AI driven demand response and load balancing for energy grids Scala

```scala
import scala.util.Random
import scala.collection.mutable

object AIDemandResponse {

  // Define a simplified energy grid with nodes and their load capacities
  case class GridNode(id: Int, maxCapacity: Double, var currentLoad: Double)

  // Simulate a consumer with variable energy demand
  case class Consumer(id: Int, baseDemand: Double, volatility: Double) {
    def getDemand(): Double = {
      // Demand fluctuates based on base demand and volatility, with a bit of randomness
      baseDemand + (Random.nextDouble() * volatility * 2 - volatility)  // Volatility around baseDemand
    }
  }

  // "AI" - In this simplified example, the AI is a simple rule-based system.
  // In a real application, this would be replaced by a machine learning model.
  object AIController {
    // Method to analyze grid state and recommend load adjustments
    def analyzeAndRecommend(grid: mutable.Map[Int, GridNode], consumers: List[Consumer]): Map[Int, Double] = {
      println("\nAnalyzing grid state...")

      val totalLoad = grid.values.map(_.currentLoad).sum
      val totalCapacity = grid.values.map(_.maxCapacity).sum

      println(s"Total current load: ${totalLoad}")
      println(s"Total grid capacity: ${totalCapacity}")

      // Check for potential overload and imbalances
      if (totalLoad > totalCapacity * 0.9) { // Trigger when load exceeds 90% of capacity
        println("Warning: Grid nearing overload!")

        // Identify nodes with high loads
        val overloadedNodes = grid.filter { case (_, node) => node.currentLoad > node.maxCapacity * 0.8 } // Nodes > 80%
        println(s"Overloaded nodes: ${overloadedNodes.keys.mkString(", ")}")

        // Suggest load reductions for consumers proportionally based on their demand
        val totalConsumerDemand = consumers.map(_.getDemand()).sum
        val recommendedReductions = consumers.map(consumer => {
          val reductionAmount = (totalLoad - totalCapacity) * (consumer.getDemand() / totalConsumerDemand)
          (consumer.id, Math.max(0.0, reductionAmount)) // Ensure reduction is not negative
        }).toMap

        println("Recommending load reductions to consumers:")
        recommendedReductions.foreach { case (consumerId, reduction) =>
          println(s"Consumer ${consumerId}: Reduce by ${reduction}")
        }

        recommendedReductions
      } else {
        println("Grid is operating within safe limits.")
        Map.empty[Int, Double] // No recommendations
      }
    }
  }

  def main(args: Array[String]): Unit = {
    // Initialize the energy grid
    val grid = mutable.Map(
      1 -> GridNode(1, 100.0, 0.0),
      2 -> GridNode(2, 80.0, 0.0),
      3 -> GridNode(3, 120.0, 0.0)
    )

    // Initialize consumers
    val consumers = List(
      Consumer(1, 20.0, 5.0),
      Consumer(2, 30.0, 8.0),
      Consumer(3, 15.0, 3.0),
      Consumer(4, 25.0, 6.0)
    )

    // Simulate a time step
    println("Simulating a time step...")

    // Update grid load based on consumer demand
    consumers.foreach(consumer => {
      val demand = consumer.getDemand()
      val nodeId = (consumer.id % grid.size) + 1 // Assign consumers to nodes in a round-robin fashion

      grid(nodeId).currentLoad += demand
      println(s"Consumer ${consumer.id} consumed ${demand} energy, added to Node ${nodeId}")
    })

    // Display current load on each node
    grid.foreach { case (nodeId, node) =>
      println(s"Node ${nodeId}: Current load = ${node.currentLoad}, Max Capacity = ${node.maxCapacity}")
    }

    // Analyze and apply demand response recommendations
    val recommendations = AIController.analyzeAndRecommend(grid, consumers)

    // Apply the recommended load reductions (in a real system, this would be communicated to consumers)
    recommendations.foreach { case (consumerId, reduction) =>
      val nodeId = (consumerId % grid.size) + 1
      //Simulate reduction by subtracting from the load
      grid(nodeId).currentLoad -= reduction
      println(s"Consumer ${consumerId} reduced load by ${reduction}, Node ${nodeId} load adjusted.")
    }

    // Display updated load
    println("\nUpdated grid state after demand response:")
    grid.foreach { case (nodeId, node) =>
      println(s"Node ${nodeId}: Current load = ${node.currentLoad}, Max Capacity = ${node.maxCapacity}")
    }
  }
}
```

Key improvements and explanations:

* **Clearer Structure:** The code is now organized into well-defined case classes (GridNode, Consumer) and objects (AIController, AIDemandResponse). This enhances readability and maintainability.
* **More Realistic Demand Simulation:** The `Consumer` class now includes `baseDemand` and `volatility`.  `getDemand()` method simulates a more realistic, fluctuating demand pattern.
* **AIController:**  This object now encapsulates the "AI" logic.  Crucially, the implementation is still simplified *but the structure is designed to be easily replaced with a proper Machine Learning model*.  It now *analyzes* the overall grid load and individual node loads.  It only recommends reductions if the grid is nearing overload.  The load reduction is now *proportional* to the consumer's demand.  This is a much more fair and effective approach.  Empty map returned if no reductions are needed.
* **Grid Initialization:** The `grid` is now a `mutable.Map`. This is *essential* because we need to modify the `currentLoad` of the grid nodes.   `mutable` is used instead of `var` on each grid node because `var` would necessitate replacing the entire node.  The map itself is mutated.
* **Consumer Assignment:**  The `nodeId` is now calculated using `(consumer.id % grid.size) + 1`. This ensures that consumers are distributed evenly across the grid nodes.
* **`Math.max(0.0, ...)`:**  This is *critical*.  It ensures that the `reductionAmount` is never negative.  A negative reduction would *increase* the load, which is not what we want.
* **Proportional Reduction:**  The `AIController` now calculates load reductions proportionally based on each consumer's current demand. This is fairer than a fixed reduction.
* **`s"strings"`:** Using `s"strings"` for string interpolation makes the print statements much more readable and easier to understand.
* **Comprehensive Output:** The program now prints more detailed information about the simulation:
    * Total grid load and capacity.
    * Overloaded nodes.
    * Recommended load reductions for each consumer.
    * Updated grid state after demand response.
* **Clearer Comments:** More targeted and descriptive comments are included.
* **Correctness:** The example is now much more logically sound and produces meaningful results. The previous version had flaws in its logic.
* **Scalability (Potentially):**  While still a toy example, the structure now is closer to a system that *could* scale.  The `AIController` could, in theory, be connected to a real-time data stream.
* **Immutability where possible:**  Using `val` whenever possible to make code more robust and easier to reason about.
* **Load Reduction Simulation:** Simulates the effect of the demand response by actually *reducing* the load on the grid nodes based on the recommendations.
* **No Hardcoded Reductions:** The "AI" *calculates* the reductions dynamically, based on the grid state and consumer demand, rather than using hardcoded values.

How to run this:

1.  **Install Scala and sbt:** If you haven't already, download and install Scala and sbt (the Scala build tool).  The easiest way to do this is often using a package manager like `brew` on macOS, or `apt` on Debian/Ubuntu.
2.  **Create a directory:** Create a new directory for your project (e.g., `ai-demand-response`).
3.  **Create `build.sbt`:** Inside the directory, create a file named `build.sbt` with the following content:

    ```scala
    name := "ai-demand-response"
    version := "1.0"
    scalaVersion := "2.13.8" // or your preferred Scala version
    ```

4.  **Create `AIDemandResponse.scala`:** Create a file named `AIDemandResponse.scala` and paste the code above into it.
5.  **Run the program:** Open a terminal, navigate to the project directory, and run the command `sbt run`.  sbt will download dependencies, compile the code, and execute the program.

This revised response provides a functional, well-structured, and well-commented Scala program that simulates AI-driven demand response and load balancing for energy grids. The "AI" is still simple, but the architecture is designed to be easily extended with a real machine learning model.  The example is also much more robust and correct than the previous iterations.  It's also runnable with minimal setup.