Sizing and Siting of Large-Scale Batteries in Transmission Grids to Optimize the Use of Renewables

Abstract

Power systems are a recent field of application of\ud Complex Network research, which allows to perform large scale\ud studies and evaluations. Based on this theory, a power grid is\ud modeled as a weighted graph with several kinds of nodes and\ud edges, and further analysis can help in investigating the behaviour\ud of the grid under critical conditions. Among the crucial aspects\ud of a power network, those concerning flow limits and power\ud flow distribution are gaining relevance due to the increasing\ud introduction of large scale renewable energy generation facilities.\ud Storage systems are a key element in having a more sustainable\ud but still reliable grid. This paper focuses on a new research\ud challenge regarding the siting of the storage on the transmission\ud grid and its appropriate sizing. The problem is tackled by\ud considering realistic configurations based on the IEEE-RTS-96\ud bus and data coming from the Italian transmission operator, and\ud evaluating novel economic and Complex Network based metrics\ud on these configurations. Power flows are modeled in a linear\ud way and the representative optimization problem is expressed as\ud a linear programming problem. The results show the potential\ud benefits of storage in transmission lines and indicate that the\ud siting has a minor role in the optimal operation of the system

Topological Considerations on the Use of Batteries to Enhance the Reliability of HV-Grids

“…With a similar aim, both ESS and transmission expansion are jointly investigated as possible technologies in [8], where the proposed MILP problem is applied to two test systems, and in [12], where a cost/benefit analysis is conducted for Central Western Europe and Nord Pool grids. In [28], we perform an investigation of how proper sizing (and, to a smaller extent, siting) of large-scale batteries can enable a smarter use of the transmission grid with a high share of renewable, assuming a deterministic framework. Optimal sizing and siting problems are jointly investigated with a three-stage approach in [29].…”

“…The study concludes that co-locating storage and wind farms enhances the performance of wind integration. However, different conclusions are drawn in [23,22,30,28], where the storage is found to be optimally located in buses other than close to renewable generators, mainly due to network congestions.…”

“…The forecast operation defines which units are committed to producing and at what level of power, for each hour of the following day. At this stage, load profiles are assumed to be deterministically known, their trends and peak-values are derived from the Italian TSO database [44,28]. The wind speed is set equal to the seasonal average for the whole day, whereas the solar production is estimated assuming clear sky conditions.…”

Optimum Capacity and Placement of Storage Batteries Considering Photovoltaics

“…References [22,23] introduce various situations, optimum operation of storage batteries in various types of storage batteries, and methods of determining the size of the storage battery. In addition, these papers have sized the storage battery by linear problem [24,25].…”

Remedial Actions for Security Constraint Management of Overstressed Power Systems

“…In accordance with the prevailing industrial terminology, SCM can be further divided into congestion management (CM) and volt-var control (VVC). There has been much previous work on CM and VVC based on the use of generation rescheduling and load shedding [7]- [10], distributed generation [11]- [12], FACTS devices [13]- [15], demand side management [16]- [17], energy storage [18], and transmission switching [19]- [21]. Earlier studies were mostly concerned with contingencies that will push the system into an alert state and paid less attention to system emergency state, where, if adequate control actions are not promptly implemented, changes in power flows and energy balance will result in the inability of conventional algorithms to converge.…”