Theorem 1, p. 14 — the unregulated aggregation model attains the benchmark welfare, dispatch and wholesale prices
ProvedDERAggregation.Unregulated.theorem_1Consider the networked market under Assumptions 1 and 2, and assume the benchmark dispatch (9) is feasible (Assumption 3). Let be the optimal solution of (9) and its welfare. Then:
- the aggregation dispatch (21) has an optimal solution;
- every optimal solution of (21) has welfare
and the same dispatch as the benchmark: , , ; 3. the split , (with , ) is an optimal solution of (21); 4. the wholesale prices coincide: a vector is a vector of optimal Lagrange multipliers of (5a) at an optimal solution of (21) if and only if it is one at an optimal solution of (9).
Hence a monopolistic, profit-seeking aggregator using two-part pricing causes no loss of market efficiency: welfare, dispatch and locational prices are those of direct participation.
Formalization Note Theorem 1 names no assumptions; it is stated under Assumptions 1–3, which the paper's proof uses through Propositions 1 and 3 (Assumption 4 follows from Assumption 3 via the split). Problem (21) determines only the net sales , not and separately; "the optimal … are the same as those solving (9)" is stated as items 2 and 3. Problem (21) is written in the separate variables , with the constraints (18) as printed.
import Mathlib import Definitions.Def_DERAggregation_Unregulated_Setting
namespace DERAggregation.Unregulated
/-- Theorem 1 (p. 14). Under Assumptions 1–3, with `(h*, z*, y*)` the optimal solution of the
benchmark dispatch (9): the aggregation dispatch (21) has an optimal solution; every optimal
solution of (21) has welfare `W*_A = W*_B` (22), `x − d = z*`, `y = y*`, `h = h*`; the split
`(h*, [z*]⁺, [−z*]⁺, y*)` is optimal for (21); and the wholesale prices (optimal Lagrange
multipliers of (5a)) of the two models coincide. -/
theorem theorem_1 {K I J L : Type} [Fintype K] [DecidableEq K] [Fintype I] [Fintype J]
[Fintype L] (M : Market K I J L) (h1 : Assumption1 M) (h2 : Assumption2 M)
(h3 : ∃ h z y, FeasB M h z y) :
∃ hB zB yB, IsOptB M hB zB yB ∧
(∃ hA xA dA yA, IsOptA M hA xA dA yA) ∧
(∀ hA xA dA yA, IsOptA M hA xA dA yA →
WA M xA dA yA = WB M zB yB ∧ (∀ i, xA i - dA i = zB i) ∧ yA = yB ∧ hA = hB) ∧
IsOptA M hB (fun i => max (zB i) 0) (fun i => max (-zB i) 0) yB ∧
(∀ lam : K → ℝ, IsPriceA M lam ↔ IsPriceB M lam) := by sorry
end DERAggregation.Unregulated
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.