Merger Analysis in Wholesale Power Markets Using the Equilibria-Band Methodology

In this paper, three single-stage stochastic programs are proposed and compared for optimal dispatch by a System Operator (SO) into balancing markets (BM). The motivation for the models is to represent a possible requirement to undertake system balancing with increasing amounts of intermittent renewable generation. The proposed optimization models are reformulated as tractable Mixed Integer Linear Programs (MILPs) and these consider both fuel cost and intermittency cost of the generators, when the SO activates the up-or down-regulation bids. These three models are based on the main approaches seen in practice: dual-imbalance pricing, single imbalance pricing and single imbalance pricing with spot reversion. A scenariogeneration algorithm based on predictive conditional dynamic density distributions is also proposed. We perform a comparative analysis of these three proposed models in terms of how they help the SO to optimize their balancing market actions considering intermittent-renewable generators. The single imbalance pricing is found to be the most market efficient.

show abstract

“…Using change of variable I dn i,w = c ω φ dn i in (14), the non-linear term can be linearized by implementing (15) [36]:…”

Section: Minimizementioning

confidence: 99%

Optimal Dispatch in a Balancing Market With Intermittent Renewable Generation

Shinde

Hesamzadeh

Date

et al. 2021

IEEE Trans. Power Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Solving all producers' MILCs together gives us the Nash equilibrium of the one-stage game. Our model may have multiple Nash equilibria, to tackle this, we employ the worst Nash equilibrium (WNE) concept where we search the Nash equilibrium which has the worst (highest) dispatch cost [26] and [39]. The WNE problem is formulated in (9).…”

Section: Minimizementioning

confidence: 99%

“…The one-stage game is formulated as a one-stage MILP model. Our one-stage MILP model is related to [26], [27], [28] and [29]. Based on our three MILP models, we analyze the imperfect competition and the inc-dec game for each congestion management method and we compare the production efficiencies and other aspects of nodal and zonal pricing.…”

Section: Introductionmentioning

confidence: 99%

Production Efficiency of Nodal and Zonal Pricing in Imperfectly Competitive Electricity Markets

Sarfati¹,

Hesamzadeh

Holmberg

2019

SSRN Journal

Self Cite

View full text Add to dashboard Cite

Electricity markets employ different congestion management methods to handle the limited transmission capacity of the power system. This paper compares production efficiency and other aspects of nodal and zonal pricing. We consider two types of zonal pricing: zonal pricing with Available Transmission Capacity (ATC) and zonal pricing with Flow-Based Market Coupling (FBMC).We develop a mathematical model to study the imperfect competition under zonal pricing with FBMC. Zonal pricing with FBMC is employed in two stages, a day-ahead market stage and a re-dispatch stage. We show that the optimality conditions and market clearing conditions can be reformulated as a mixed integer linear program (MILP), which is straightforward to implement. Zonal pricing with ATC and nodal pricing is used as our benchmarks. The imperfect competition under zonal pricing with ATC and nodal pricing are also formulated as MILP models. All MILP models are demonstrated on 6-node and the modified IEEE 24-node systems. Our numerical results show that the zonal pricing with ATC results in large production inefficiencies due to the incdec-game. Improving the representation of the transmission network as in the zonal pricing with FBMC mitigates the inc-dec game. Cambridge Working Papers in Economics Faculty of Economics www.eprg.group.cam.ac.uk

show abstract

“…2) Interaction of SGPs: The Extremal-Nash Equilibrium (ENE) concept is employed in this paper to model the system market power, [46], [47]. The ENE formulation is a twolevel optimization problem.…”

Section: A Efficient Planning Of Generation and Transmissionmentioning

confidence: 99%