Market Operation Services
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TCR brings a multi-disciplinary, quantitative based approach to its analyses of market operation issues. The foundation of our approach is a thorough knowledge of generation, transmission and distribution technologies and systems. We build upon that technical foundation by applying principles from economics and finance informed by the goals of utility regulation and public policy.
The TCR team understands the rules and decision processes that drive price formation in wholesale markets for energy & ancillary services, capacity and renewable energy credits (RECs). Our team also knows the principles of a sound rate structure and the need for a reasonable balancing of them - economic efficiency (rates should reflect the utility‘s marginal costs of providing its services), effectiveness in yielding revenue requirements (rates should enable the utility to recover its embedded costs) and fairness in the allocation of costs (rates should not change dramatically, rate and bill impacts for each rate class should be perceived as fair). TCR provides analyses with rigorous results that withstand peer reviews and litigation scrutiny. We present those analyses and results clearly and convincingly to both technical and non-technical audiences. |
Representative Projects
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Case Study
An example of TCR’s expert testimony on market operations, supported by quantitative analyses, is Dr. Richard Tabors’ affidavit in response to a Federal Energy Regulatory Commission (FERC) July 19, 2018, Order Granting Rehearing and Establishing Paper Hearing Procedures, 164 FERC ¶ 61,035 (2018). At issue was the choice of a methodology for allocating the cost of a proposed Artificial Island (“AI”) project among the Load Serving Entities (“LSEs”) within the PJM Interconnection. PJM and other intervenors had identified three possible methodologies based on engineering criteria, either flow over transmission lines (i.e., Solution-Based DFAX, Stability Interface-DFAX) or flow angle related measurement (i.e., Stability Deviation). The New Jersey State Agencies retained Dr. Tabors to comment on the merits of those potential methodologies. His affidavit makes the following key points.
- FERC established a cost allocation principle in Order No. 1000 of Docket RM10-23-000 that requires the cost of such projects to be “…allocated to those within the transmission planning region that benefit from those facilities in a manner that is at least roughly commensurate with estimated benefits.” That Order further specifies that the determination of those beneficiaries may consider the extent to which the transmission facilities “…provide for maintaining reliability and sharing reserves, production cost savings and congestion relief, and/or meeting Public Policy Requirements.”
- The potential methodologies identified by PJM et al may not be consistent with the FERC principle because none of them quantify the economic benefits of the AI project to retail customers, the ultimate beneficiaries of the project. In contrast, an LMP-based methodology that allocates the costs of the AI facility to each LSE in proportion to the cost savings the retail customers served by that LSE receive from the facility would be consistent with the FERC principle. That methodology would calculate cost savings by using detailed modeling of the power system to calculate the energy, ancillary service and constraint costs to LSEs for a scenario without the AI project in place and then comparing them to the costs for a scenario with the AI project in place.
- There is evidence indicating that an LMP-based methodology would identify measurable economic benefits. For example, had the AI project been in operation in 2017 and removed constraints on Hope Creek-Red Lion and New Freedom – East Windsor 500 KV lines entirely it would have saved ultimate consumers more than forty-six million dollars in avoided congestion costs in the Day Ahead and Real Time markets. Dr. Tabors developed this estimate based upon PJM published power flow (MMWG2017SUM2015) and historic binding constraints and marginal costs for 2017.