Pricing

Pricing#

Price-determination subproblem and price consistency constraints.

Price Determination Subproblem#

API path: apem.order_book_based_model.euphemia.pricing.price_determination_subproblem

class PriceSubproblem(master_problem)[source]#

Bases: object

Formulate and solve the Euphemia price-determination subproblem.

The pricing model takes the current incumbent allocation from MasterProblem and searches for market clearing prices that satisfy primal-dual consistency, PAB restrictions, MIC or MP conditions, and network-price coupling.

Parameters:

master_problem (MasterProblem)

add_MIC_MP_constraints(order, step_order_df, order_type)[source]#

Add the MIC or MP revenue constraint for one accepted complex-style order.

Parameters:

  • order (Series) – Parent complex or scalable complex order.

  • step_order_df (DataFrame) – Step-order dataframe containing the child bids.

  • order_type (OrderType) – Whether the parent order is complex or scalable complex.

Return type:

None

add_atc_price_consistency_constraints()[source]#

Add ATC active-set price coupling constraints based on incumbent flows.

For each directed arc (i, j, t) with 0 <= f <= cap:

f == 0      -> MCP[j,t] <= MCP[i,t]
f == cap    -> MCP[j,t] >= MCP[i,t]
0 < f < cap -> MCP[j,t] == MCP[i,t]
Return type:

None

add_block_order_constraints()[source]#

Add surplus constraints for accepted block orders.

Standard accepted blocks must remain in the money, linked block families must have non-negative total surplus, and accepted linked leaf blocks must not create negative standalone surplus.

Return type:

None

add_complex_order_constraints()[source]#

Add pricing constraints for accepted complex orders.

Return type:

None

add_fbmc_price_consistency_constraints()[source]#

Add FBMC dual-consistent MCP/PTDF coupling based on incumbent net positions.

The formulation introduces one system price component per period and dual multipliers on FBMC constraints:

mu_up[c,t] >= 0   for PTDF * NP <= RAM
mu_lo[c,t] >= 0   for PTDF * NP >= LB   (when LB exists)

Zonal prices are linked through stationarity:

MCP[z,t] = lambda[t] - sum_c PTDF[c,t,z] * (mu_up[c,t] - mu_lo[c,t])

Active-set complementarity is enforced from the incumbent allocation:

if upper_slack > tol: mu_up[c,t] = 0
if lower_slack > tol: mu_lo[c,t] = 0
Return type:

None

add_linked_block_order_constraints(parent_order)[source]#

Add a non-negative family-surplus constraint for one linked parent block.

Parameters:

parent_order (Series)

Return type:

None

add_linked_leafs_positive_surplus(child_order)[source]#

Add a non-negative surplus constraint for one accepted linked child block.

Parameters:

child_order (Series)

Return type:

None

add_load_gradient_constraints(order, step_order_df, order_type)[source]#

Add a non-negative surplus constraint for one accepted load-gradient order.

Parameters:

  • order (Series) – Parent complex or scalable complex order.

  • step_order_df (DataFrame) – Step-order dataframe containing the child bids.

  • order_type (OrderType) – Whether the parent order is complex or scalable complex.

Return type:

None

add_piecewise_linear_order_constraints()[source]#

Add price-consistency constraints for all piecewise-linear orders.

Return type:

None

add_scalable_complex_order_constraints()[source]#

Add pricing constraints for accepted scalable complex orders.

Return type:

None

add_step_order_constraint(step_order, infix)[source]#

Add price-consistency constraints for one step order.

Fully accepted step orders must be in the money, fully rejected step orders must be out of the money, and partially accepted step orders must clear at the order price.

Parameters:

  • step_order (Series)

  • infix (str)

Return type:

None

add_step_order_constraints()[source]#

Add pricing constraints for all step orders in the incumbent allocation.

Return type:

None

extract_prices()[source]#

Extract the optimized market clearing prices from the pricing model.

Returns:

A mapping keyed by period t for uniform pricing, or by (zone, t) for zonal pricing runs.

Return type:

dict[Any, float]

solve_price_determination_subproblem()[source]#

Formulate and solve the full pricing model for the current allocation.

The objective keeps prices near the midpoint of the allowed price range while the constraints enforce order acceptance consistency and, when enabled, network price coupling and accepted-order revenue conditions.

Return type:

None