## Abstract

The decreasing cost of energy storage technologies coupled with their potential to bring significant benefits to electric power networks have kindled research efforts to design both market and regulatory frameworks to facilitate the efficient construction and operation of such technologies. In this paper, we examine an *open access* approach to the integration of storage, which enables the complete decoupling of a storage facility’s ownership structure from its operation. In particular, we analyze a nodal spot pricing system built on a model of economic dispatch in which storage is centrally dispatched by the independent system operator (ISO) to maximize social welfare. Concomitant with such an approach is the ISO’s collection of a merchandising surplus reflecting congestion in storage. We introduce a class of tradable electricity derivatives—referred to as *financial storage rights* (FSRs)—to enable the redistribution of such rents in the form of financial property rights to storage capacity; and establish a generalized *simultaneous feasibility test* to ensure the ISO’s revenue adequacy when allocating such financial property rights to market participants. Several advantages of such an approach to open access storage are discussed. In particular, we illustrate with a stylized example the role of FSRs in synthesizing fully hedged, fixed-price bilateral contracts for energy, when the seller and buyer exhibit differing intertemporal supply and demand characteristics, respectively.

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## Notes

The PJM Interconnection has explored a similar regulatory framework in which energy storage would be operated and compensated traditionally like a transmission asset (PJM 2012).

According to the formulation of DC power flow considered in Sect. 2.2, the line flow \(p_{ij}(k)\) corresponds to a single entry of the vector \(H({\mathbf {v}}(k) + {\mathbf {u}}(k))\). And, if there is no line connecting nodes \(i\) and \(j\), then \(p_{ij}(k) = - p_{ji}(k) = 0\) necessarily.

We refer the reader to Rosellón and Kristiansen (2013) for a recent survey on financial transmission rights.

The \(i\)th element of the net injection vector \({\mathbf {t}}(k)\) is given by \(t_i(k) = \sum _{j=1}^n (t_{ij}(k)-t_{ji}(k))\). It follows that \({\mathbf {1}}^{\top } {\mathbf {t}}(k)=0\). Also, the \(\ell \)th element of the flowgate vector \({\mathbf {f}}(k)\) is given by the \(k\)th element of the FGR \({\mathbf {f}}_\ell \).

We refer the reader to (Oren 2013, Sect. 3.6), which examines several mechanisms to cover revenue shortfalls that might occur when settling payments to FTR holders in the event of transmission line contingencies. For example, PJM handles revenue inadequacy in settling FTR payments by prorating the revenue shortfall among the FTR holders; whereas, in NYISO-run markets, transmission line owners are held responsible for the shortfall (O’Neill et al. 2013). A mechanism of the former type generally transfers the risk of shortfall to the FTR holders, undermines the ability of FTRs to provide perfect price hedges, and is vulnerable to gaming due to the socialization of the shortfalls. Conversely, a mechanism of the latter type fully funds the outstanding rights, thereby transferring the risk of shortfall to the transmission line owners themselves. An argument in favor of such a mechanism is that it provides an incentive to transmission line owners to effectively maintain their assets, and avoids the socialization of revenue shortfalls among the FTR holders (O’Neill et al. 2013; Oren 2013).

Clearly, each of these amounts is as equally likely to be negative as positive, depending on the specific values of the contract price and nodal spot prices.

Of course, this is but one of several natural ways in which the CFD might be specified. Alternative specifications that entail risk sharing between the supplier and demander can also be envisaged.

It is worth mentioning that, should the two parties enter into a bilateral contract specifying common production and consumption profiles, i.e., \({\mathbf {q}}_i = {\mathbf {q}}_j\), the storage congestion charge would vanish – thereby eliminating the need for the procurement of a FSR in the pursuit of a perfectly hedged, fixed price contract.

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## Acknowledgements

The authors would like to thank Shmuel Oren, Pravin Varaiya (UC Berkeley), Michael Swider (New York ISO), Eugene Litvinov, Feng Zhao, Chris Geissler, Tongxin Zheng, Jinye Zhao (New England ISO), participants of the 2014 CERTS Reliability and Markets Internal Program Review, and an anonymous referee for their helpful comments and feedback. This work was supported in part by NSF grant ECCS-1351621, NSF grant CNS-1239178, NSF grant CNS-1135844, US DoE under the CERTS initiative, and the Simons Institute for the Theory of Computing.

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## Appendix

### Appendix

### Proof of Lemma 1

Let \((V,U,{\varLambda })\) denote an efficient market equilibrium throughout. And, let \((\varvec{{\lambda }}(k), \gamma (k), \varvec{{\mu }}(k), \overline{\varvec{{\nu }}}_i, \underline{\varvec{{\nu }}}_i)\) denote the corresponding Lagrange multipliers satisfying the KKT conditions (6)–(13) for all \(k\) and \(i\). We prove the desired result by establishing nonnegativity of both the TCS and SCS.

### Proposition 1

\( \mathrm{TCS}= \sum _{k=0}^{N-1} \varvec{{\mu }}(k)^{\top } {\mathbf {c}} \ge 0\).

### Proof

We have that \(\mathrm{TCS}= -\sum _{k=0}^{N-1} \varvec{{\lambda }}(k)^{\top } ({\mathbf {v}}(k)+{\mathbf {u}}(k))\) based on its definition in (16). Substituting for \(\varvec{{\lambda }}(k)\) according to Eq. (8), and using the fact that \({\mathbf {1}}^{\top }({\mathbf {v}}(k)+{\mathbf {u}}(k)) = 0\) for all \(k\), we have that

The complementary slackness condition (11) yields \(\mathrm{TCS}= \sum _{k=0}^{N-1} \varvec{{\mu }}(k)^{\top } {\mathbf {c}}\), which is clearly nonnegative. \(\square \)

### Proposition 2

\(\mathrm{SCS}= \sum _{i=1}^{n} \overline{\varvec{{\nu }}}_i^{\top } {\mathbf {b}}_i \ge 0\).

### Proof

We have that \(\mathrm{SCS}= \sum _{i=1}^{n} \varvec{{\lambda }}_i^{\top } {\mathbf {u}}_i\) based on its definition in (17). A direct substitution of the stationarity condition (10) and complementary slackness conditions (12)–(13) yields

which is clearly nonnegative. \(\square \)

The desired result follows from Propositions 1 and 2, as \(\mathrm{MS}= \mathrm{TCS}+ \mathrm{SCS}\).

### Proof of Lemma 2

Let \((V,U,{\varLambda })\) denote an efficient market equilibrium throughout the proof. Also, let \(\gamma (k)\) and \(\varvec{{\mu }}(k)\) denote the corresponding Lagrange multipliers associated with the power balance and line flow capacity constraints for each time period \(k=0, \dots , N-1\). The maximum rent achievable by any simultaneously feasible collection of transmission rights is given by the optimal value of

where \(\mathrm{{\Phi }}({{\mathcal {T}}}, {{\mathcal {F}}})= \sum _{k=0}^{N-1} -\varvec{{\lambda }}(k)^{\top } {\mathbf {t}}(k) \ + \ \varvec{{\mu }}(k)^{\top } {\mathbf {f}}(k)\). It is not difficult to show (by induction) that any feasible solution of problem (22)–(24) must satisfy \({\mathbf {q}}_i={\mathbf {0}}\) for \(i = 1, \dots , n\). This stems from the injection/extraction symmetry required by our definition of FTRs (which implies that \({\mathbf {1}}^{\top } {\mathbf {t}}(k) = 0\) for \(k= 0, \dots , N-1\)), and our assumption of zero initial stored energy. One can, therefore, equivalently reformulate problem (22)–(24) as

This is a convex optimization problem with linear constraints in the decision variables \({\mathbf {t}}(k)\in \mathbb {R}^n\) and \({\mathbf {f}}(k) \in \mathbb {R}^{2m}_+\) (\(k=0, \dots , N-1\)). It follows that a primal optimal solution is characterized by the existence of Lagrange multipliers \({\tilde{\gamma }}(k)\in \mathbb {R}\) and \(\tilde{\varvec{{\mu }}}(k) \in \mathbb {R}^{2m}_+\) (\(k=0, \dots , N-1)\) such that the KKT conditions (26)–(29) hold. The stationarity condition is given by:

The complementary slackness condition is given by:

Recall that \((V,U,{\varLambda })\) and \(\gamma (k), \varvec{{\mu }}(k)\) satisfy the KKT conditions (6), (8) and (11) associated with multi-period economic dispatch problem. It follows that a primal optimal solution to problem (25)–(26) is given by

This optimal solution yields a collection of transmission rights with an associated rent of

Upon examination of Eqs. (14)–(17), it is straightforward to verify that the right-hand side equals the \(\mathrm{TCS}\) associated with \((V,U,{\varLambda })\), thus completing the proof.

### Proof of Theorem 1

Let \((V,U,{\varLambda })\) denote an efficient market equilibrium, and let \(\gamma (k), \varvec{{\mu }}(k)\) (\(k=0, \dots , N-1\)) and \({\mathbf {{\overline{\nu }}}}_i, {\mathbf {{\underline{\nu }}}}_i\) (\(i =1, \dots , n\)) denote the corresponding Lagrange multipliers. The maximum rent achievable by any simultaneously feasible collection of transmission and storage rights is given by the optimal value of

where the objective function is given by

This is a convex optimization problem with linear constraints in the decision variables \({\mathbf {t}}(k), {\mathbf {f}}(k), {\mathbf {q}}(k), {\mathbf {s}}(k)\) (\(k=0, \dots , N-1\)) and \({\mathbf {e}}_i\) (\(i =1, \dots , n\)). Using arguments identical to those employed in the proof of Lemma 2, it is straightforward to verify that an optimal solution to problem (30)–(32) is given by

for all \(k= 0, \dots , N-1\) and \(i = 1, \dots , n\). This optimal solution yields a collection of transmission and storage rights with an associated rent of

According to (14), this equals the \(\mathrm{MS}\) associated with \((V,U,{\varLambda })\), thus completing the proof.

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### Cite this article

Muñoz-Álvarez, D., Bitar, E. Financial storage rights in electric power networks.
*J Regul Econ* **52**, 1–23 (2017). https://doi.org/10.1007/s11149-017-9327-9

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DOI: https://doi.org/10.1007/s11149-017-9327-9

### Keywords

- Energy storage
- Electricity markets
- Financial storage rights
- Financial transmission rights

### JEL Classification

- L52
- L98
- Q48
- D47