social Sciences, June 13-14, 2024, Bucharest, Romania, published in Proceedings of the International Conference on Economics and Social Sciences, Editura ASE, ISSN: 2704-6524, pp. 672-681, https://doi.org/10.24818/icess/2024/060 |
| Andreea Vineș, Performance Evaluation of Data Vault and Dimensional Modeling: Insights from TPC-DS Dataset Analysis, The 23rd International Conference on Informatics in Economy (IE 2024), Education, Research & Business Technologies, 23-24 May 2024, Timișoara, România |
| Adela Bâra, Simona-Vasilica Oprea, Optimizing Energy Storage Systems. A Dynamic Framework for Capacity Allocation and Profit Maximization in Electricity Markets, The International Conference “Global Economy Under Crisis” – 13th Edition, Constanța, România, 11-12 Dec 2024, articolul va fi publicat în Ovidius University Annals. Economic Sciences Series – Vol. XXIV, Issue 2/2024, ISSN 2393-3127 |
2025 – SCIENTIFIC REPORT ON THE DEVELOPMENT OF DATA LEVEL AND BUSINESS MODELS FOR CHARGING STATIONS, AGGREGATORS AND LOCAL ENERGY MARKETS
- Act 2.1 – Development of the data level of the SMART-LEM platform
- Act 2.2 – Development of business models for con-/prosumers and charging stations
- Act 2.3 – Proposal of business models for aggregators and local markets (LEM)
- Act 2.4 – Dissemination of results
Summary:
The 2025 stage aimed at developing the level of data and business models for charging stations, aggregators and local electricity markets. The SMART-LEM platform was developed for modeling and simulating energy community models, including market mechanisms, membership management, weather data integration, and financial and energy performance analysis. Digital models have been defined for members and their assets – baseload consumption, PV/wind generation, battery storage, heat pumps and electric vehicles – allowing the detailed behavior of members of real communities and their interaction with the grid to be assessed.
Advanced business models for prosumers and smart buildings have been developed, based on the energy assistant EAB, a hierarchical edge-to-fog system that coordinates energy flexibility, optimizes costs and maximizes self-consumption by participating in DAM and IDM. Simulations demonstrated major improvements: cost reduction of over 70%, self-consumption of over 80% and increased energy autonomy, confirming the operational benefits of multi-level optimization.
Also, business models for aggregators and local markets (LEM) were proposed, through the FlexSym system and the NSGA-DEMIX multi-objective algorithm, which optimizes sequential trading strategies on the aFRR, DAM and IDM markets. Benchmarking revealed significant differences between an aggregator-governed and a community-managed model: communities achieve higher collective benefits, lower costs, and lower grid dependency, even if gross revenues are lower. The results confirm that community-oriented strategies are more sustainable and equitable.
The results of the 2025 phase were disseminated in 20 WoS indexed articles and in 2 international conferences. At the same time, the participation in the CETP Knowledge Community, Vienna, facilitated the connection of the project to similar European initiatives, the exchange of best practices and the strengthening of international collaboration.
| Journal papers published in 2025 |
| A. Bâra and S. -V. Oprea, Flexibility-driven strategies for the optimal scheduling of industrial batteries across stacked energy markets, Computers in industry (2025), Volume 172, 104346, 10.1016/j.compind.2025.104346. |
| A. Bâra, S.V. Oprea, Energy assistants for prosumers to enable trading strategies on local electricity markets, Knowledge-Based Systems, Volume 309, 2025, 112927, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2024.112927. |
| S.V. Oprea, A. Bâra, Cost outweighing environmental concerns in user preferences within peer-to-peer (P2P) local electricity markets, Expert Systems with Applications, Volume 292, 2025, 128611, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2025.128611. |
| A. Bâra, I.A. Georgescu, S.V. Oprea, The role of generation mix, demand fluctuations and sequential markets in shaping intraday electricity prices. Evidence from Romania, Renewable Energy, Volume 255, 2025, 123782, ISSN 0960-1481, https://doi.org/10.1016/j.renene.2025.123782 |
| Oprea, S.-V., Bâra, A., An adaptive pricing mechanism framework for post-auction in the local electricity markets integrating a higher share of renewables, Renewable Energy (2025), Volume 256, Part B, 124056 10.1016/j.renene.2025.124056. |
| Oprea, S.-V., & Bâra, A. (2025), Two-step price mechanism using Newton-Raphson method and peer-to-peer mediation for local electricity markets, Energy Strategy Reviews, Volume 59, 2025, 101701, ISSN 2211-467X, https://doi.org/10.1016/j.esr.2025.101701. (https://www.sciencedirect.com/science/article/pii/S2211467X25000641) |
| Bucur, C., Tudorică, B.-G., Bâra, A., & Oprea, S.-V. (2025). Multifractal analysis of Bitcoin price dynamics. Journal of Business Economics and Management, 26(1), 21–48. https://doi.org/10.3846/jbem.2025.23025 |
| Ciuverca, A.-C.-D., & Oprea, S. (2025). The influence of AI on price forecasting. The view of the academic community. Journal of Business Economics and Management, 26(1), 231–254. https://doi.org/10.3846/jbem.2025.23544 |
| S. -V. Oprea and A. Bâra, How Predictable Is Electric Vehicle Adoption? Exploring the Broader Role of Renewables in Transportation Using a Data-Driven Approach, in IEEE Access, vol. 13, pp. 141937-141957, 2025, doi: 10.1109/ACCESS.2025.3597101. |
| C. Iancu and S. -V. Oprea, AI and Human Resources in a Literature-Driven Investigation Into Emerging Trends, in IEEE Access, vol. 13, pp. 81897-81916, 2025, doi: 10.1109/ACCESS.2025.3568338 |
| Andreescu, A. I., Oprea, S., Văduva, A. G., & Bâra, A. (2025). Anti-Money Laundering Compliance Using Feature Engineering with SQL Analytics, TF-IDF and Oversampling: Conditional Tabular Generative Adversarial Networks. Informatica, 1-34. doi:10.15388/25-INFOR598 |
| Bâra, A., Georgescu, I. A., & Oprea, S.-V. (2025). Drivers of price volatility in Romania’s electricity markets. Journal of Business Economics and Management, 26(4), 958–981. https://doi.org/10.3846/jbem.2025.24709 |
| Oprea, S.-V., & Bâra, A. (2025). Is Artificial Intelligence a Game-Changer in Steering E-Business into the Future? Uncovering Latent Topics with Probabilistic Generative Models. Journal of Theoretical and Applied Electronic Commerce Research, 20(1), 16. https://doi.org/10.3390/jtaer20010016 |
| Oprea, S.-V., & Bâra, A. (2025). Customer-Centric Decision-Making with XAI and Counterfactual Explanations for Churn Mitigation. Journal of Theoretical and Applied Electronic Commerce Research, 20(2), 129. https://doi.org/10.3390/jtaer20020129 |
| Oprea, S.-V., & Bâra, A. (2025). Transforming Product Discovery and Interpretation Using Vision–Language Models. Journal of Theoretical and Applied Electronic Commerce Research, 20(3), 191. https://doi.org/10.3390/jtaer20030191 |
| Oprea, S.-V., & Bâra, A. (2025). Extracting Emotions from Customer Reviews Using Text Mining, Large Language Models and Fine-Tuning Strategies. Journal of Theoretical and Applied Electronic Commerce Research, 20(3), 221. https://doi.org/10.3390/jtaer20030221 |
| Oprea, S.V., Bâra, A. (2025), Analyzing shocks transmission and spillover effect in the day-ahead and intra-day markets. Key implications for price forecasting. J Knowl Econ. https://doi.org/10.1007/s13132-025-02603-1 |
| Bâra, A., Oprea, SV. Transformer-based forecasting with synthetic input data generation for day-ahead electricity markets. J. King Saud Univ. Comput. Inf. Sci. 37, 233 (2025). https://doi.org/10.1007/s44443-025-00259-0 |
| Georgescu, I. A., Bâra, A., & Oprea, S.-V. (2025). Life Expectancy and Its Determinants in Selected European Union (EU) and Non-EU Countries in the Mediterranean Region. Sustainability, 17(11), 5103. https://doi.org/10.3390/su17115103 |
| A.M. Tanasă, S.V. Oprea, Rethinking Chart Understanding Using Multimodal Large Language Models, Computers, Materials & Continua, https://doi.org/10.32604/cmc.2025.06542 |
| International conferences in 2025 |
| Alin-Gabriel Văduva, Anca-Ioana Andreescu, Simona-Vasilica, Assessing the Trustworthiness of Large Language Models: a Two-Stage Framework Using Ragas and LlamaIndex, The 24th International Conference on Informatics in Economy (IE 2025), May 15-16, 2025, Bucharest, Romania, published in Proceedings of the International Conference on Economics and Social Sciences, Editura ASE, ISSN: 2704-6524 |
| Simona Vasilica Oprea, Adela Bâra, Development of a Retrieval-Augmented Generation (RAG) chatbot, Conferința Internațională „Present Issues of Global Economy” (PIGE) – ediţia a 22-a, 19 – 21 iunie 2025, Constanta, “Ovidius” University Annals, Economic Sciences Series Volume XXV, Issue 1 /2025, pp 225-234, ISSN 2393-3119 |
Contact
prof. Simona Oprea: simona.oprea@csie.ase.ro
prof. Adela Bara: bara.adela@ie.ase.ro