[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84120-en":3,"doc-seo-84120-105":28,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":13,"seo_description":14,"update_tm":26,"read_time":27},84120,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Multi-Agent Deep Reinforcement Learning for Multi Objective Battery Management in Dairy Farms","The dairy industry in Ireland has strong potential to integrate renewable energy while lowering carbon emissions, yet distributed-generation control research has largely emphasized residential and commercial settings. The presented work proposes a two-layer multi-objective optimization control system combining differential evolution with multi-agent deep reinforcement learning for battery management. Upper-layer dynamic pricing and lower-layer multi-agent DRL regulate grid interaction and battery operation. Simulations on a rural distribution circuit show improved energy-arbitrage profits up to 18%, greater distributed-generation utilization, stable cost, and compliance with Irish grid voltage-variation requirements.","arXiv :2607 .06489v 1 [ cs .AI ] 7 Jul 2026  \nMulti-Agent Deep Reinforcement Learning for Multi Objective Battery Management in Dairy  \nFarms  \nMarcos Eduardo Cruz Victorio [0000−0003−2604−176X] and Karl Mason [0000−0002−8966−9100]  \nUniversity of Galway, Ireland  \n[marcos.cruzvictorio@universityofgalway.ie](marcos.cruzvictorio@universityofgalway.ie)  \nAbstract. The dairy industry in Ireland has a large potential for the integration of renewable energy and the reduction of carbon emissions.  \nHowever, researchers of distributed generation control are mainly focused on residential and commercial applications. To contribute to the effective integration of renewable energy in the dairy sector, this paper presents a multi-objective optimisation control system based on differential evolution and multi agent Deep Reinforcement Learning. The proposed control is organised in two layers: the upper layer uses dynamic pricing, and the lower layer is based on multi-agent reinforcement learning for battery management. This paper also simulates the electrical response of the proposed control system in a rural distribution circuit. The simulation results show that the proposed control framework can improve profits from energy arbitrage up to 18% compared to using Rule-based models, increase the use of distributed generation without significantly increasing cost, and comply with the Irish grid code in terms of voltage variation.  \nKeywords: Multi-Objective Optimisation · Deep Reinforcement  \nLearning · Distributed Generation.  \n1 Introduction  \nCurrently, there is a large focus on research to deliver the energy transition to combat climate change. In the case of electricity, research on the energy transition focuses on the integration of Distributed Energy Resources (DER)s. In this context, there is a large potential in the agricultural sector for the integration of DERs, due to its energy intensive nature and limited use of smart grid technologies.  \nIn an electrical grid, there are multiple, often competing, objectives that need to be balanced in the smart grid [1], such as grid compliance, minimisation of cost and minimisation of carbon emissions. Although previous approaches have been proposed for grid multi objective energy optimisation (MOO) [2, 3], their performance is limited by the precision of their models. In this context, Deep Reinforcement Learning (DRL) methods can be used for energy optimisation in conditions with limited information of the system dynamics. The applications  \n2 M. E. Cruz Victorio et al.  \nof DRL in smart grids include maximising the use of renewable energy [4] and minimising costs in electric vehicles [5] . Among DRL methods, Proximal Policy Optimisation (PPO) emerges as an ideal method for energy management [6] .  \nMMO applications in smart grid have been presented with some limiations [7, 8] . For example, in [9] MOO is applied to reduce line congestion and costs, however, this approach did not account for multiple generators and storages in the grid. In [10], a MOO framework is proposed for cost minimisation and voltage regulation, however, this approach is limited to the retail electricity market.  \nTo further develop smart grid applications in the rural sector, our proposed control framework combines heuristic optimisation with DRL agents to maximise the use of distributed generation and affordability in dairy farms, accounting for uncertain electricity prices and multiple generation and storage sources. The following section describes the proposed energy management framework.  \n2 Methodology  \nThe proposed control system is divided into two layers. The lower layer performs local optimisation in the distribution system using a multi-agent system, where each agent controls a single battery based on the price of electricity provided by the upper layer. The upper layer adjusts the internal price of electricity to regulate the interaction with the main grid. These control layers form a distributed control [11","cbCaitMqr7L0QEtW","https://ap.wps.com/l/cbCaitMqr7L0QEtW","pdf",653342,1,"English","en",105,"# Introduction\n# Methodology\n## Multi-Objective Layer","[{\"question\":\"What two-layer control structure does the paper propose for dairy-farm battery management?\",\"answer\":\"The upper layer performs dynamic pricing using a multi-objective optimization solved via differential evolution, while the lower layer uses multi-agent deep reinforcement learning where each agent controls a single battery.\"},{\"question\":\"How does the multi-objective layer balance system cost and grid interaction?\",\"answer\":\"It defines two objectives: minimizing total system cost and minimizing power flow with the main grid, with weights that combine both objectives at each time step and constraints on the weighting parameters.\"},{\"question\":\"What improvements do the simulation results report compared with rule-based models?\",\"answer\":\"The framework increases energy-arbitrage profits by up to 18%, improves distributed-generation usage without significantly increasing cost, and meets Irish grid code requirements regarding voltage 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two-layer control structure does the paper propose for dairy-farm battery management?","Question",{"text":74,"@type":75},"The upper layer performs dynamic pricing using a multi-objective optimization solved via differential evolution, while the lower layer uses multi-agent deep reinforcement learning where each agent controls a single battery.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the multi-objective layer balance system cost and grid interaction?",{"text":79,"@type":75},"It defines two objectives: minimizing total system cost and minimizing power flow with the main grid, with weights that combine both objectives at each time step and constraints on the weighting parameters.",{"name":81,"@type":72,"acceptedAnswer":82},"What improvements do the simulation results report compared with rule-based models?",{"text":83,"@type":75},"The framework increases energy-arbitrage profits by up to 18%, improves distributed-generation usage without significantly increasing 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