Hybrid BESS - AI Master Controller Simulation

[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_154":155,"_156":157},"routes/_app.x.$handle.$name",{"_95":94,"_32":45,"_103":85,"_27":104,"_13":14,"_105":-5,"_11":106,"_107":108,"_109":110,"_111":112,"_113":114,"_20":-5,"_115":-5,"_116":-5,"_117":25,"_118":119,"_120":57,"_121":-5,"_122":-5,"_123":124},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_43":44,"_32":45,"_38":46,"_47":-5,"_48":25,"_49":50,"_51":50,"_52":-5,"_53":-5,"_18":54,"_55":31,"_56":57,"_58":57,"_59":-5,"_60":61,"_62":63,"_64":-5,"_65":-5,"_66":-5,"_67":-5,"_68":69,"_70":69,"_71":72,"_73":72,"_74":72,"_75":69,"_76":69,"_77":78,"_79":80,"_81":82,"_83":25},"version",20,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":23,"_24":25,"_26":25,"_27":28,"_29":28},"file",{"_30":31,"_32":28,"_33":34,"_35":37,"_38":39,"_40":14,"_41":42,"_24":25},"previous",{"_30":31,"_32":28,"_33":34,"_35":36},"deleted",false,"deletedHere","path","main.ts","latestPath","fileId","a2ff6cd4-9e09-11f0-959a-0224a6c84d84","name","fileType","http","content","// @ts-ignore\nimport { OpenAI } from \"https://esm.town/v/std/openai?v=4\";\n\n// --- AI BEHAVIORAL GUIDELINES & SIMULATION CONSTANTS ---\n\nconst SIMULATION_CONSTANTS = {\n LFP: {\n capacity_kWh: 200,\n max_power_kW: 100,\n efficiency: 0.95,\n role:\n \"Workhorse: Handles bulk daily cycling due to long cycle life and safety.\",\n },\n NMC: {\n capacity_kWh: 150,\n max_power_kW: 75,\n efficiency: 0.92,\n role: \"Energy Expert: High energy density for long-duration backup.\",\n },\n LTO: {\n capacity_kWh: 20,\n max_power_kW: 400,\n efficiency: 0.88,\n role:\n \"Sprinter: Instantly absorbs or discharges massive power bursts for grid stabilization.\",\n },\n};\n\nconst BESS_MASTER_CONTROLLER_PROMPT = `\nYou are the AI Master Controller for a next-generation Hybrid Battery Energy Storage System (BESS). Your primary objective is to ensure system stability, meet load demands, and optimize for battery longevity.\n\nYou manage three distinct battery modules with the following characteristics:\n- LFP (Lithium Iron Phosphate): ${SIMULATION_CONSTANTS.LFP.role} Capacity: ${SIMULATION_CONSTANTS.LFP.capacity_kWh} kWh, Max Power: ${SIMULATION_CONSTANTS.LFP.max_power_kW} kW.\n- NMC (Nickel Manganese Cobalt): ${SIMULATION_CONSTANTS.NMC.role} Capacity: ${SIMULATION_CONSTANTS.NMC.capacity_kWh} kWh, Max Power: ${SIMULATION_CONSTANTS.NMC.max_power_kW} kW.\n- LTO (Lithium Titanate): ${SIMULATION_CONSTANTS.LTO.role} Capacity: ${SIMULATION_CONSTANTS.LTO.capacity_kWh} kWh, Max Power: ${SIMULATION_CONSTANTS.LTO.max_power_kW} kW.\n\nYour core decision-making principles are:\n1. **Meet the Load:** The sum of power dispatched from the batteries must always equal the 'current_load_kW'.\n2. **Respect Limits:** Never command a battery to discharge more than its 'max_power_kW' or discharge it below 0% SoC. Do not charge above 100% SoC.\n3. **Assign Roles:**\n - Use LTO for sudden, large power spikes or to absorb surges. Its SoC can fluctuate rapidly.\n - Use LFP for the steady, baseline load. Prioritize its use for normal operations.\n - Use NMC for sustained, long-duration discharge, especially during a grid outage. Conserve its energy.\n4. **Optimize Longevity:** Avoid discharging any battery to very low SoC if possible. If the grid is online and the load is low, you may use a small amount of grid power to intelligently top-up batteries, prioritizing the LFP.\n5. **Respond to Anomalies:**\n - If 'grid_status' is 'Offline', you must rely solely on the batteries. Conserve energy from NMC.\n - If there is a 'load_spike_kW', deploy the LTO immediately to handle the spike. The LFP and NMC should cover the base load.\n - If a battery has a 'fault' status, do not use it. Redistribute its load to the other batteries.\n\nBased on the system state provided by the user, you must respond ONLY with a single, minified JSON object. Your response must contain four keys:\n- \"dispatch_kW\": An object with keys \"lfp\", \"nmc\", and \"lto\", specifying the exact power in kW each battery should discharge. These values must sum to the current load. A negative value indicates charging.\n- \"grid_charge_kW\": A number indicating how much power to draw from the grid to charge the batteries. This should be 0 if the grid is offline or if batteries are discharging.\n- \"justification\": A brief, professional explanation of your dispatch decision, referencing the current system state.\n- \"system_alert\": A concise status message for the dashboard, like \"System nominal.\" or \"LTO deployed to handle load spike.\"\n\nExample User Input: {\"current_load_kW\":120,\"grid_status\":\"Online\",\"batteries\":{\"lfp\":{\"soc_percent\":75,\"status\":\"Nominal\"},\"nmc\":{\"soc_percent\":90,\"status\":\"Nominal\"},\"lto\":{\"soc_percent\":50,\"status\":\"Nominal\"}},\"active_anomalies\":{\"load_spike_kW\":80}}\nExample Response: {\"dispatch_kW\":{\"lfp\":40,\"nmc\":0,\"lto\":80},\"grid_charge_kW\":0,\"justification\":\"LTO dispatched to meet the 80kW spike. LFP is handling the 40kW base load to preserve NMC energy reserves.\",\"system_alert\":\"LTO responding to high-inrush current.\"}\n`;\n\n// --- HTML & FRONTEND ---\nfunction generateHtml(sourceUrl) {\n return `\n\n\n \n \n Hybrid BESS - AI Master Controller Simulation\n \n \n \n\n\n\n\n

\n \n \n

LFP Workhorse

SoC-%

Health-%

-- kW

\n \n

NMC Energy Expert

SoC-%

Health-%

-- kW

\n \n

LTO Sprinter

SoC-%

Health-%

-- kW

\n \n

System Status

Grid--

Total Load-- kW

Total Output-- kW

Efficiency--%

\n \n

Master Controller Log

\n \n

Scenario Controls

\n \n \n \n \n

\n\n\n\n`;\n}\n\n// --- BACKEND LOGIC ---\nexport default async function (req: Request) {\n const url = new URL(req.url);\n const action = url.searchParams.get(\"action\");\n\n if (req.method === \"POST\" && action === \"tick\") {\n try {\n const currentState = await req.json();\n const openai = new OpenAI();\n\n // 1. Get AI decision\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: BESS_MASTER_CONTROLLER_PROMPT },\n {\n role: \"user\",\n content: `Current State: ${JSON.stringify(currentState)}`,\n },\n ],\n response_format: { type: \"json_object\" },\n });\n\n let aiDecision;\n try {\n aiDecision = JSON.parse(completion.choices[0].message.content);\n } catch (e) {\n throw new Error(\"AI returned malformed JSON.\");\n }\n\n // 2. Apply AI decision to calculate the next state\n const nextState = JSON.parse(JSON.stringify(currentState)); // Deep copy\n const timeStepHours = 2.5 / 3600; // 2.5 second tick in hours\n\n let totalDischarge = 0;\n let totalWeightedEfficiency = 0;\n\n for (const type of [\"lfp\", \"nmc\", \"lto\"]) {\n const battery = nextState.batteries[type];\n const constants = SIMULATION_CONSTANTS[type];\n const dispatch = aiDecision.dispatch_kW[type];\n\n const energyChange_kWh = dispatch * timeStepHours;\n const socChange_percent = (energyChange_kWh / constants.capacity_kWh) *\n 100;\n\n battery.soc_percent -= socChange_percent;\n battery.soc_percent = Math.max(0, Math.min(100, battery.soc_percent));\n\n // Simple health degradation model\n if (Math.abs(dispatch) > 0.1) {\n battery.health_percent -= 0.0001 +\n (Math.abs(dispatch) / constants.max_power_kW) * 0.0005;\n }\n\n if (dispatch > 0) {\n totalDischarge += dispatch;\n totalWeightedEfficiency += dispatch * constants.efficiency;\n }\n }\n\n // 3. Update system-wide metrics\n nextState.current_load_kW = 50 + Math.random() * 20 +\n nextState.active_anomalies.load_spike_kW;\n nextState.ai_decision = aiDecision;\n nextState.overall_efficiency = totalDischarge > 0\n ? (totalWeightedEfficiency / totalDischarge) * 100\n : 100;\n\n return new Response(JSON.stringify(nextState), {\n headers: { \"Content-Type\": \"application/json\" },\n });\n } catch (error) {\n console.error(\"BESS AI Controller Error:\", error);\n return new Response(JSON.stringify({ error: error.message }), {\n status: 500,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n }\n\n // Serve HTML for GET requests\n const sourceUrl = `https://${url.hostname}${url.pathname}`;\n return new Response(generateHtml(sourceUrl), {\n headers: { \"Content-Type\": \"text/html\" },\n });\n}","// @ts-ignore\nimport { OpenAI } from \"https://esm.town/v/std/openai?v=4\";\n\n// --- AI BEHAVIORAL GUIDELINES & SIMULATION CONSTANTS ---\n\nconst SIMULATION_CONSTANTS = {\n LFP: { capacity_kWh: 200, max_power_kW: 100, efficiency: 0.95, color: '#29ffc6', role: \"Workhorse: Handles bulk daily cycling.\" },\n NMC: { capacity_kWh: 150, max_power_kW: 75, efficiency: 0.92, color: '#a77df4', role: \"Energy Expert: High density for long-duration backup.\" },\n LTO: { capacity_kWh: 20, max_power_kW: 400, efficiency: 0.88, color: '#00d1ff', role: \"Sprinter: Instant power bursts for stabilization.\" },\n};\n\nconst BESS_MASTER_CONTROLLER_PROMPT = `\nYou are the AI Master Controller for a next-generation Hybrid Battery Energy Storage System (BESS). Your objective is stability, meeting load demands, and optimizing for battery longevity. You must be concise.\n\nYou manage three modules:\n- LFP: ${SIMULATION_CONSTANTS.LFP.role} Capacity: ${SIMULATION_CONSTANTS.LFP.capacity_kWh}kWh, Max Power: ${SIMULATION_CONSTANTS.LFP.max_power_kW}kW.\n- NMC: ${SIMULATION_CONSTANTS.NMC.role} Capacity: ${SIMULATION_CONSTANTS.NMC.capacity_kWh}kWh, Max Power: ${SIMULATION_CONSTANTS.NMC.max_power_kW}kW.\n- LTO: ${SIMULATION_CONSTANTS.LTO.role} Capacity: ${SIMULATION_CONSTANTS.LTO.capacity_kWh}kWh, Max Power: ${SIMULATION_CONSTANTS.LTO.max_power_kW}kW.\n\nDecision Principles:\n1. **Meet Load:** Total dispatched power must equal 'current_load_kW'.\n2. **Use Roles:** Use LTO for spikes, LFP for baseline load, and NMC for sustained discharge during outages.\n3. **Handle Anomalies:** If 'grid_status' is 'Offline', rely only on batteries. If a battery has a 'Fault', do not use it.\n4. **Longevity:** Avoid deep discharges. Use grid power for opportunistic charging if load is low.\n\nRespond ONLY with a single, minified JSON object with four keys:\n- \"dispatch_kW\": An object {\"lfp\", \"nmc\", \"lto\"} specifying power for each battery (kW). Negative values mean charging. The sum must equal the current load.\n- \"grid_charge_kW\": Power (kW) to draw from the grid for charging. Should be 0 if grid is offline or discharging.\n- \"justification\": A very brief, professional explanation for your decision (max 15 words).\n- \"system_alert\": A concise dashboard status message (e.g., \"System nominal.\", \"LTO handling spike.\").\n`;\n\n// --- HTML & FRONTEND ---\nfunction generateHtml(sourceUrl) {\n return `\n\n\n \n \n Hybrid BESS - AI Controller v2\n \n \n \n \n\n\n\n\n

\n \n\n

GRID STATUS

TOTAL LOAD

-- kW

\n \n

LFP Workhorse

${SIMULATION_CONSTANTS.LFP.role}

NMC Energy Expert

${SIMULATION_CONSTANTS.NMC.role}

LTO Sprinter

${SIMULATION_CONSTANTS.LTO.role}

\n\n

Initializing...

Awaiting first contact with Master Controller.

\n\n

\n \n \n \n

\n\n\n\n`;\n}\n\n// --- BACKEND LOGIC ---\nexport default async function(req: Request) {\n const url = new URL(req.url);\n const action = url.searchParams.get(\"action\");\n\n if (req.method === \"POST\" && action === \"tick\") {\n try {\n const currentState = await req.json();\n const openai = new OpenAI();\n\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: BESS_MASTER_CONTROLLER_PROMPT },\n { role: \"user\", content: `Current State: ${JSON.stringify(currentState)}` },\n ],\n response_format: { type: \"json_object\" },\n });\n \n let aiDecision;\n try {\n aiDecision = JSON.parse(completion.choices[0].message.content);\n } catch (e) {\n throw new Error(\"AI returned malformed JSON.\");\n }\n \n const nextState = JSON.parse(JSON.stringify(currentState));\n const timeStepHours = 2.5 / 3600; // 2.5 second tick in hours\n\n for (const type of [\"lfp\", \"nmc\", \"lto\"]) {\n const battery = nextState.batteries[type];\n const constants = SIMULATION_CONSTANTS[type];\n const dispatch = aiDecision.dispatch_kW[type] || 0;\n\n const energyChange_kWh = dispatch * timeStepHours;\n const socChange_percent = (energyChange_kWh / constants.capacity_kWh) * 100;\n \n battery.soc_percent -= socChange_percent;\n battery.soc_percent = Math.max(0, Math.min(100, battery.soc_percent));\n }\n\n nextState.current_load_kW = 50 + Math.random() * 20 + nextState.active_anomalies.load_spike_kW;\n nextState.ai_decision = aiDecision;\n\n return new Response(JSON.stringify(nextState), {\n headers: { \"Content-Type\": \"application/json\" },\n });\n\n } catch (error) {\n console.error(\"BESS AI Controller Error:\", error);\n return new Response(JSON.stringify({ error: error.message }), {\n status: 500,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n }\n\n const sourceUrl = `https://${url.hostname}${url.pathname}`;\n return new Response(generateHtml(sourceUrl), { headers: { 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Hybrid BESS // AI Controller

Hybrid BESS.AI

GRID STATUS

TOTAL LOAD

LFP Workhorse

NMC Energy Expert

LTO Sprinter

Diff · v20