Projects
Manifesto: Advancing Renewable Energy Communities through AI and Machine Learning
Welcome to a transformative era fueled by the rapid progress of Artificial Intelligence (AI) and Machine Learning (ML). Our manifesto advocates for the pivotal role of AI and ML in revolutionizing the field of energy management, particularly in Renewable Energy Communities (RECs). With the growing adoption of renewable energy sources, optimizing their efficient utilization becomes paramount.
Traditionally, conventional energy management systems struggled to harness the full potential of renewables due to their unpredictable nature. In response, we embark on a research project that harnesses the power of AI and ML algorithms to tackle the complexities of energy management in RECs. By leveraging historical and real-time data, we aim to create intelligent systems that optimize renewable energy resources, minimize waste, and reduce carbon footprint.
Through our research, we aspire to achieve several objectives. Firstly, we will develop robust ML algorithms that accurately predict renewable energy generation and consumption patterns at various temporal scales. Secondly, we seek to create optimization models that dynamically balance energy supply and demand, catering to community requirements and available renewable resources. Lastly, our vision includes designing user-friendly algorithms that empower community members to actively engage in energy management, fostering sustainability and ownership.
By treating Smart Grids as complex technological ecosystems, we envision a future where AI and ML empower RECs to achieve unprecedented levels of energy efficiency and reliability. The outcomes of this research hold profound implications for the future of renewable energy communities. Our mission is to contribute significantly to mitigating climate change, reduce reliance on non-renewable energy sources, and pave the way for a greener, sustainable world. Embrace this revolutionary journey and be part of the solution that shapes the future of renewable energy through AI and Machine Learning. Together, let us build a cleaner, brighter, and more sustainable future for generations to come.
Software solutions and Artificial Intelligence algorithms for energy sustainability and support for Renewable Energy Communities and Smart Grids
Background
In recent years, the rapid advancements in Artificial Intelligence (AI) and Machine Learning (ML) have paved the way for transformative applications across various sectors. One such area of significant importance is the field of energy management, specifically in Renewable Energy Communities (RECs). With the increasing adoption of renewable energy sources, the efficient utilization and optimization of these resources have become paramount.
Traditionally, energy management systems relied on conventional approaches that often struggled to fully harness the potential of renewable energy sources. The unpredictable nature of renewable energy generation, coupled with the varying energy demands of modern communities, necessitates intelligent systems that can adapt and optimize energy consumption in real-time.
This research project aims to leverage the power of AI and ML algorithms to tackle the complex challenges associated with energy management in RECs. By developing advanced models capable of learning from vast amounts of historical and real-time data, we seek to create intelligent systems that can optimize the utilization of renewable energy resources while minimizing waste and reducing overall carbon footprint.
The integration of AI and ML techniques in Energy Management Systems holds tremendous potential. By utilizing these cutting-edge technologies, we can analyze intricate patterns in energy generation, consumption, and storage. Moreover, these systems can intelligently forecast energy demand, allowing for proactive management and ensuring a stable and reliable power supply to the community.
Through this research, we endeavor to address several key objectives.
Firstly, we aim to develop robust Machine Learning algorithms that can accurately predict renewable energy generation and consumption patterns at various temporal scales. Secondly, we strive to create optimization models capable of dynamically balancing energy supply and demand, considering both the community's requirements and the available renewable energy resources. Lastly, we aim to design user-friendly interfaces that empower community members to actively engage in energy management, fostering a sense of ownership and sustainability.
Our vision is to approach Smart Grids as complex technological ecosystems or as Systems of Systems.
The outcomes of this research hold significant implications for the future of renewable energy communities. The successful integration of AI and ML algorithms into Energy Management Systems can lead to more efficient utilization of renewable resources, reduced dependence on non-renewable energy sources, and a tangible contribution towards mitigating climate change.
Piano Nazionale di Ripresa e Resilienza (PNRR) Projects
The project is affiliated with the following projects funded by the PNRR
Spoke 8 - PE2. Scenari energetici del futuro. PE0000021 "NEST - Network 4 Energy Sustainable Transition (NEST)" (Leader Sapienza University of Rome)
NEST - Network 4 Energy Sustainable Transition
Spoke 8 will deal with the electrification of final energy uses, energy efficiency, and demand reduction since these are crucial to the new green energy paradigm. The combination of novel market models, aggregation of local consumption (e.g., energy communities), coupling of diverse vectors and systems, flexibility, optimized energy management, and reduction of demand are necessary but are still at the early stage of research.
Spoke 8 aims at developing innovative governance and market models, high-efficient technologies and materials for energy use and storage, with a pervasive application of digitalization, artificial intelligence and communication techniques. The impact on energy system management, planning and design will be disruptive. Thus, the development of new algorithms, models and tools is also part of Spoke 8.
Spoke 8 considers sustainability and resilience in the energy supply chain, which is of utmost importance to modern societies highly dependent on continued access to energy services.
Spoke 8 also investigates on the fundamental rules of electromagnetic compatibility and reliability during the overall operational life of the innovative smart materials, components and systems under different stresses.
Spoke 13 - Electric Traction System and Batteries (ETSB)
Methods
Machine Learning methods for generation and load forecasting and its clasterization.
Computational Intelligence based optimization algorithm for the ETS environment which is very stochastic and highly dependent on exogenous variables.
Design, implementation and use of simulators.
Dynamic optimisation.
Outcome
Management strategies and simulators for hybrid charging stations with storage and RES.
Forecasting algorithm for EV charging station.
Optimization tools for the management of EVs, RES and BESS, and grid.
Impact
The impact of the project lies in the transformative effects it has on people's lives, ushering in a new era of sustainable energy management in communities. Here's a synthetic description of the impact:
The project's impact is multifaceted and far-reaching, with significant implications for individuals and communities alike. By leveraging the power of artificial intelligence and machine learning algorithms, the Energy Management Systems developed through this research revolutionize the way renewable energy is harnessed and optimized, leading to the following outcomes:
Enhanced Energy Efficiency: The project enables the efficient utilization of renewable energy resources, minimizing wastage and maximizing their potential. By accurately predicting energy demand and dynamically balancing it with the available supply, the Energy Management Systems optimize energy consumption patterns, leading to reduced energy bills and a more sustainable lifestyle for individuals within the community.
Reliable Power Supply: The integration of AI and ML techniques empowers the Energy Management Systems to intelligently forecast energy generation and consumption patterns. This proactive approach ensures a stable and reliable power supply to the community, minimizing disruptions and enhancing the overall quality of life for residents. Dependence on non-renewable energy sources is reduced, leading to a more resilient and sustainable energy infrastructure.
Empowerment and Engagement: The project aims to develop user-friendly interfaces that enable community members to actively engage in energy management. By providing real-time data, intuitive visualizations, and personalized insights, individuals gain a deeper understanding of their energy consumption habits and the impact on the environment. This knowledge empowers them to make informed decisions, adapt their behavior, and actively contribute to a more sustainable future.
Environmental Sustainability: The ultimate impact of the project lies in its significant contribution towards mitigating climate change and promoting environmental sustainability. By optimizing renewable energy utilization, the Energy Management Systems reduce greenhouse gas emissions and minimize the carbon footprint of the community. This has long-term benefits for the environment, fostering a cleaner, healthier, and more sustainable planet for future generations
Artificial Intelligence Impact on new Energy Systems - Our Idea
AI's impact on energy sustainability is poised to revolutionize the way we optimize energy systems and forecast energy production, consumption, and market prices. By leveraging the power of artificial intelligence (AI), we can unlock unprecedented opportunities for efficiency, reliability, and sustainability in the energy sector. Let's delve into the two key pillars where AI will make a substantial impact:
Optimization of Systems: AI-powered optimization techniques have the potential to transform the way energy systems operate, ensuring optimal utilization of resources and minimizing waste. Through advanced machine learning algorithms, energy management systems can analyze vast amounts of historical and real-time data, enabling real-time decision-making for energy distribution, storage, and consumption. These systems can adapt to changing energy demand patterns, integrate renewable energy sources seamlessly, and optimize energy generation and distribution to minimize costs and environmental impact.
AI algorithms can continuously learn from data, identifying complex patterns and optimizing system parameters to achieve energy efficiency. For example, in a renewable energy community, AI algorithms can intelligently balance energy supply and demand, dynamically allocating energy resources based on factors such as weather conditions, demand patterns, and available storage capacity. By doing so, AI optimizes the use of renewable energy sources, reducing reliance on non-renewable energy and minimizing greenhouse gas emissions.
Furthermore, AI can facilitate predictive maintenance of energy infrastructure. By analyzing sensor data and identifying anomalies or potential equipment failures, AI algorithms can trigger proactive maintenance, preventing costly breakdowns and maximizing the lifespan of assets. This optimization not only improves energy efficiency but also reduces maintenance costs, making renewable energy solutions more economically viable.
Forecasting of Energy Production, Consumption, and Market Prices: Accurate forecasting is vital for efficient energy management and market operations. AI-based forecasting models leverage historical data, weather patterns, and market trends to predict energy production, consumption, and market prices with greater precision. By capturing intricate relationships and complex dynamics, these models enable stakeholders to make informed decisions, optimize energy generation and distribution, and anticipate market fluctuations.
For energy production, AI algorithms can analyze weather data, historical energy generation patterns, and other relevant factors to predict renewable energy production levels. These forecasts allow grid operators to proactively manage the integration of fluctuating renewable sources into the grid, ensuring grid stability and reducing the need for backup power from non-renewable sources.
Similarly, AI-based consumption forecasting models utilize historical data, user behavior patterns, and external factors such as temperature and economic indicators to anticipate energy demand. This enables utilities to optimize energy distribution, schedule maintenance activities, and implement demand response programs effectively. By accurately predicting demand, utilities can prevent overloading the grid and avoid costly energy shortages or surpluses.
AI's impact on forecasting extends to energy market prices. By analyzing historical price data, market trends, and external factors like supply and demand dynamics, AI algorithms can generate highly accurate market price forecasts. These forecasts empower market participants, including renewable energy producers, grid operators, and consumers, to make informed decisions regarding energy trading, investment planning, and cost optimization.
The synergy between AI and energy sustainability is a game-changer. AI's ability to optimize energy systems and provide accurate forecasts enhances energy efficiency, grid stability, and cost-effectiveness. By promoting the integration of renewable energy sources and enabling smart energy management, AI contributes significantly to the transition to a sustainable energy future. With ongoing advancements in AI technology, the potential for further innovation and positive impact on energy sustainability is immense.
Brief Bibliography on Energy Management Systems
The EMS design is strictly linked to the Smart Grid (SG) or Microgrid (MG) architecture (depending on the scale). A wide range of configurations are possible, thanks to the several energy sources commonly adopted. In fact, besides Photovoltaic (PV) and wind energy, also biomass and geothermal are involved [1]. Thus, power generators are included in the architecture, together with Energy Storage Systems (ESSs) and, obviously, loads. Relatively simple architectures can be found in literature, such as smart homes represented as a group of household appliances (electrical loads) with a Point of Common Coupling (PCC) to the Main Grid, at most [2]. More complicated structures comprehend PV or wind generators with ESSs as green energy buffers, besides the loads [3][4]. As electrical loads, SGs or MGs can include both residential and commercial buildings [5], generally considered as aggregate loads, i.e. avoiding a focus on the single internal appliances, according to the EMS objectives. In some works, Combined Heat and Power (CHP) [6] and Electric Vehicles (EVs) [1] are part of the EMS architecture. Sometimes, simulation with deeper accuracy are carried on thanks to detailed PV and ESS models (e.g. the equivalent circuit ESS model) [4].
As aforementioned, the EMS architecture and design depend on each other, in the sense that an EMS synthesized on a given architecture has different objectives than and EMS based on a different one. In [2][6], which refer to a single smart home, a granular approach provides the shifting of the single household appliances for Demand Response (DR) purposes, i.e. aiming at an energy consumption concentrated on time intervals with lower Main Grid electricity prices. More in general, the EMS objective is to minimize electricity costs for consumers, that means a less reliance on the Main Grid and, as a consequence, more reliance on locally-produced green energy [7][8]. Nevertheless, many different objectives are pursued in literature, even if in the same direction. In [3] the operational costs of the generators, also involving start-up and shut-down times, are minimized. In [8], by a multi-objective approach, CO2 emissions are minimized while energy quality is maximized. Energy quality comes from the fact that consumers usually prefer to rely on generators with a low interruption probability, that is not the case of PV or wind generators. Also, energy quality is represented through the wating times to power the loads. In addition, selling excess energy is expected in some works [4].
The optimization task in an EMS can be based on different techniques. Following an exact approach, Mixed Integer Linear Programming (MILP) and Quadratic Programming (QP) optimizers are generally developed [1][5]. Nevertheless, heuristic and meta-heuristic algorithms are often used. Evolutionary Optimization (EO) is preeminent, since Genetic Algorithms (GAs) are very widespread [1][5], even though also Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) paradigms are relevant in literature [1][8]. Fuzzy Logic (FL) results to be a good paradigm for the problem at hand [1][3]. Generally, the parameters of a Fuzzy Inference System (FIS) are optimized by a meta-heuristic algorithm. Speaking of which, hybrid optimization designs are frequent: in [8], ACO runs together with a Teaching-based Optimization algorithm and, in [5], a GA is combined with a QP algorithm.
The EMS optimization procedure needs predicted data about weather (from which PV or wind power figures), energy prices and load consumption profiles [2] [7]. In fact, green energy generation is volatile and hardly predictable in its nature, also like the consumption figures that usually depend on the consumer habits. On a wider scale, consumption profiles concur to the Man Grid electricity prices setting (among other factors); thus, electricity prices have to be predicted with the highest possible accuracy. Neuralcomputing come to help for achieving a solution. For example, in [7], A Deep Learning LSTM-based prediction of the aforementioned figures in performed. Exact algorithms can be used as a benchmark for metaheuristic approaches. Optimization results are also compared with the same values achieved through similar optimizers in literature, as benchmark solutions [6][8].
To finish, in [9], a relatively new architecture called Energy Community is studied. It consists in a group of consumer that can auto-produce green energy and share it to each other with the aim of becoming the most independent as possible from the Main Grid. A clustering about consumption profiles is performed and a multi-objective optimization involves costs and energy quality.
[1] A. O. Ali, M. R. Elmarghany, M. M. Abdelsalam, M. N. Sabry, e A. M. Hamed, «Closed-loop home energy management system with renewable energy sources in a smart grid: A comprehensive review», Journal of Energy Storage, vol. 50, p. 104609, giu. 2022, doi: 10.1016/j.est.2022.104609.
[2] Y.-H. Lin, H.-S. Tang, T.-Y. Shen, e C.-H. Hsia, «A Smart Home Energy Management System Utilizing Neurocomputing-Based Time-Series Load Modeling and Forecasting Facilitated by Energy Decomposition for Smart Home Automation», IEEE Access, vol. 10, pp. 116747–116765, 2022, doi: 10.1109/ACCESS.2022.3219068.
[3] M. A. Mohamed, A. Almalaq, H. M. Abdullah, K. A. Alnowibet, A. F. Alrasheedi, e M. S. A. Zaindin, «A Distributed Stochastic Energy Management Framework Based-Fuzzy-PDMM for Smart Grids Considering Wind Park and Energy Storage Systems», IEEE Access, vol. 9, pp. 46674–46685, 2021, doi: 10.1109/ACCESS.2021.3067501.
[4] N. Uddin e Md. S. Islam, «Optimal Fuzzy Logic Based Smart Energy Management System For Real Time Application Integrating RES, Grid and Battery», in 2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT), set. 2018, pp. 296–301. doi: 10.1109/CEEICT.2018.8628057.
[5] H. Li, C. Zang, P. Zeng, H. Yu, e Z. Li, «A genetic algorithm-based hybrid optimization approach for microgrid energy management», in 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), giu. 2015, pp. 1474–1478. doi: 10.1109/CYBER.2015.7288162.
[6] A. U. Rehman et al., «An Optimal Power Usage Scheduling in Smart Grid Integrated With Renewable Energy Sources for Energy Management», IEEE Access, vol. 9, pp. 84619–84638, 2021, doi: 10.1109/ACCESS.2021.3087321.
[7] S. A. Nabavi, N. H. Motlagh, M. A. Zaidan, A. Aslani, e B. Zakeri, «Deep Learning in Energy Modeling: Application in Smart Buildings With Distributed Energy Generation», IEEE Access, vol. 9, pp. 125439–125461, 2021, doi: 10.1109/ACCESS.2021.3110960.
[8] S. Ali et al., «Demand Response Program for Efficient Demand-Side Management in Smart Grid Considering Renewable Energy Sources», IEEE Access, vol. 10, pp. 53832–53853, 2022, doi: 10.1109/ACCESS.2022.3174586.
[9] L. Xiong et al., «Multi-objective Energy Management Strategy for Multi-energy Communities Based on Optimal Consumer Clustering with Multi-Agent System», IEEE Transactions on Industrial Informatics, pp. 1–16, 2023, doi: 10.1109/TII.2023.3242812.