{"id":69996,"date":"2025-11-24T10:40:19","date_gmt":"2025-11-24T15:40:19","guid":{"rendered":"https:\/\/blogs.sw.siemens.com\/simcenter\/?p=69996"},"modified":"2026-03-26T06:50:37","modified_gmt":"2026-03-26T10:50:37","slug":"smarter-better-faster-how-to-benefit-from-ai-and-reinforcement-learning-to-augment-your-battery-energy-storage-system-bess","status":"publish","type":"post","link":"https:\/\/blogs.sw.siemens.com\/simcenter\/smarter-better-faster-how-to-benefit-from-ai-and-reinforcement-learning-to-augment-your-battery-energy-storage-system-bess\/","title":{"rendered":"Smarter, better, faster: How to benefit from AI and Reinforcement Learning to augment your Battery Energy Storage System (BESS)"},"content":{"rendered":"\n<p>As the global energy landscape shifts toward renewables, the need for <strong>intelligent energy management systems<\/strong> becomes increasingly critical. One of the most promising innovations in this space is the integration of \u201cBattery Energy Storage Systems\u201d (BESS) with <strong>AI-driven smart controllers<\/strong>, particularly those trained using \u201cReinforcement Learning\u201d (RL). So that you can optimize your Battery Energy Storage Systems with AI-powered smart controllers and see directly the benefits using such approaches practically.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"600\" height=\"300\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/Wind_turbine_with_smartphone.png\" alt=\"\" class=\"wp-image-69997\"\/><figcaption class=\"wp-element-caption\">The Energy industry needs BESS to save money and to reduce carbon emissions<\/figcaption><\/figure><\/div>\n\n\n<p>Let\u2019s discover together the 3 steps needed to convert your digital twin from a BESS plant model into an augmented system with an optimal controller automatically generated, with no user interaction nor brainstorming and no coding. The best controller comes directly out-of-the-box, it\u2019s a matter of a few hours combining two SIEMENS softwares: 1.- Simcenter Amesim for the digital twin of the physical system, and 2.- Simcenter Studio for the Reinforcement Learning. That\u2019s it, that\u2019s the way it is !<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"383\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/AI_RL_workflow_4_steps-1024x383.png\" alt=\"\" class=\"wp-image-69998\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/AI_RL_workflow_4_steps-1024x383.png 1024w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/AI_RL_workflow_4_steps-600x224.png 600w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/AI_RL_workflow_4_steps-768x287.png 768w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/AI_RL_workflow_4_steps-900x336.png 900w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/AI_RL_workflow_4_steps.png 1217w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">3 steps to get your BESS digital twin augmented thanks to AI \/ Reinforcement Learning<\/figcaption><\/figure><\/div>\n\n\n<h2 class=\"wp-block-heading\"><a><strong>Challenges of AI-based solutions for smart BESS<\/strong><\/a><\/h2>\n\n\n\n<p>There are a couple of challenges to be tackled when implementing the BESS onsite. Typically, we can find these usual challenges for balancing the supply, demand, and price:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The wind energy, while clean and abundant, is inherently variable. Its generation depends heavily on weather conditions -especially the wind speed- which fluctuate unpredictably. This variability poses a challenge for grid operators and energy providers who must balance supply and demand while maintaining grid stability and minimizing costs.<\/li>\n\n\n\n<li>The wind energy is slightly more unstable than solar energy in the sense that for a specific location, the sun always appears at sunrise and goes down at sunset. Then it\u2019s a matter of variations in weather conditions: sky can be cloudy, we can get a higher turbidity factor when particles are more present, \u2026 Still the windy conditions are usually much more variable over the day, the seasons and the whole year.<\/li>\n<\/ul>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"600\" height=\"300\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/Siemens_Wind_Turbine_Simulation_and_Sustainability.png\" alt=\"\" class=\"wp-image-69999\"\/><figcaption class=\"wp-element-caption\">System Simulation helps engineers in Energy save money and reduce carbon emissions<\/figcaption><\/figure><\/div>\n\n\n<p>So it makes sense to consider this renewable source of energy to investigate how much benefit we can get from Reinforcement Learning applied to BESS. Indeed, the Battery Energy Storage Systems (BESS) offers a solution by storing excess energy during periods of low demand and releasing it when demand and prices peak. However, determining the optimal times to charge and discharge these batteries is a complex task influenced by:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Real-time electricity prices<\/li>\n\n\n\n<li>Grid load conditions<\/li>\n\n\n\n<li>Weather forecasts<\/li>\n\n\n\n<li>Historical wind generation patterns<\/li>\n<\/ul>\n\n\n\n<p>To tackle this complexity, researchers and engineers are turning to Reinforcement Learning, a branch of AI where agents learn optimal strategies through trial and error within simulated environments. In this case, the Reinforcement Learning agent is trained in two years of historical weather data, learning to predict wind energy availability and make informed decisions about when to store or release energy.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"754\" height=\"312\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/RL_Training_Accumulated_rewards_during_episodes.png\" alt=\"\" class=\"wp-image-70000\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/RL_Training_Accumulated_rewards_during_episodes.png 754w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/RL_Training_Accumulated_rewards_during_episodes-600x248.png 600w\" sizes=\"auto, (max-width: 754px) 100vw, 754px\" \/><figcaption class=\"wp-element-caption\">Typical reward principle during episodes with trial and error to reach the highest reward<\/figcaption><\/figure><\/div>\n\n\n<p>The Reinforcement Learning (RL)-powered smart controller continuously monitors:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Wind speed forecasts<\/strong> to estimate upcoming energy generation<\/li>\n\n\n\n<li><strong>Grid load levels<\/strong> to identify periods of high or low demand<\/li>\n\n\n\n<li><strong>Electricity market prices<\/strong> to maximize economic returns<\/li>\n<\/ul>\n\n\n\n<p>By learning from past patterns and adapting to real-time conditions, the controller can:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Charge batteries<\/strong> when wind energy is abundant and prices are low<\/li>\n\n\n\n<li><strong>Discharge batteries<\/strong> when grid demand spikes and prices are high<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><a><strong>What is Reinforcement Learning (RL)?<\/strong><\/a><\/h2>\n\n\n\n<p>Don\u2019t be afraid, Reinforcement Learning (RL) is not as complex as it looks like. It\u2019s just an interdisciplinary area of machine learning and optimal control concerned how intelligent agents should take actions in a dynamic environment to maximize a reward signal.<\/p>\n\n\n\n<p>This approach translated into the context of System Simulation brings these 3 steps. Let\u2019s consider first the simple illustration below, easy to understand. It is a domestic HVAC system with the following key characteristics:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>User goal:<\/strong> keep a comfortable temperature in the house<\/li>\n\n\n\n<li><strong>Control inputs:<\/strong> ventilator speed, HVAC power request<\/li>\n\n\n\n<li><strong>User inputs:<\/strong> temperature setpoint<\/li>\n\n\n\n<li><strong>Information feedback:<\/strong> room temperature<\/li>\n<\/ul>\n\n\n\n<p>First, you need to provide the \u201cenvironment\u201d. It\u2019s the system to be controlled (i.e. everything outside of the agent). It can be <strong>controlled <\/strong>through <em>actions. <\/em>And it can be <strong>observed<\/strong> through <em>observations.<\/em><\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1020\" height=\"270\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/RL_Agent_workflow_Step1_Environment.png\" alt=\"\" class=\"wp-image-70001\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/RL_Agent_workflow_Step1_Environment.png 1020w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/RL_Agent_workflow_Step1_Environment-600x159.png 600w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/RL_Agent_workflow_Step1_Environment-768x203.png 768w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/RL_Agent_workflow_Step1_Environment-900x238.png 900w\" sizes=\"auto, (max-width: 1020px) 100vw, 1020px\" \/><figcaption class=\"wp-element-caption\">Definition of the \u201cenvironment\u201d outside the agent<\/figcaption><\/figure><\/div>\n\n\n<p>Then there\u2019s the agent. It\u2019s the part of the controller that will decide the <em>actions <\/em>to control the environment the best as possible. The training is done in <strong>episodes<\/strong>. Training an agent based on multiple scenarios is important for the convergence and generality of the agent.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1020\" height=\"270\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/RL_Agent_workflow_Step2_Training.png\" alt=\"\" class=\"wp-image-70002\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/RL_Agent_workflow_Step2_Training.png 1020w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/RL_Agent_workflow_Step2_Training-600x159.png 600w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/RL_Agent_workflow_Step2_Training-768x203.png 768w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/RL_Agent_workflow_Step2_Training-900x238.png 900w\" sizes=\"auto, (max-width: 1020px) 100vw, 1020px\" \/><figcaption class=\"wp-element-caption\">Definition of the agent de be trained during episodes<\/figcaption><\/figure><\/div>\n\n\n<p>The goal of the agent is to maximize the accumulated reward over all steps. The <em>agent <\/em>learns a policy by trial and error which <em>actions<\/em> the actor should apply for which <em>observation <\/em>of the environment, by observing the reward of the environment.<\/p>\n\n\n\n<p>Once ready, the Reinforcement Learning can go for <strong>deployment. <\/strong>It\u2019s the use of the policy trained by the agent as a controller by exporting it to a FMU (Functional Mockup Unit). It does not need to be executed within the same environment. E.g. the training can occur on a low fidelity model to be used in a higher fidelity system.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1020\" height=\"270\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/RL_Agent_workflow_Step3_Export.png\" alt=\"\" class=\"wp-image-70003\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/RL_Agent_workflow_Step3_Export.png 1020w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/RL_Agent_workflow_Step3_Export-600x159.png 600w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/RL_Agent_workflow_Step3_Export-768x203.png 768w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/RL_Agent_workflow_Step3_Export-900x238.png 900w\" sizes=\"auto, (max-width: 1020px) 100vw, 1020px\" \/><figcaption class=\"wp-element-caption\">Export of the controller (trained agent with Reinforcement Learning) with FMI (Functional Mockup Interface)<\/figcaption><\/figure><\/div>\n\n\n<p>Let\u2019s see how to implement this Reinforcement Learning approach practically for our BESS use case.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><a><strong>System simulation is a perfect enabler<\/strong><\/a><\/h2>\n\n\n\n<p>The good thing with System Simulation is its ability to represent a complete system with a selection of key components. Thanks to its abstraction level, it\u2019s easy to get a whole system and to execute it in a few seconds or a couple of minutes, no more. It\u2019s a great advantage in the case of training where multiple scenarios have to be investigated. You don\u2019t need weeks or months of computation, everything can be launched and delivered within the same day so that your optimal controller is ready quickly.<\/p>\n\n\n\n<p>Let\u2019s consider our BESS system with its input subsystems and output loads.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"446\" height=\"289\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/Energy_BESS_Wind_turbines_Reinforcement_Learning_Grid-1.png\" alt=\"\" class=\"wp-image-70005\"\/><figcaption class=\"wp-element-caption\">BESS with inputs and outputs, augmented thanks to Reinforcement Learning<\/figcaption><\/figure><\/div>\n\n\n<p>Basically, the diagram overview includes:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Wind Turbines<\/strong>: Generate renewable energy based on weather conditions, especially wind speed.<\/li>\n\n\n\n<li><strong>Weather Data Input<\/strong>: Historical and real-time weather data feed into the Reinforcement Learning agent.<\/li>\n\n\n\n<li><strong>Reinforcement Learning Agent<\/strong>: Trained in 2 years of weather data, it predicts wind energy availability and optimizes battery control decisions.<\/li>\n\n\n\n<li><strong>Smart Controller<\/strong>: Executes the Reinforcement Learning agent\u2019s decisions to charge or discharge the battery.<\/li>\n\n\n\n<li><strong>Battery Energy Storage System (BESS)<\/strong>: Stores energy when prices are low and releases it when prices are high.<\/li>\n\n\n\n<li><strong>Grid Interface<\/strong>: Connects the BESS to the power grid, responding to load and price signals.<\/li>\n<\/ul>\n\n\n\n<p>This schematic representation is directly reflected in the Simcenter Amesim sketch where anyone can recognize the same layout. The main difference is the use of a signal bus (see the red bold line) to transport multiple signals within one single transmission line.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"685\" height=\"355\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/Simcenter_Amesim_BESS_smart_RL_controiller_sketch.png\" alt=\"\" class=\"wp-image-70006\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/Simcenter_Amesim_BESS_smart_RL_controiller_sketch.png 685w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/Simcenter_Amesim_BESS_smart_RL_controiller_sketch-600x311.png 600w\" sizes=\"auto, (max-width: 685px) 100vw, 685px\" \/><figcaption class=\"wp-element-caption\">BESS with smart control : complete system in Simcenter Amesim<\/figcaption><\/figure><\/div>\n\n\n<p>The digital twin is now ready. Let\u2019s go the training and reward steps to get an optimal smart controller in a couple of clicks.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><a><strong>Few steps to go: training and reward<\/strong><\/a><\/h2>\n\n\n\n<p>The agents from Reinforcement Learning can find out the optimal control strategy and parameter settings, just connecting Simcenter Amesim (for the digital twin of the physical system) with Simcenter Studio (for the reinforcement learning process). To get automatically the improved controller as an FMU (Functional Mockup Unit), ready for deployment back in the plant model.<\/p>\n\n\n\n<p>The Reinforcement Learning agent is trained in a simulated environment that mirrors real-world conditions using mainly the historical weather data (wind speed, temperature, \u2026).<\/p>\n\n\n\n<p>The simulation allows the agent to learn optimal strategies without risking real-world assets, and once trained, the model is deployed to control the BESS in real-time.<\/p>\n\n\n\n<p>We can compare our two controllers to estimate the benefits:<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-medium\"><img loading=\"lazy\" decoding=\"async\" width=\"600\" height=\"189\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/Rule-based_controller_vs_RL_Neural-Networks_controller-600x189.png\" alt=\"\" class=\"wp-image-70007\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/Rule-based_controller_vs_RL_Neural-Networks_controller-600x189.png 600w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/Rule-based_controller_vs_RL_Neural-Networks_controller-768x242.png 768w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/Rule-based_controller_vs_RL_Neural-Networks_controller.png 849w\" sizes=\"auto, (max-width: 600px) 100vw, 600px\" \/><figcaption class=\"wp-element-caption\">Rule-based controller (left) and Neural network controller (right) from Reinforcement Learning<\/figcaption><\/figure><\/div>\n\n\n<ul class=\"wp-block-list\">\n<li>The <strong>rule-based controller<\/strong> (left). It\u2019s manually defined from knowledge. The control logics is fixed, with possible variations on a few parameters. It\u2019s a simple strategy depending on the State of Charge (SoC) and price evolution. Basically, we define:<ul><li>If the current energy price is lower than the mean value of energy price during the week, the energy is stored into the BESS<\/li><\/ul>\n<ul class=\"wp-block-list\">\n<li>Otherwise, the energy is taken from the BESS and delivered to the grid, until BESS SoC reaches 5%<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>The <strong>neural network controller<\/strong> from the <strong>Reinforcement Learning<\/strong> (right). It\u2019s automatically obtained with no experience needed, except the right selection of the appropriate Reinforcement Learning algorithm. It automatically gets the best control strategy after its learning process with trial and error.<\/li>\n<\/ul>\n\n\n\n<p>The <strong>agent <\/strong>is trained on short period with a random initial \u201cSoC\u201d (state of charge) in a given random week out of a two-year period thanks to the weather data provided for 2022 and 2023.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"685\" height=\"370\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/RL_Training_Weather_conditions_Random_week.png\" alt=\"\" class=\"wp-image-70008\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/RL_Training_Weather_conditions_Random_week.png 685w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/RL_Training_Weather_conditions_Random_week-600x324.png 600w\" sizes=\"auto, (max-width: 685px) 100vw, 685px\" \/><figcaption class=\"wp-element-caption\">Training process over weather conditions (database for 2022 and 2023)<\/figcaption><\/figure><\/div>\n\n\n<p>Then every 100 trainings, it is evaluated for a complete new year (2024) that it has never seen before:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The <strong>inputs <\/strong>of the Neural Networks are the current power demand, the current power generation, the current weather conditions, the next day weather, the current SoC, the current price, and the next day price .<\/li>\n\n\n\n<li>The <strong>output <\/strong>is the battery power.<\/li>\n<\/ul>\n\n\n\n<p>The agent takes a new decision every 10 minutes.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><a><strong>Real-world BESS use case with RL<\/strong><\/a><\/h2>\n\n\n\n<p>Let\u2019s check the typical results obtained for a real-world scenario. We compare the two controllers to measure the benefits using Reinforcement Learning.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-medium\"><img loading=\"lazy\" decoding=\"async\" width=\"600\" height=\"487\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/Real_world_use_case_Smart_BESS_with_RL-600x487.png\" alt=\"\" class=\"wp-image-70009\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/Real_world_use_case_Smart_BESS_with_RL-600x487.png 600w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/Real_world_use_case_Smart_BESS_with_RL-768x624.png 768w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/Real_world_use_case_Smart_BESS_with_RL-900x731.png 900w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/Real_world_use_case_Smart_BESS_with_RL.png 909w\" sizes=\"auto, (max-width: 600px) 100vw, 600px\" \/><figcaption class=\"wp-element-caption\">The manually defined controller is replaced by the optimal controller obtained with Reinforcement Learning (AI)<\/figcaption><\/figure><\/div>\n\n\n<p>The configuration for exploration (training\/validation) of the smart controller (AI\/RL) is the complete BESS system with the wind turbine for renewables . It\u2019s used to verify that the controller works well and is fully efficient and predictive.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The <strong>objective <\/strong>is to find out the optimal control strategy for 1 configuration with weather forecast (future) and energy price evolution.<\/li>\n\n\n\n<li>The <strong>reward <\/strong>is the reduction of the energy bill (so the increase of the associated benefits due to savings) and the reduction of the carbon emissions.<\/li>\n\n\n\n<li>The <strong>exploitation <\/strong>can be done by exporting the optimal controller as an FMU onto the hardware device, connected to the weather forecast.<\/li>\n<\/ul>\n\n\n\n<p>Below is what happens practically for the training and reward process in Simcenter Studio:<\/p>\n\n\n\n<figure class=\"wp-block-video aligncenter\"><video controls src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/Simcenter_Studio_Energy_BESS_AI_Reinforcement-Learning.mp4\"><\/video><figcaption class=\"wp-element-caption\">Video: workflow for Reinforcement Learning in Simcenter Studio<\/figcaption><\/figure>\n\n\n\n<p>The goal is to get an optimal controller quickly. Let\u2019s check on a real-world scenario testing the last year 2024 of weather conditions to formally evaluate how it behaves. Just to confirm it\u2019s efficient and well predictive.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><a><strong>Key benefits for users<\/strong><\/a><\/h2>\n\n\n\n<p>We simulate one complete year (2024) which is basically 365 days. We compare 3 configurations as described below:<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"635\" height=\"395\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/Comparison_energy_bill_3_configurations.png\" alt=\"\" class=\"wp-image-70011\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/Comparison_energy_bill_3_configurations.png 635w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/Comparison_energy_bill_3_configurations-600x373.png 600w\" sizes=\"auto, (max-width: 635px) 100vw, 635px\" \/><figcaption class=\"wp-element-caption\">Comparison of the energy bill for the 3 configurations<\/figcaption><\/figure><\/div>\n\n\n<ul class=\"wp-block-list\">\n<li>Configuration #0 : <strong>[No battery]<\/strong> (dotted grey curve &#8211; reference) is the results without any BESS considered.<\/li>\n\n\n\n<li>Configuration #1 : <strong>[Ruled-base controller]<\/strong> (blue curve) is the previous case we had with a BESS introduced and its control defined manually,<\/li>\n\n\n\n<li>Configuration #2 : <strong>[Neural Network controller]<\/strong> (red curve) is the latest case we tested with a BESS introduced and its control found out automatically with Reinforcement Learning (AI\/RL).<\/li>\n<\/ul>\n\n\n\n<p>There\u2019s no doubt. The comparison proves that the Reinforcement Learning controller is much better than the legacy one, which was defined manually based on quite simple rules known by experience.<\/p>\n\n\n\n<p>We clearly see the benefits using the Reinforcement Learning controller since the electricity bill is strongly reduced after 1 year compared to the other approaches. It\u2019s important to note that the lifespan for such BESS systems is typically 20 years, so any gain obtained over 1 year will be amplified significantly over such a long period of time. Well done, we see great benefits using Reinforcement Learning!<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><a><strong>Going further<\/strong><\/a><\/h2>\n\n\n\n<p>We can consider the benefits of <strong>AI-Optimized BESS<\/strong> for various KPIs (key performance indicators) like its economic efficiency, the grid stability, the sustainability or its scalability when deployed across multiple sites and adapted to other renewable sources like solar.<\/p>\n\n\n\n<p>While looking ahead, as AI continues to evolve, we can already notice that the integration of RL-based controllers with BESS will become more sophisticated, potentially incorporating real-time weather sensors, predictive analytics, and even collaborative multi-agent systems across distributed grids.<\/p>\n\n\n\n<p>This convergence of <strong>renewable energy<\/strong>, <strong>battery storage<\/strong>, and <strong>artificial intelligence<\/strong> marks a transformative step toward a smarter, greener, and more resilient energy future.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"758\" height=\"225\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/Simulation_in_Operations_Energy.png\" alt=\"\" class=\"wp-image-70013\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/Simulation_in_Operations_Energy.png 758w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/Simulation_in_Operations_Energy-600x178.png 600w\" sizes=\"auto, (max-width: 758px) 100vw, 758px\" \/><figcaption class=\"wp-element-caption\">Simulation extends outside \u201cDesign\u201d or \u201cManufacture\u201d areas to now reach the \u201cService\u201d area<\/figcaption><\/figure><\/div>\n\n\n<p>It opens the door to <strong>\u201cSimulations in Operations\u201d<\/strong> with digital twins and <strong>AI-based controllers<\/strong> to take the right informed decisions live in operations. It\u2019s \u201cSimulation for Service\u201d thanks to physics-based-AI.<\/p>\n\n\n\n<p>As usual, System Simulation definitely helps you and your company be successful in your BESS journey thanks to digitalization, with system integration and smart controls.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Learn more about Siemens Simcenter Amesim<\/strong><\/h2>\n\n\n\n<p><a href=\"https:\/\/plm.sw.siemens.com\/en-US\/simcenter\/systems-simulation\/amesim\/\" target=\"_blank\" rel=\"noreferrer noopener\">Simcenter Amesim<\/a>&nbsp;is the leading integrated, scalable system simulation platform, allowing system simulation engineers to virtually assess and optimize the performance of mechatronic systems.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Thus blog post explains how to benefit from smart AI-powered controllers with Reinforcement Learning applied to the BESS (Battery Energy Storage Systems).<\/p>\n","protected":false},"author":27840,"featured_media":70017,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spanish_translation":"","french_translation":"","german_translation":"","italian_translation":"","polish_translation":"","japanese_translation":"","chinese_translation":"","footnotes":""},"categories":[1],"tags":[63732,82,63891,1823,63722,16],"industry":[63664,137,145,150,23375,155],"product":[590],"coauthors":[10813,63894],"class_list":["post-69996","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","tag-decarbonization","tag-digital-twin","tag-renewables","tag-simcenter","tag-sustainability","tag-system-simulation","industry-battery","industry-consumer-products-retail","industry-electronics-semiconductors","industry-energy-utilities","industry-hvac-equipment","industry-industrial-machinery-heavy-equipment","product-simcenter-amesim"],"featured_image_url":"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2025\/11\/Smart_BESS_Cover_page.png","_links":{"self":[{"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/posts\/69996","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/users\/27840"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/comments?post=69996"}],"version-history":[{"count":5,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/posts\/69996\/revisions"}],"predecessor-version":[{"id":70482,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/posts\/69996\/revisions\/70482"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/media\/70017"}],"wp:attachment":[{"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/media?parent=69996"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/categories?post=69996"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/tags?post=69996"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/industry?post=69996"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/product?post=69996"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/coauthors?post=69996"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}