Abstract
Liberalized electricity markets promise a cost-efficient operation and expansion of power systems but may as well introduce opportunities for strategic gaming for price-making agents. Given the rapid transition of today’s energy systems, unconventional generation and consumption patterns are emerging, presenting new challenges for regulators and policymakers to prevent strategic behavior. The strategic offering of various price-making agents in oligopolistic electricity markets resembles a multi-leader-common-follower game. The decision problem of each agent can be modeled as a bi-level optimization problem, consisting of the strategic agent’s decision problem at the upper-level, and the market clearing at the lower-level. When modeling a multi-leader game, i.e., a set of bi-level optimization problems, the resulting equilibrium problem with equilibrium constraints poses several challenges. Real-life applicability or policy-oriented studies are challenged by the potential multiplicity of equilibria and the difficulty of exhaustively exploring this range of equilibria. In this paper, the range of equilibria is explored by using a novel simultaneous solution method. The proposed solution technique relies on applying Scholtes’ regularization before concatenating the strategic actor’s decision problems’ optimality conditions. Hence, the attained solutions are stationary points with high confidence. In a stylized example, different strategic agents, including an energy storage system, are modeled to capture the asymmetric opportunities they may face when exercising market power. Our analysis reveals that these models’ outcomes may span a broad range, impacting the derived economic metrics significantly.
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Notes
The primal and dual reformulations, when applied on this problem type, results in as many bi-linear terms in the SDC as primal decision variables the upper-level agent has. Whereas KKT conditions would introduce as many CS constraints, i.e., bi-linear terms, as primal inequality constraints are included in the lower-level problem.
We remark that the framework allows for any convex LL formulation, such as quadratic (Dorn 1960) or second-order cone programs.
A too-small choice may lead to an ill-conditioned problem; on the other hand, an unnecessarily large value could result in constraint violations and fairly optimistic objective values.
The model formulation for MPPDCs differs in the validation algorithm. As opposed to the simultaneously solved NCP model, in the validation loop complex bidding (De Vivero-Serrano et al. 2019) is used. This implies that the energy content related constraints are part of the LL problem instead of the UL one. As such, we avoid rejecting candidate equilibria because of the asymmetric view on these constraints.
Abbreviations
- \(i \in \mathcal {I}\) :
-
Set of conventional generators
- \(k \in \mathcal {K}\) :
-
Set of renewable generators
- \(l \in \mathcal {L}\) :
-
Set of energy storage systems
- \(t \in \mathcal {T}\) :
-
Set of time steps
- \(\eta ^{ch}, \eta ^{dch}\) :
-
Charging and discharging efficiency (–)
- \({\overline{D}_t}\) :
-
Maximum demand (MWh)
- \({c^{D}_t}\) :
-
Willingness-to-pay of the demand (k€/MWh)
- \({E^{ESS,max}}\) :
-
Energy capacity of the ESS (MWh)
- \({OPEX^{G}_{i}}\) :
-
Operational expenditure of the CG (k€/MWh)
- \({p^{cap}}\) :
-
Price cap in the day-ahead market (k€/MWh)
- \({P^{ESS,max}}\) :
-
Power capacity of the ESS (MW)
- \({p^{floor}}\) :
-
Price floor in the day-ahead market (k€/MWh)
- \({P^{G,max}_{i}}\) :
-
Power capacity of the CG (MW)
- \({P^{R,forecast}_{k,t}}\) :
-
Renewable production forecast (MWh)
- \({\overline{CH}_{t}}\) :
-
Quantity bid of the ESS for charging (MWh)
- \({\overline{DCH}_{t}}\) :
-
Quantity bid of the ESS for discharging (MWh)
- \({\overline{G}_{i,t}}\) :
-
Quantity bid of the CG (MWh)
- \({\overline{W}_{k,t}}\) :
-
Quantity bid of the RG (MWh)
- \({c^{CH}_t}\) :
-
Price bid of the ESS for charging (k€/MWh)
- \({c^{DCH}_t}\) :
-
Price bid of the ESS for discharging (k€/MWh)
- \({c^{G}_{i,t}}\) :
-
Price bid of the CG (k€/MWh)
- \({c^{W}_{k,t}}\) :
-
Price bid of the RG (k€/MWh)
- \({ch_{t}}\) :
-
Dispatched charging of the ESS (MWh)
- \({d_{t}}\) :
-
Cleared demand (MWh)
- \({dch_{t}}\) :
-
Dispatched discharging of the ESS (MWh)
- \({g_{i,t}}\) :
-
Dispatched quantity for the CG (MWh)
- \({SoC_{t}}\) :
-
State-of-charge variable of the ESS (MWh)
- \({w_{k,t}}\) :
-
Dispatched quantity for the RG (MWh)
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Acknowledgements
M. Dolanyi’s work has been funded by the University of Leuven’s C2 Research Project C24/16/018 entitled “Energy Storage as a Disruptive Technology in the Energy System of the Future”. K. Bruninx is a post-doctoral research fellow of the Flanders Research Foundation (FWO) (Grant No. 12J3320N) at the University of Leuven (KU Leuven) Energy Institute, TME Branch (Energy Conversion), B-3001 Leuven, Belgium) and EnergyVille, B-3600 Genk, Belgium. The authors gratefully acknowledge the constructive comments of Anthony Papavasiliou (CORE, Université Catholique de Louvain), Salvador Pineda (University of Málaga), and Panagiotis Patrinos (ESAT, KU Leuven).
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Appendices
Appendix A: Single-level reformulation of ESS problem
We present the required steps to transform the bi-level formulation of the ESS owner problem (i) into the single-level (MPPDC) equivalent by the primal-dual optimality conditions, (ii) into a nonlinear complementarity problem by the KKT optimality conditions of the MPPDC form.
1.1 A.1 MPPDC ESS
The obtained single-level problem, after applying primal-dual reformulation on the LL Problem (7):
The resulting constraints from the LL reformulated via duality:
where \(\epsilon _{1}\) is the first-stage regularization parameter for the nonlinear, non-convex strong duality constraint.
1.2 A.2 Linearization of the ESS’ objective
To linearize the initially bi-linear objective function following the method of Ruiz and Conejo (2009), we make use of the SDC equation (A.1y), the stationary conditions Eqs. A.1q–A.1r and the complementary slackness (CS) constraints of the LL. As the LL problem is convex, we know that the following CS conditions hold:
Furthermore multiplying Eqs. A.1q–A.1r by \(dch_{l,t}\) and \(ch_{l,t}\) gives:
After substituting terms of Eq. A.2 in Eq. A.3:
And lastly, to get the linearized equivalent of \(\mathcal {F}_{ESS}^{l}\), we substitute Eq. A.4a in the SDC (Eq. A.1y):
We refer to the RHS of (A.5) as \(\mathcal {D}_{ESS}^{l}\). Thus MPPDC-ESS with linearized objective function constitutes the objective function (A.5) and Constraints (A.1b–A.1y).
1.3 A.3 SCR-NCP, ESS
We recast MPPDC-ESS via its KKT conditions. This results in a nonlinear complementarity problem (NCP). After adding the regularization term \(\epsilon _{2}\) as shown in Section (3.2), we obtain the SCR-NCP formulation: Primal feasibility:
Dual feasibility:
All dual variables belonging to the inequality constraints of Eq. A.1b-A.1y are greater than or equal to 0.
Complementary slackness: Lower-level
Upper-level
Appendix B: Central planner objectives
The formulation of the three different CP objectives are listed below.
Competitive:
Collusive:
Favoring ESS:
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Dolányi, M., Bruninx, K. & Delarue, E. Triggering a variety of Nash-equilibria in oligopolistic electricity markets. Optim Eng (2023). https://doi.org/10.1007/s11081-023-09866-0
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DOI: https://doi.org/10.1007/s11081-023-09866-0