Abstract
Market power is a dominant feature of many modern electricity markets with an oligopolistic structure, resulting in increased consumer cost. This work investigates how consumers, through demand response (DR), can mitigate against market power. Within DR, our analysis particularly focusses on the impacts of load shifting and self-generation. A stochastic mixed complementarity problem is presented to model an electricity market characterised by an oligopoly with a competitive fringe. It incorporates both energy and capacity markets, multiple generating firms and different consumer types. The model is applied to a case study based on data for the Irish power system. The results demonstrate how DR can help consumers mitigate against the negative effects of market power and that load shifting and self-generation are competing technologies, whose effectiveness against market power is similar for most consumers. We also find that DR does not necessarily reduce emissions in the presence of market power.
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Notes
There are many types of renewable support schemes. In this work, we consider a constant feed-in premium, i.e. when generators use renewable sources to generate electricity, they receive the market price plus a fixed premium (per MWh). We refer the reader to Devine et al. (2017) for a detailed discussion on different renewable support schemes.
The derating factor in this work reflects the proportion of its overall capacity a technology can provide to meet the capacity target.
In an Irish context, these efficiencies may lead to existing baseload generation having higher variable power generation costs than existing mid-merit generators.
We acknowledge that the exact quantitative results would differ when assuming different (higher) fuel prices. The qualitative findings, however, would largely remain the same. A quantitative sensitivity analysis for fuel prices would go beyond the scope of the paper but could be part of future research specifically focusing on the effects of fuel prices during periods with price shocks.
All values for \(\mathrm{NORM}^{\text {G}}_{f,t,p,s}\) and \(\mathrm{NORM}^{\text {PV}}_{p,s}\) are provided in the online appendix: https://figshare.com/articles/dataset/inputs_Devine_Bertsch_2022/19947842. The values for \(\mathrm{NORM}^{\text {G}}_{f,t,p,s}\) are split into three different wind regions in Ireland - see Bertsch et al. (2018) for details.
Note that these consumer costs only include the directly market-related costs, i.e. they do not incorporate the levies end consumers have to pay resulting from feed-in premium payments to renewable generators.
In addition to costs calculated via Eq. (5a), the costs in Fig. 9 also account for costs consumers would have to pay in order to facilitate capacity payments. We assume capacity payment costs are spread proportionally amongst consumers according to their peak demand. Accounting for capacity payment costs is in contrast to the rest of the paper where consumer costs are solely calculated via Eq. (5a) as capacity payments costs are not relevant.
Note that constraint (5i) is only binding in the cases with market power and in these cases, it is only ever binding for consumer groups 2 and 5, i.e. those consumers with micro generation.
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Acknowledgements
Devine acknowledges funding from Science Foundation Ireland (SFI) under the SFI Strategic Partnership Programme Grant number SFI/15/SPP/E3125. The opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Science Foundation Ireland. Bertsch acknowledges funding from the ESRI’s Energy Policy Research Center. All omissions and errors are our own.
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A. Karush-Kuhn-Tucker conditions
A. Karush-Kuhn-Tucker conditions
This appendix presents the Karush-Kuhn-Tucker (KKT) conditions for optimality for the two types of players modelled in this work. These conditions, along with the market clearing conditions (8), make up the mixed complementarity problem. The “perp” notation \(0 \le a \perp b \ge 0\) is equivalent to \(a\ge 0\), \(b\ge 0\) and \(a.b=0\).
1.1 A.1 Firms’ KKT conditions
The firms’ KKT conditions are
If firm f is a price-making firm, then it’s optimal level of generation for technology t, using equation (10a), is
1.2 A.2 Consumers’ KKT conditions
The consumers’ KKT conditions are
where
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Devine, M.T., Bertsch, V. The role of demand response in mitigating market power: a quantitative analysis using a stochastic market equilibrium model. OR Spectrum 45, 555–597 (2023). https://doi.org/10.1007/s00291-022-00700-0
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DOI: https://doi.org/10.1007/s00291-022-00700-0