Self-Forecasting Energy Load Stakeholders for Smart Grids

The unpredictability of energy loads is responsible for a significant portion of efficiency loss in power grids. In order to reduce load uncertainties, emerging Smart Grid business models call for the active participation of traditionally passive stakeholders. The contribution of this work enables self-forecasting energy load stakeholders whose deterministic load behaviour make them reliable resources that can greatly benefit themselves and other Smart Grid stakeholders

Chapter The EU Research Project PLANET

Renewable energy sources offer unprecedented opportunities to reduce greenhouse gas emissions. But some challenges remain to be solved before their full benefits can be reaped. The main one relates to the intermittency of their electricity supply which can lead to grid problems such as congestion and imbalance between generation and demand. Energy conversion and storage has been touted as a very promising solution to all aforementioned issues. PLANET will develop a holistic decision support system for the optimal orchestration of the different energy networks for aggregators and balance responsible parties, policy makers and network operators. It will aid them to leverage innovative energy conversion in alternative carriers and storage technologies in order to explore, identify, evaluate and quantitatively assess optimal grid planning and management strategies for future energy scenarios targetting full energy system decarbonization. Moreover, an analysis of the possible synergies between electricity, gas and heat networks will be carried out by creating simulation models for the integration between energy networks and conversion/storage technologies, for example power-to-gas, power-to-heat and virtual thermal energy storage. Application of the developed tools in two different test cases in Italy and France will showcase their benefits and reveal potential grid stability issues and effective countermeasures

The EU Research Project PLANET

Renewable energy sources offer unprecedented opportunities to reduce greenhouse gas emissions. But some challenges remain to be solved before their full benefits can be reaped. The main one relates to the intermittency of their electricity supply which can lead to grid problems such as congestion and imbalance between generation and demand. Energy conversion and storage has been touted as a very promising solution to all aforementioned issues. PLANET will develop a holistic decision support system for the optimal orchestration of the different energy networks for aggregators and balance responsible parties, policy makers and network operators. It will aid them to leverage innovative energy conversion in alternative carriers and storage technologies in order to explore, identify, evaluate and quantitatively assess optimal grid planning and management strategies for future energy scenarios targetting full energy system decarbonization. Moreover, an analysis of the possible synergies between electricity, gas and heat networks will be carried out by creating simulation models for the integration between energy networks and conversion/storage technologies, for example power-to-gas, power-to-heat and virtual thermal energy storage. Application of the developed tools in two different test cases in Italy and France will showcase their benefits and reveal potential grid stability issues and effective countermeasures

A review of prosumers’ behaviours in smart grids and importance of smart grid management

Purpose: The concept of the smart grid is relatively new. The first aim of the study is to understand the behaviour of prosumers in smart grids. The other goal is to raise awareness of the management tasks and risks of smart grids by highlighting the relevant issues of some business networks (PPP projects, outsourcing, strategic alliances etc.). Methodology: Systemized literature review was used in the paper. Results: The discussed management problems of various business networks indicate that management challenges can also be expected in smart grids, so it is worth preparing in time. Conclusion: We found a lack of empirical research about the behaviour of prosumers and believe that studying the electric power grid of the future from a management perspective, that is, examining the possible behaviours and decisions of various actors, can provide valuable and useful information for smart grid design and safe operation insurance

Smart Sustainable Mobility: Analytics and Algorithms for Next-Generation Mobility Systems

To this date, mobility ecosystems around the world operate on an uncoordinated, inefficient and unsustainable basis. Yet, many technology-enabled solutions that have the potential to remedy these societal negatives are already at our disposal or just around the corner. Innovations in vehicle technology, IoT devices, mobile connectivity and AI-powered information systems are expected to bring about a mobility system that is connected, autonomous, shared and electric (CASE). In order to fully leverage the sustainability opportunities afforded by CASE, system-level coordination and management approaches are needed. This Thesis sets out an agenda for Information Systems research to shape the future of CASE mobility through data, analytics and algorithms (Chapter 1). Drawing on causal inference, (spatial) machine learning, mathematical programming and reinforcement learning, three concrete contributions toward this agenda are developed. Chapter 2 demonstrates the potential of pervasive and inexpensive sensor technology for policy analysis. Connected sensing devices have significantly reduced the cost and complexity of acquiring high-resolution, high-frequency data in the physical world. This affords researchers the opportunity to track temporal and spatial patterns of offline phenomena. Drawing on a case from the bikesharing sector, we demonstrate how geo-tagged IoT data streams can be used for tracing out highly localized causal effects of large-scale mobility policy interventions while offering actionable insights for policy makers and practitioners. Chapter 3 sets out a solution approach to a novel decision problem faced by operators of shared mobility fleets: allocating vehicle inventory optimally across a network when competition is present. The proposed three-stage model combines real-time data analytics, machine learning and mixed integer non-linear programming into an integrated framework. It provides operational decision support for fleet managers in contested shared mobility markets by generating optimal vehicle re-positioning schedules in real time. Chapter 4 proposes a method for leveraging data-driven digital twin (DT) frameworks for large multi-stage stochastic design problems. Such problem classes are notoriously difficult to solve with traditional stochastic optimization. Drawing on the case of Electric Vehicle Charging Hubs (EVCHs), we show how high-fidelity, data-driven DT simulation environments fused with reinforcement learning (DT-RL) can achieve (close-to) arbitrary scalability and high modeling flexibility. In benchmark experiments we demonstrate that DT-RL-derived designs result in superior cost and service-level performance under real-world operating conditions

Scalable method for administration of resource technologies under stochastic procedures

During the development of the S3Unica project (Smart Specialisation University Campus) and its application in the ASSET project (Advanced Systems Studies for Energy Transition), both within the European Commission, the resolution of the distributed energy generation model was proposed through the creation of an algorithm that would allow the shared market between producers and consumers. From this premise arose the need to create a replicable system to resolve this situation in the new shared generation environment, using low-cost technologies. This work develops the scalable method for resource management technologies (SMART), based on stochastic procedures, which generates microgrids with an integrated energy market. The interest of this work is based on the incorporation of real-time analysis, applying stochastic methods, and its fusion with probabilistic predictive methods that evolve and harmonise the results. The fact that the process is self-learning also enables the use of metadomotic as a tool for both comfort improvement and energy sharing. The most important results developed were the design of the internal scheme of the low-cost SMART control device together with the developments of both individual and collective resolution algorithms. By achieving the incorporation of internal and external producers in the same numerical procedure, the distributed and hybrid generation models are solved simultaneously.We thank the support of this paper from University of Malaga and CBUA (funding for open access charge: Universidad de Málaga / CBUA) and we thank also the anonymous reviewers whose suggestions helped improve and clarify this manuscript. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors

Multi-domain maturity model for AI and analytic capability in power generation sector: A case study of ABB PAEN Oy

As more smart devices and smart meters are available on the market, industry actors offer AI and analytic suites and platforms where the data streams can be contextualized and leveraged in pre-made industry specific templates and model, together with self-serving machine learning environments. How can a traditional EPC company, use its domain knowledge in offering these AI and analytic suites. The assumption made is that there is no inherent value in the AI and analytics suite without data. How should this assumption be incorporated in projects executed before the operation phase where data from operation is non-existent.This thesis investigate which elements provide a value proposition in the AI and analytic suite and map this against the domain knowledge of the EPC company. The findings is a novel design in where both operational data is integrated into design for new projects. A survey is also conducted on the data utilization in the power generation sector based on the same elements. The findings is that while the granularity is low, the quality is good, with an overall maturity between managed and proactive data utilization, which indicate that there are few automated data streams, but that the data is available structurally and in a defined way

Smart electric vehicle charging strategy in direct current microgrid

This thesis proposes novel electric vehicle (EV) charging strategies in DC microgrid (DCMG) for integrating network loads, EV charging/discharging and dispatchable generators (DGs) using droop control within DCMG. A novel two-stage optimization framework is deployed, which optimizes power flow in the network using droop control within DCMG and solves charging tasks with a modified Djistra algorithm. Charging tasks here are modeled as the shortest path problem considering system losses and battery degradation from the distribution system operator (DSO) and electric vehicles aggregator (EVA) respectively. Furthermore, a probabilistic distribution model is proposed to investigate the EV stochastic behaviours for a charging station including time-of-arrival (TOA), time-of-departure(TOD) and energy-to-be-charged (ETC) as well as the coupling characteristic between these parameters. Markov Chain Monte Carlo (MCMC) method is employed to establish a multi-dimension probability distribution for those load profiles and further tests show the scheme is suitable for decentralized computing of its low burn-in request, fast convergent and good parallel acceleration performance. Following this, a three-stage stochastic EV charging strategy is designed to plug the probabilistic distribution model into the optimization framework, which becomes the first stage of the framework. Subsequently, an optimal power flow (OPF) model in the DCMG is deployed where the previous deterministic model is deployed in the second stage which stage one and stage two are combined as a chance-constrained problem in stage three and solved as a random walk problem. Finally, this thesis investigates the value of EV integration in the DCMG. The results obtained show that with smart control of EV charging/discharging, not only EV charging requests can be satisfied, but also network performance like peak valley difference can be improved by ancillary services. Meanwhile, both system loss and battery degradation from DSO and EVA can be minimized.Open Acces