Abstract
Energy storage systems can improve the uncertainty and variability related to renewable energy sources such as wind and solar create in power systems. Aside from applications such as frequency regulation, time-based arbitrage, or the provision of the reserve, where the placement of storage devices is not particularly significant, distributed storage could also be used to improve congestions in the distribution networks. In such cases, the optimal placement of this distributed storage is vital for making a cost-effective investment. Furthermore, the now reached massive spread of distributed renewable energy resources in distribution systems, intrinsically uncertain and non-programmable, together with the new trends in the electric demand, often unpredictable, require a paradigm change in grid planning for properly lead with the uncertainty sources and the distribution system operators (DSO) should learn to support such change. This paper considers the DSO perspective by proposing a methodology for energy storage placement in the distribution networks in which robust optimization accommodates system uncertainty. The proposed method calls for the use of a multi-period convex AC-optimal power flow (AC-OPF), ensuring a reliable planning solution. Wind, photovoltaic (PV), and load uncertainties are modeled as symmetric and bounded variables with the flexibility to modulate the robustness of the model. A case study based on real distribution network information allows the illustration and discussion of the properties of the model. An important observation is that the method enables the system operator to integrate energy storage devices by fine-tuning the level of robustness it willing to consider, and that is incremental with the level of protection. However, the algorithm grows more complex as the system robustness increases and, thus, it requires higher computational effort.
Highlights
The share of renewable power generation in the global electricity generation is anticipated to expand from today’s 23% to levels between 30%–45% by 2030 [1]
The paper proposes an application of the Robust Optimization (RO) to solve the energy storage system (ESS) optimal location problem in distribution networks operated by a distribution system operators (DSO)
The ESS OPEX is not considered in the optimization. According to this point of view, it is supposed that the minimization of the network operational cost, in terms of reduction of the curtailed power from renewable energy sources (RES) and to loads, that would be necessary to relieve contingencies, represents the only incomes that allow DSO to pay back EES CAPEX
Summary
The share of renewable power generation in the global electricity generation is anticipated to expand from today’s 23% to levels between 30%–45% by 2030 [1]. The optimal operation studies of ESS consider that energy and power ratings of a storage unit are given, the purpose of these studies is to identify operation strategies to optimize the exploitation of resources able of contributing to network support at minimum cost. The research on the topic has been dramatically increasing in the last two years since ESS are crucial for the energy transition towards the carbon free world, but there is still room for new contributions, on dealing with the uncertainties modeling For these reasons, the paper proposes an application of the Robust Optimization (RO) to solve the ESS optimal location problem in distribution networks operated by a DSO. In the nomenclature of the symbols used in the mathematical formulation has been reported
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
