Recently updated on September 20th, 2024 at 10:04 am<\/p>","protected":false},"excerpt":{"rendered":"
A hybrid optimization system was developed by combining physical models with data-driven models. The optimizer was specifically designed for a district heating network in the Milan area and will be tested through experiments on the plant. Economic assessments have shown that using the optimization system leads to an average savings of 4-6% compared to the plant’s standard operation, and this savings can reach up to 30% with the optimal use of cogenerators for participation in the electricity market. <\/p>\n","protected":false},"author":93,"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"tags":[1602,1389,1411,1410,1321,1337,1299],"targets":[1317],"rapporti_tipologie":[762],"class_list":["post-192387","rapporti","type-rapporti","status-publish","hentry","tag-accumulation-transportation-and-utilization","tag-cogeneration-en","tag-district-heating","tag-electric-market","tag-emissions","tag-flexibility","tag-storage-en","targets-research","rapporti_tipologie-report-en"],"acf":{"projects":{"ID":191052,"post_author":"464","post_date":"2024-07-10 16:51:37","post_date_gmt":"2024-07-10 14:51:37","post_content":"","post_title":"Integration and coordination of the electric system with other systems (gas and water), analysis of needs, availability, performance and costs of storage systems","post_excerpt":"Improve the sustainability of the energy system through a better understanding of the availability of Renewable Energy Sources (RES) and the use of an integrated approach that can exploit synergies between different sectors.","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"integration-and-coordination-of-the-electric-system-with-other-systems-gas-and-water-analysis-of-needs-availability-performance-and-costs-of-storage-systems","to_ping":"","pinged":"","post_modified":"2024-08-09 16:00:36","post_modified_gmt":"2024-08-09 14:00:36","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.rse-web.it\/progetti\/integration-and-coordination-of-the-electric-system-with-other-systems-gas-and-water-analysis-of-needs-availability-performance-and-costs-of-storage-systems\/","menu_order":0,"post_type":"progetti","post_mime_type":"","comment_count":"0","filter":"raw"},"order_posts":"","dont_show_search":false,"related_posts":false,"dont_show_hompage":false,"show_on_slider":false,"single_post_data":{"titolo_spot":"","post_content":"
The evolution of district heating systems (DHC) toward more efficient and low-emission models will involve the gradual integration of both dispatchable and non-dispatchable renewable energy sources, combined heat and power systems (CHP), geothermal heat pumps, and operational improvements through reduced operating temperatures. Managing such a system requires optimization software with the following characteristics: i) optimality, ii) predictability, iii) flexibility, and iv) responsiveness and robustness.<\/p>\n
The software developed and described in this report aims to enhance the efficiency of a district heating plant by adjusting the supply temperature based on load demands and defining the operation of generators to participate in the electricity market (Day-Ahead Market – MGP), and, where possible, providing flexibility for the Dispatching Services Market (MSD), resulting in energy savings, reduced CO2<\/sub> emissions, and economic benefits. The optimization system will determine the status and set points of the involved generators and adjust the supply temperature according to the forecasted load demands.<\/p>\n The optimization system is based on models of the plant components as well as equations describing the network dynamics. A hybrid approach was chosen for this problem, featuring an innovative control scheme that combines Model Predictive Control (MPC) techniques with Machine Learning (ML) approaches. The latter are used to model the district heating network (using autoregressive models) and to predict thermal loads (using neural networks). The developed optimization system is classified as a MILP (Mixed Integer Linear Programming) system.<\/p>\n Economic evaluations obtained through simulations have shown that using the optimization system, even with stringent contractual constraints on supply temperatures, results in an average operational cost savings of 4-6% compared to normal plant operation, thanks to reduced losses and therefore lower thermal power output from the plant. Additionally, this economic saving can reach up to 30% through the appropriate use of CHP systems, which allow for the sale of electricity to the grid (MGP) while simultaneously producing heat to meet thermal demand.<\/p>\n The optimizer was developed for a real case of a district heating network in the Milan area, and its actual performance will be tested through plant trials during 2021.<\/p>\n","link_estreno":false,"scarica_file":[{"download_option":"download","file_name":"Download Report","download":{"ID":166714,"id":166714,"title":"20010757","filename":"20010757.pdf","filesize":2745784,"url":"https:\/\/www.rse-web.it\/wp-content\/uploads\/2022\/05\/20010757.pdf","link":"https:\/\/www.rse-web.it\/en\/rapporti\/sviluppo-e-test-in-ambiente-simulato-di-un-software-per-la-gestione-ottimizzata-innovativa-di-una-centrale-di-teleriscaldamento\/attachment\/20010757\/","alt":"","author":"93","description":"","caption":"","name":"20010757","status":"inherit","uploaded_to":166713,"date":"2022-05-13 13:26:10","modified":"2022-05-13 13:26:10","menu_order":0,"mime_type":"application\/pdf","type":"application","subtype":"pdf","icon":"https:\/\/www.rse-web.it\/wp-includes\/images\/media\/document.png"}}],"button":{"text":"","link":""},"referente_group":false,"data_emissione":"2020-12-30","autori":"A. Del Corno, A. La Bella (RSE S.P.A.)","rapporto":"","rif_rse":"20010757"},"satellite_post_url":""},"yoast_head":"\n