Distribution energy storage investment prioritization with a real coded multi-objective genetic algorithm

Energy Storage Systems (ESSs) are progressively becoming an essential requisite for the upcoming Smart Distribution Systems thanks to the flexibility they introduce in the network operation. A rapid improvement in ESS technology efficiency has been seen, but not yet sufficient to drastically reduce the high investments associated. Thus, optimal planning and management of these devices are crucial to identify specific configurations that can justify ESSs installation. This consideration has motivated a strong interest of the researchers in this field that, however, have separately solved the optimal ESS location and the optimal ESS schedule. In the paper, a novel multi-objective approach is presented, based on the Non-dominated Sorted Genetic Algorithm - II integrated with a real codification that allows joining in a single optimization all the main features of an optimal ESS implementation project: siting, sizing and scheduling. The methodology has been tested on a real-size rural distribution network

Relieving Tensions on Battery Energy Sources Utilization among TSO, DSO, and Service Providers with Multi-Objective Optimization

The European strategic long-term vision underlined the importance of a smarter and flexible system for achieving net-zero greenhouse gas emissions by 2050. Distributed energy resources (DERs) could provide the required flexibility products. Distribution system operators (DSOs) cooperating with TSO (transmission system operators) are committed to procuring these flexibility products through market-based procedures. Among all DERs, battery energy storage systems (BESS) are a promising technology since they can be potentially exploited for a broad range of purposes. However, since their cost is still high, their size and location should be optimized with a view of maximizing the revenues for their owners. Intending to provide an instrument for the assessment of flexibility products to be shared between DSO and TSO to ensure a safe and secure operation of the system, the paper proposes a planning methodology based on the non-dominated sorting genetic algorithm-II (NSGA-II). Contrasting objectives, as the maximization of the BESS owners’ revenue and the minimization of the DSO risk inherent in the use of the innovative solutions, can be considered by identifying trade-off solutions. The proposed model is validated by applying the methodology to a real Italian medium voltage (MV) distribution network

Uncertainty Reduction on Flexibility Services Provision from DER by Resorting to DSO Storage Devices

Current trends in electrification of the final energy consumption and towards a massive electricity production from renewables are leading a revolution in the electric distribution system. Indeed, the traditional “fit & forget” planning approach used by Distributors would entail a huge amount of network investment. Therefore, for making these trends economically sustainable, the concept of Smart Distribution Network has been proposed, based on active management of the system and the exploitation of flexibility services provided by Distributed Energy Resources. However, the uncertainties associated to this innovation are holding its acceptance by utilities. For increasing their confidence, new risk-based planning tools are necessary, able to estimate the residual risk connected with each choice and identify solutions that can gradually lead to a full Smart Distribution Network implementation. Battery energy storage systems, owned and operated by Distributors, represent one of these solutions, since they can support the use of local flexibility services by covering part of the associated uncertainties. The paper presents a robust approach for the optimal exploitation of these flexibility services with a simultaneous optimal allocation of storage devices. For each solution, the residual risk is estimated, making this tool ready for its integration within a risk-based planning procedure

Energy Blockchain for Public Energy Communities

This paper suggests an application of blockchain as an energy open data ledger, designed to save and track data regarding the energy footprint of public buildings and public energy communities. The developed platform permits writing energy production and consumption of public buildings using blockchain-enabled smart meters. Once authenticated on the blockchain, this data can be made available to the public domain for techno-economic analyses for either research studies and internal or third parties audits, increasing, in this way, the perceived transparency of the public institutions. A further feature of the platform, starting on the previously disclosed raw data, allows calculating, validating, and sharing sustainability indicators of public buildings and facilities, allowing the tracking of their improvements in sustainability goals. The paper also provides the preliminary results of a field-test experimentation of the proposed platform on a group of public buildings, highlighting the possible benefits of its widespread exploitation

Development of a tool to optimize economic and environmental feasibility of food waste chains

11 figures, 6 tables.-- Supplementary information available.The Sustainable Development Goal 12.3 focuses on food and its inedible parts that exit the supply chain and thus are lost or wasted. This work addresses the food waste problem by presenting the development of a tool to design business models to reduce the production of food waste. This has been developed within the LIFE16 project iRexfo, coordinated by the University of Perugia. The tool aims at transferring the results obtained in a pilot region (Umbria, Italy) to 4 other regions in Europe. It has been coded in Python and has a graphical user interface (GUI) to insert inputs and display outputs. The GUI has been developed in FLASK and it is hosted in the website of PythonAnywhere. A case study on the application of the software is also presented, mainly based on data retrieved in the Umbria region, Italy. Together with economic analysis, also, environmental assessment is performed.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. i-REXFO LIFE (LIFE16ENV/IT/000547) is a project funded by the EU under the LIFE 2016 program. This work has been partially funded by the GTCLC-NEG project that has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 101018756.Peer reviewe

Multicentre performance evaluation of the E170 Module for MODULAR ANALYTICS

The E170 module was evaluated at 13 sites in an international multicentre study. The objective of the study was to assess the analytical performance of 49 analytes, and to collect feedback on the system's reliability and practicability. The typical, within-run coefficients of variation (CVs) for most of the quantitative assays ranged between 1 and 2% while a range of 2-4% was achieved with the infectious disease methods. Total precision CVs were found to be within the manufacturer's expected performance ranges, demonstrating good concordance of the system's measuring channels and a high reproducibility during the 2-4-week trial period. The functional sensitivity of 11 selected assays met the clinical requirements (e.g., thyreotroponin (TSH) 0.008 mU/l, troponin T 0.02 µg/l, total prostate-specific antigen (PSA) 0.03 µg/l). The E170 showed no drift during an 8-hour period and no relevant reagent carryover. Accuracy was confirmed by ring trial experiments and method comparisons vs. Elecsys® 2010. The reliability and practicability of the system's hardware and software met with, or even exceeded, the evaluator's requirements. Workflow studies showed that E170 can cover the combined workload of various routine analysers in a variety of laboratory environment. Throughput and sample processing time requirements were achieved while personnel ‘hands-on-time' could be reduce

Chronic Obstructive Pulmonary Disease, inflammation and co-morbidity – a common inflammatory phenotype?

Chronic Obstructive Pulmonary Disease (COPD) is and will remain a major cause of morbidity and mortality worldwide. The severity of airflow obstruction is known to relate to overall health status and mortality. However, even allowing for common aetiological factors, a link has been identified between COPD and other systemic diseases such as cardiovascular disease, diabetes and osteoporosis. COPD is known to be an inflammatory condition and neutrophil elastase has long been considered a significant mediator of the disease. Pro-inflammatory cytokines, in particular TNF-α (Tumour Necrosis Factor alpha), may be the driving force behind the disease process. However, the roles of inflammation and these pro-inflammatory cytokines may extend beyond the lungs and play a part in the systemic effects of the disease and associated co-morbidities. This article describes the mechanisms involved and proposes a common inflammatory TNF-α phenotype that may, in part, account for the associations

Uncertainty Reduction on Flexibility Services Provision from DER by Resorting to DSO Storage Devices

Current trends in electrification of the final energy consumption and towards a massive electricity production from renewables are leading a revolution in the electric distribution system. Indeed, the traditional “fit & forget” planning approach used by Distributors would entail a huge amount of network investment. Therefore, for making these trends economically sustainable, the concept of Smart Distribution Network has been proposed, based on active management of the system and the exploitation of flexibility services provided by Distributed Energy Resources. However, the uncertainties associated to this innovation are holding its acceptance by utilities. For increasing their confidence, new risk-based planning tools are necessary, able to estimate the residual risk connected with each choice and identify solutions that can gradually lead to a full Smart Distribution Network implementation. Battery energy storage systems, owned and operated by Distributors, represent one of these solutions, since they can support the use of local flexibility services by covering part of the associated uncertainties. The paper presents a robust approach for the optimal exploitation of these flexibility services with a simultaneous optimal allocation of storage devices. For each solution, the residual risk is estimated, making this tool ready for its integration within a risk-based planning procedure