The Marginal Value of Networked Energy Storage

Abstract

We consider the problem of characterizing the locational marginal value of energy storage capacity in electric power networks with stochastic renewable supply and demand. The perspective taken is that of a system operator, whose objective is to minimize the expected cost of firm supply required to balance a stochastic net-demand process over a finite horizon, subject to transmission and energy storage constraints. The value of energy storage capacity is defined in terms of the optimal value of the corresponding constrained stochastic control problem. It is shown to be concave and non-decreasing in the vector of location-dependent storage capacities -implying that the greatest marginal value of storage is derived from initial investments in storage capacities. We also provide -as part of our main result -a characterization of said marginal value, which reveals its explicit dependency on a specific measure of nodal price variation. More generally, we derive an upper bound on the locational marginal value of energy storage capacity in terms of the total variation of the corresponding nodal price process, and provide conditions under which this bound is tight.

Marginal Value of Mobile Energy Storage in Power Network

“…| , and we define t e T e +1 := T + 1 and λ(T + 1) := 0. This result generalizes existing results on the locational marginal value of stationary storage [9], [10] by incorporating storage power constraints.…”

“…Several papers address the question of quantifying the marginal value of stationary storage. Bose and Bitar [9] develop an expression for locational marginal value of storage in a stochastic setting, i.e., when the system operator has to meet uncertain demand by dispatching generation and storage, and the marginal value is determined to be a function of the expected locational marginal prices (LMPs). Qin et al [10] derive an expression for the locational marginal value of stationary storage in terms of LMPs determined by the economic dispatch problem, and propose a discrete optimization framework for optimal siting from the system operator perspective.…”

On Carsharing Platforms With Electric Vehicles as Energy Service Providers

“…In our next result, we provide a closed-form expression for the optimal revenue J ⋆ (z 0 ). The proof followed relies on a dynamic programming argument, adapted from [56]. We use the notation y + = max{y, 0} for any scalar y in stating the result.…”