PowerGAMA: A new simplified modelling approach for analyses of large interconnected power systems, applied to a 2030 Western Mediterranean case study

Abstract

This paper describes a modelling approach suitable for assessments of future scenarios for renewable energy integration in large and interconnected power systems, based on sequential optimal power flow computations that take into account variability in power consumption, in renewable power production, energy storage, and flexible demand. The approach and the implementation as an open source Python package called Power Grid And Market Analysis is described in some detail. Particular emphasis is put on the modelling of energy storage systems, and the use of storage values as a means to define storage utilisation strategies. A case study representing a 2030 scenario for the Western Mediterranean region is then analysed using this approach. The main aim of this study is to assess the benefit for the system of adding flexibility in terms of storage associated with concentrated solar power or flexible demand. But other results are also presented, such as the resulting energy mix, generation costs, price variations, and grid congestion.

A Mechanism for Allocating Benefits and Costs from Transmission Interconnections under Cooperation: A Case Study of the North Sea Offshore Grid

“…We use a planning model based on previous work by Trötscher and Korpås (2011), Munoz et al (2014) and Svendsen and Spro (2016) which has been customized for offshore grid applications (Kristiansen et al, 2017(Kristiansen et al, , 2018. To this end, we only provide a high-level overview of its most relevant features supplemented with a detailed description of all variables, parameters, constraints, and its objective function in the Online Appendix.…”

PyPSA: Python for Power System Analysis

“…The consideration of multiple time periods is important on the operational side for unit commitment of conventional generators and the optimisation of storage and demand side management, and on the investment side for optimising infrastructure capacities over representative load and weather situations. Several tools have subsets of these capabilities, such as calliope [11], minpower [12], MOST [13], oemof [14], OSeMOSYS [15], PLEXOS [16], PowerGAMA [17], PRIMES [18], TIMES [19] and urbs [20], but their representations of electrical grids are often simplified.…”

Optimal Sizing and Spatial Allocation of Storage Units in a High-Resolution Power System Model

“…The consideration of the power grid allows for a simulation or optimization of flexibility not only to shift power according to a residual load, but also make use of the interconnection of different regions. While certain studies [4,6,13] only take net-transfer capacities between countries into account, recent works [14][15][16] try to include-partly for several countries-the extra-high voltage grid. This paradigm shift and its implication have been analyzed by [17], who concludes that clustering methods are necessary when working with a high spatial resolution in a comparably big network, such as Europe.…”