Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Flexible Ramping Product Procurement in Day-Ahead Markets

Ogun Yurdakul, Erik Ela, Farhad Billimoria

Abstract

Flexible ramping products (FRPs) emerge as a promising instrument for addressing steep and uncertain ramping needs through market mechanisms. Initial implementations of FRPs in North American electricity markets, however, revealed several shortcomings in existing FRP designs. In many instances, FRP prices failed to signal the true value of ramping capacity, most notably evident in zero FRP prices observed in a myriad of periods during which the system was in acute need for rampable capacity. These periods were marked by scheduled but undeliverable FRPs, often calling for operator out-of-market actions. On top of that, the methods used for procuring FRPs have been primarily rule-based, lacking explicit economic underpinnings. In this paper, we put forth an alternative framework for FRP procurement, which seeks to set FRP requirements and schedule FRP awards such that the expected system operation cost is minimized. Using real world data from U.S. ISOs, we showcase the relative merits of the framework in (i) reducing the total system operation cost, (ii) improving price formation, (iii) enhancing the the deliverability of FRP awards, and (iv) reducing the need for out-of-market actions.

Flexible Ramping Product Procurement in Day-Ahead Markets

Abstract

Flexible ramping products (FRPs) emerge as a promising instrument for addressing steep and uncertain ramping needs through market mechanisms. Initial implementations of FRPs in North American electricity markets, however, revealed several shortcomings in existing FRP designs. In many instances, FRP prices failed to signal the true value of ramping capacity, most notably evident in zero FRP prices observed in a myriad of periods during which the system was in acute need for rampable capacity. These periods were marked by scheduled but undeliverable FRPs, often calling for operator out-of-market actions. On top of that, the methods used for procuring FRPs have been primarily rule-based, lacking explicit economic underpinnings. In this paper, we put forth an alternative framework for FRP procurement, which seeks to set FRP requirements and schedule FRP awards such that the expected system operation cost is minimized. Using real world data from U.S. ISOs, we showcase the relative merits of the framework in (i) reducing the total system operation cost, (ii) improving price formation, (iii) enhancing the the deliverability of FRP awards, and (iv) reducing the need for out-of-market actions.
Paper Structure (19 sections, 4 equations, 10 figures, 6 tables)

This paper contains 19 sections, 4 equations, 10 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Background and Contributions
  3. Need for FRPs
  4. Review of Existing Approaches
  5. Research and Practical Gaps & Our Contributions
  6. Proposed Methodology
  7. First Market Pass: Stochastic Unit Commitment (SUC) Problem
  8. Setting the FRP Requirements
  9. Second Market Pass: Day-Ahead Market Clearing (DAMC) Problem
  10. Numerical Experiments
  11. Datasets and Benchmarks
  12. Results
  13. Case Study I
  14. Case Study II Results
  15. Case Study III Results
  16. ...and 4 more sections

Figures (10)

  • Figure 1: Cost curves of DGs in the toy example
  • Figure 2: Flowchart depicting the execution of the proposed methodology. Observe that the optimal schedules of the DGs committed by the first market pass are passed onto the second market pass. Subsequently, the second pass utilizes the passed information to prioritize the committed DGs for the DAM awards by enforcing that the DGs committed by the first pass remain committed in the optimal DAMC solution. Finally, the second pass issues financially binding DAM awards and outputs the LMPs and the market-clearing price for capacity (MCPC) for the up-FRP and up-FRP products.
  • Figure 3: Total system operation costs as a function of the standard deviation level of the random forecast error used in constructing the out-of-sample scenarios.
  • Figure 4: Total shed load levels as a function of the standard deviation level of the random forecast error used in constructing the out-of-sample scenarios.
  • Figure 5: Production and startup costs of the generators $g_3$ and $g_5$ in the IEEE 14-bus test system used in Case Study I experiments.
  • ...and 5 more figures