Overprocurement of balancing capacity may increase the welfare in the cross-zonal energy-reserve coallocation problem

TL;DR

The paper addresses cross-zonal energy-reserve co-allocation under flow-based network constraints in European day-ahead markets. It introduces a simple co-allocation model with three products $E$, $R+$, and $R-$ within a portfolio-bidding framework, using $PTDF$-based flows and a worst-case-flow approach to ensure deliverability, and defines total social welfare as $TSW$ with decomposition into bid surplus and congestion rent. A four-zone illustrative example demonstrates a counterintuitive result: overprocurement of reserves can increase welfare by enabling unused reserves to alleviate congestion, raising $TSW$ from 76 to 80 EUR and altering the distribution between bid surplus and congestion rent. The analysis highlights cash-flow stability and discusses activation-cost effects, noting that unknown activation costs at allocation could influence the potential benefits of overprocurement. The work suggests that relying on reserve overprocurement may improve market welfare in certain network states and calls for a computational framework to identify when such strategies are advantageous, including the treatment of activation costs in the optimization.

Abstract

When the traded energy and reserve products between zones are co-allocated to optimize the infrastructure usage, both deterministic and stochastic flows have to be accounted for on interconnector lines. We focus on allocation models, which guarantee deliverability in the context of the portfolio bidding European day-ahead market framework, assuming a flow-based description of network constraints. In such models, as each unit of allocated reserve supply implies additional cost, it is straightforward to assume that the amount of allocated reserve is equal to the accepted reserve demand quantity. However, as it is illustrated by the proposed work, overprocurement of reserves may imply counterintuitive benefits. Reserve supplies not used for balancing may be used for congestion management, thus allowing valuable additional flows in the network.

Figures (5)

• Figure 1: The topology of the example network. Reference directions for positive sign flows are denoted with arrows. Maximal flow values of lines are indicated by $\overline{f}_i$, and are assumed to be identical for both the positive and negative direction of the line.
• Figure 2: The flows implied in the network, by the cross-zonal allocation of 4 units of energy (left) or 4 units of positive reserve (right) from zone A to zone B. Numbers in parentheses refer to inlets in the case of reserve activation. Arrows pointing inside of the circles representing the zones indicate inlets corresponding to energy supply or reserve supply activation, while arrows pointing outward correspond to energy consumption and arising reserve demand. Dashed flows correspond to reserve-activation related flows, arising in the case of full activation of the reserves.
• Figure 3: The flows of the network, implied by the cross-zonal allocation of 4 units of negative reserve from zone C to zone B in the case of full activation. Orange color indicates a line load on the capacity limit.
• Figure 4: The worst-case flows in the network, in the case of transporting 4 units of energy (continuous line) and 4 units of positive reserve from zone A to zone B (dashed line). Line 4 at its limit load is indicated with orange color.
• Figure 5: The worst-case flows in the network, in the case of transporting 8 units of energy (continuous line) and 4 units of positive reserve from zone A to zone B (dashed line). Negative reserve supply allocated in node C and positive reserve supply allocated in B are used for congestion management, denoted with green color.