Optimising energy flows and synergies between energy networks

Abstract The increased use of fluctuating renewable energy sources (RES) that is expected in the near future will lead to challenges concerning their full integration in the distribution grid, the reduction of RES curtailments and the mitigation of electric unbalances on the grid. Besides electric batteries (EB), other technologies, such as Power-to-Gas (P2G), Power-to-Heat (P2H) and Combined Heat and Power (CHP), make it possible to exploit synergies between various energy networks, thus alleviating problems of RES integration. In fact, when these technologies work simultaneously in a single energy system, their installed power mix, along with their optimised management and control, play a fundamental role in the energy optimisation of the whole system. The aim of this paper is to offer a methodological approach for the analysis of the synergies between the different energy networks in order to cope with the increasing RES penetration. The proposed model has been used to perform a sensitivity analysis on the installed capacity of the various technologies; moreover, different simplified system management logics have been analysed by changing the priority order of the renewable energy surplus usage. The obtained results have been compared from an energetic, economic and environmental point of view

The PLANET Project: A Tool for Flexibility in the Energy Transition

Renewable energy resources offer immense prospects to mitigate greenhouse gas emissions and combat climate change, whilst addressing the growing energy demand. In recent years, owing to falling costs and supportive policies, the integration of renewable energy has expanded significantly. Nevertheless, challenges to its further expansion are raised due to the inherent variability of renewable energy production (‘vRES’) coupled with grid stability considerations, which – if not properly addressed – shall lead to vRES generation curtailment. The latter would cause renewable capacity expansion to decelerate, reductions in the capacity factors of vRES technologies and subsequent economic losses, to name a few. Against this backdrop, PLANET has developed a holistic decision support system for utilities, network operators and policy makers to help them implement optimal grid planning and management solutions compatible with complete decarbonization of the energy system. To that end, the project leverages energy conversion and storage technologies, such as Power-to-Gas, Power-to-Heat, Combined Heat and Power, Thermal storages and Virtual Energy Storage. These technologies have been deemed very promising to address issues related to the integration of renewables in the electricity grid, by enabling coordination of the electricity, heat and gas sectors towards revealing the maximum potential of network flexibility, a vital prerequisite for ensuring security of supply. The PLANET project commenced in November 2017 with the participation of 11 partners from 7 different countries: Italy, Finland, Greece, UK, Germany, France and Belgium including technical universities, research centers and associations, consultancy firms, utilities and information technology companie

Comparison of Genetic and Reinforcement Learning Algorithms for Energy Cogeneration Optimization

Large process plants generally require energy in different forms: mechanical, electrical, or thermal (in the form of steam or hot water). A commonly used source of energy is cogeneration, also defined as Combined Heat and Power (CHP). Cogeneration can offer substantial economic as well as energy savings; however, its real-time operation scheduling is still a challenge today. Multiple algorithms have been proposed for the CHP control problem in the literature, such as genetic algorithms (GAs), particle swarm optimization algorithms, artificial neural networks, fuzzy decision making systems and, most recently, reinforcement learning (RL) algorithms.This paper presents the comparison of a RL approach and a GA for the control of a cogenerator, using as a case study a thermal power plant serving a factory during the year 2021. The two methods were compared based on an earnings before interest, taxes, depreciation, and amortization (EBITDA) metric. The EBITDA that could be obtained using the RL algorithm, exceeds both the EBITDA that could be generated using a per-week genetic algorithm and the one from the manual scheduling of the CHP. Thus, the RL algorithm proves to be the most cost-effective strategy for the control of a CHP

NAWTEC16-1926 MSW INCINERATION CAPACITY EVALUATIONS FOR THE PROVINCE OF TURIN (NORTHERN ITALY)

ABSTRACT This paper assesses the incineration capacity requirement of the Province of Turin through a detailed analysis of the mass streams and the properties of residual Municipal Solid Waste (MSW). Historical data series were elaborated to study the trend evolution of household generation and separate collection. Residual MSW material compositions were calculated for each year over an observed period and for planned scenarios. A waste properties model was applied to calculate the residual MSW chemical composition and the LHV. The analysis allows conclusions to be drawn about the design of the planned waste-to-energy plant and to estimate the required size and technology to be used. The results show that the use of grate furnace combustor appears to be more suitable than fluidized bed

cigarette smoke inhibits the nlrp3 inflammasome and leads to caspase 1 activation via the tlr4 trif caspase 8 axis in human macrophages

n/

Co-simulation Management Algorithm for Distribution System Operation with Real-Time Simulator

This article presents a co-simulation framework consistent with the real-time simulation for operational analysis of electrical distribution networks. Realtime simulators have become a fundamental tool for testing and optimising control strategies in a safe and controlled environment. The proposed methodology outlines the steps required for setting up, controlling, and monitoring an electrical grid using a real-time simulator. The framework proposes the use of the Message Queuing Telemetry Transport communication between the electrical grid module and an external coordinator. An algorithm based on the Python programming language is proposed to manage the real-time simulation, create the grid topology, and communicate with the external coordinator. The implementation of the electrical network and the validation of the real-time simulator network are also presented. The article concludes that the proposed framework can improve the performance and flexibility of co-simulation for studies on the penetration of power electronics-based renewable sources

Delivery of Mycobacterium tuberculosis epitopes by Bordetella pertussis adenylate cyclase toxoid expands HLA-E-restricted cytotoxic CD8+ T cells

Introduction: Tuberculosis (TB) remains the first cause of death from infection caused by a bacterial pathogen. Chemotherapy does not eradicate Mycobacterium tuberculosis (Mtb) from human lungs, and the pathogen causes a latent tuberculosis infection that cannot be prevented by the currently available Bacille Calmette Guerin (BCG) vaccine, which is ineffective in the prevention of pulmonary TB in adults. HLA-E-restricted CD8+ T lymphocytes are essential players in protective immune responses against Mtb. Hence, expanding this population in vivo or ex vivo may be crucial for vaccination or immunotherapy against TB.Methods: The enzymatically inactive Bordetella pertussis adenylate cyclase (CyaA) toxoid is an effective tool for delivering peptide epitopes into the cytosol of antigen-presenting cells (APC) for presentation and stimulation of specific CD8+ T-cell responses. In this study, we have investigated the capacity of the CyaA toxoid to deliver Mtb epitopes known to bind HLA-E for the expansion of human CD8+ T cells in vitro.Results: Our results show that the CyaA-toxoid containing five HLA-E-restricted Mtb epitopes causes significant expansion of HLA-E-restricted antigen-specific CD8+ T cells, which produce IFN-gamma and exert significant cytotoxic activity towards peptide-pulsed macrophages.Discussion: HLA-E represents a promising platform for the development of new vaccines; our study indicates that the CyaA construct represents a suitable delivery system of the HLA-E-binding Mtb epitopes for ex vivo and in vitro expansion of HLA-E-restricted CD8+ T cells inducing a predominant Tc1 cytokine profile with a significant increase of IFN-gamma production, for prophylactic and immunotherapeutic applications against Mtb

Delivery of Mycobacterium tuberculosis epitopes by Bordetella pertussis adenylate cyclase toxoid expands HLA-E-restricted cytotoxic CD8+ T cells

IntroductionTuberculosis (TB) remains the first cause of death from infection caused by a bacterial pathogen. Chemotherapy does not eradicate Mycobacterium tuberculosis (Mtb) from human lungs, and the pathogen causes a latent tuberculosis infection that cannot be prevented by the currently available Bacille Calmette Guerin (BCG) vaccine, which is ineffective in the prevention of pulmonary TB in adults. HLA-E-restricted CD8+ T lymphocytes are essential players in protective immune responses against Mtb. Hence, expanding this population in vivo or ex vivo may be crucial for vaccination or immunotherapy against TB.MethodsThe enzymatically inactive Bordetella pertussis adenylate cyclase (CyaA) toxoid is an effective tool for delivering peptide epitopes into the cytosol of antigen-presenting cells (APC) for presentation and stimulation of specific CD8+ T-cell responses. In this study, we have investigated the capacity of the CyaA toxoid to deliver Mtb epitopes known to bind HLA-E for the expansion of human CD8+ T cells in vitro.ResultsOur results show that the CyaA-toxoid containing five HLA-E-restricted Mtb epitopes causes significant expansion of HLA-E-restricted antigen-specific CD8+ T cells, which produce IFN-γ and exert significant cytotoxic activity towards peptide-pulsed macrophages.DiscussionHLA-E represents a promising platform for the development of new vaccines; our study indicates that the CyaA construct represents a suitable delivery system of the HLA-E-binding Mtb epitopes for ex vivo and in vitro expansion of HLA-E-restricted CD8+ T cells inducing a predominant Tc1 cytokine profile with a significant increase of IFN-γ production, for prophylactic and immunotherapeutic applications against Mtb

An HLA-E-targeted TCR bispecific molecule redirects T cell immunity against Mycobacterium tuberculosis

Peptides presented by HLA - E, a molecule with very limited polymorphism, represent attractive targets for T cell receptor (TCR) - based immunotherapies to circumvent the limitations imposed by the high polymorphism of classical HLA genes in the human population. Here, we describe a TCR - based bispecific molecule that potently and selectively binds HLA - E in complex with a peptide encoded by the inhA gene of Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis in humans. We reveal the biophysical and structural bases underpinning the potency and specificity of this molecule and demonstrate its ability to redirect polyclonal T cells to target HLA - E - expressing cells transduced with mycobacterial inhA as well as primary cells infected with virulent Mtb. Additionally, we demonstrate elimination of Mtb - infected cells and reduction of intracellular Mtb growth. Our study suggests an approach to enhance host T cell immunity against Mtb and provides proof of principle for an innovative TCR - based therapeutic strategy overcoming HLA polymorphism and therefore applicable to a broader patient population