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Erschienen in: Energy Systems 1/2014

01.03.2014 | Original Paper

Assessing the value of storage in a future energy system with a high share of renewable electricity generation

An agent-based simulation approach with integrated optimization methods

verfasst von: Fabio Genoese, Massimo Genoese

Erschienen in: Energy Systems | Ausgabe 1/2014

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Abstract

Increasing the share of intermittent renewable electricity generation will require additional flexibility in the electricity system. While energy storage can provide such flexibility, studies about the economics of power storage often conclude that there is no business case for large-scale storage applications. In this paper, we present a new approach on how to assess the benefits of energy storage. Key improvements have been made in two areas: Firstly, the agent-based market simulation model PowerACE has been enhanced to make use of optimization methods (MILP) for the unit commitment of the agents, enabling us to quantify the economic benefit of flexibility at supply-agent level. Secondly, we have considerably extended the common unit commitment problem (Carrion and Arroyo, IEEE Trans Power Syst 21(3):1371–1378, 2006), so that we can now model the provision of positive and negative balancing power and the dispatch of storage units. We compare the flexibility offered by thermal power plants to that offered by storage units for the four major German electricity generating companies under two different scenarios. The results for 2030 indicate that it would be more profitable to build up to 4,800 MW storage capacity in the German market rather than investing in flexible combined cycle gas turbine plants or hard coal-fired units. The increasingly fluctuating residual load implies that inflexible power plants will be penalized. Using storage units, the power plants of an existing portfolio can be dispatched in a more efficient way, i.e. with less operation in part load and avoiding start-up or shutdown events.

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Fußnoten
1
Currently, the utilities E.ON, EnBW, RWE, Vattenfall and Evonik-Steag are modeled as individual agents. Aggregated agents are used to represent regional utilities and capacities of the industrial sector.
 
2
In a different development branch of the model (PowerACE Europe [26]), the regional scope is broader while the market actors and technical constraints of the unit commitment are modeled with less accuracy in order to keep the computing time of the model within reasonable limits. Also, the provision of balancing power is not modeled.
 
3
There is an exception to this rule: When the clearing price of an auction is below \(-150\) €/MWh, a second auction takes place where grid operators are requested to randomly set price limits between \(-\)150 and \(-\)300 €/MWh.
 
4
Alternatively, one could use the dual value (shadow price) of the LP approximation as the forecast market price as described in [15].
 
5
The focus of this analysis is to assess the value of storage units operated with hourly or daily cycles, e.g. pumped hydro storage. The modeling approach is less useful to measure the value of long-term storage technologies, e.g. hydrogen storage.
 
6
The values of the technical parameters used for this analysis are given in Table 5 in the appendix.
 
7
A description of the earlier model version is given in [10].
 
8
Compared to the common unit commitment problem presented in [2], the following equations or inequations have been extended or added: (4) and (12)–(31).
 
9
This equation is needed to address the problem of energy-limited generation and load of storage units.
 
10
For more information on Gurobi, please refer to http://​www.​gurobi.​com.
 
12
For a more detailed description, cp. to [14].
 
13
The technology option energy storage is not modeled in the investment module. Therefore, variational model runs are carried out analyzing the effect of storage being added to the portfolio or replacing investments in thermal units (cf. Sect. 4.2).
 
14
For these simulations, \(\xi \) was set to 5 years.
 
15
Grid congestion is not modeled, i.e. this figure only describes the amount of renewable electricity that could not be sold on the spot market.
 
16
Before changing the operator in the electricity demand constraint, we checked that the problem is feasible.
 
17
Interest rate: 10 %; depreciation period: 35a.
 
18
Provided that these investments are made and existing PHES units are not decommissioned, the total (German) storage capacity in 2030 would amount to 12,500 MW.
 
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Metadaten
Titel
Assessing the value of storage in a future energy system with a high share of renewable electricity generation
An agent-based simulation approach with integrated optimization methods
verfasst von
Fabio Genoese
Massimo Genoese
Publikationsdatum
01.03.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
Energy Systems / Ausgabe 1/2014
Print ISSN: 1868-3967
Elektronische ISSN: 1868-3975
DOI
https://doi.org/10.1007/s12667-013-0076-2

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