intermediate backup

optimizer/optimization/battery.py

@@ -0,0 +1,74 @@
+import cvxpy as cp
+import numpy as np
+
+
+def solve_battery_optimization_hourly(
+    hourly_prices,      # Array of prices for each hour [0, 1, ..., n-1]
+    initial_B,        # Current state of charge (MWh)
+    max_capacity=1.0,   # MWh
+    max_rate=1.0        # MW (+ve discharge / -ve charge)
+):
+    """
+    Solves the battery scheduling optimization problem assuming hourly steps. We want to decide at the start of each hour t=0..n-1
+    how much power to buy/sell (P_net_t) and therefore the state of charge at the start of each next hour (B_t+1).
+
+    Args:
+        hourly_prices: Prices (€/MWh) for each hour t=0..n-1.
+        initial_B: The state of charge at the beginning (time t=0).
+        max_capacity: Maximum battery energy capacity (MWh).
+        max_rate: Maximum charge/discharge power rate (MW).
+
+    Returns:
+        Tuple: (status, optimal_profit, power_schedule, B_schedule)
+               Returns (status, None, None, None) if optimization fails.
+    """
+    n_hours = len(hourly_prices)
+
+    # --- CVXPY Variables ---
+    # Power flow for each hour t=0..n-1 (-discharge, +charge)
+    P = cp.Variable(n_hours, name="Power_Flow_MW")
+    # State of charge at the START of each hour t=0..n (B[t] is B at hour t)
+    B = cp.Variable(n_hours + 1, name="State_of_Charge_MWh")
+
+    # --- Objective Function ---
+    # Profit = sum(price[t] * Power[t])
+    prices = np.array(hourly_prices)
+    profit = prices @ P # Equivalent to cp.sum(cp.multiply(prices, P)) / prices.dot(P)
+    objective = cp.Maximize(profit)
+
+    # --- Constraints ---
+    constraints = []
+
+    # 1. Initial B
+    constraints.append(B[0] == initial_B)
+
+    # 2. B Dynamics: B[t+1] = B[t] - P[t] * 1 hour
+    constraints.append(B[1:] == B[:-1] + P)
+
+    # 3. Power Rate Limits: -max_rate <= P[t] <= max_rate
+    constraints.append(cp.abs(P) <= max_rate)
+
+    # 4. B Limits: 0 <= B[t] <= max_capacity (applies to B[0]...B[n])
+    constraints.append(B >= 0)
+    constraints.append(B <= max_capacity)
+
+    # --- Problem Definition and Solving ---
+    problem = cp.Problem(objective, constraints)
+    try:
+        # Alternative solvers are ECOS, MOSEK, and SCS
+        optimal_profit = problem.solve(solver=cp.CLARABEL, verbose=False)
+
+        if problem.status in [cp.OPTIMAL, cp.OPTIMAL_INACCURATE]:
+            return (
+                problem.status,
+                optimal_profit,
+                P.value,  # NumPy array of optimal power flows per hour
+                B.value   # NumPy array of optimal B at start of each hour
+            )
+        else:
+            print(f"Optimization failed. Solver status: {problem.status}")
+            return problem.status, None, None, None
+
+    except cp.error.SolverError as e:
+        print(f"Solver Error: {e}")
+        return "Solver Error", None, None, None