Assessing the market power due to the network constraints in competitive electricity markets

Bompard, Ettore; Ma, Yuanlin; Napoli, Roberto; Jiang, Chongying

doi:10.1016/j.epsr.2005.12.004

Cited by 16 publications

(7 citation statements)

References 11 publications

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“…However, the transmission congestion in case H motivates some high-cost GenCos to participate in the market. As shown in Table 4, the NWSR i values of GenCos at buses 1,4,6,12,24,32,34,36,40 and 116 are increased in case H. Due to the limited generation of GenCo at bus 10, NWSR 10 decreases from 0.2121 to 0. In addition, DWI of the market in cases G and H is 0.3311 and 0.2238, respectively.…”

Section: Ieee 118-bus Test Systemmentioning

confidence: 94%

“…The concept of must-run generation (MRG) is introduced in [3]; it also proposes nodal must-run share (NMRS) and expected NMRS are proposed to assess the geographical difference of market power and the impact of random failures on nodal market power, respectively. In [4], two sets of indices are proposed: (a) locational privilege (LP) indices that take into account the effect of GenCos positioning in the network on their surplus considering perfect competition model and (b) network market power indices that assess the strategic behaviours of GenCos that may take advantage of transmission congestion. Market inefficiency index, producer surplus deviation index, and consumer surplus deviation index are proposed in [5]t o assess the total surplus distribution between the supply and demand sides in oligopoly and perfectly competitive markets.…”

Section: Introductionmentioning

confidence: 99%

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Capacity withholding analysis in transmission‐constrained electricity markets

Salarkheili

Nazar

2016

IET Generation, Transmission & Distribution

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Section: Ieee 118-bus Test Systemmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Capacity withholding analysis in transmission‐constrained electricity markets

Salarkheili

Nazar

2016

IET Generation, Transmission & Distribution

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“…[30] Capacity pricing mechanism [31][32][33][34] Ancillary service pricing mechanism [35] Combined clearing of power energy and ancillary service [36] Distributed trading mechanism [37] Market structure design [38] Market power Evaluating metrics [19,[39][40][41][42][43][44][45][46] Evaluating methods [47][48][49][50] Demand response [51][52][53][54] Market efficiency assessment [19] Investment in power systems [55,56] Environmental policy [57][58][59] Pricing mechanisms are the core of market operation design. A lot of practice and research on the applicability and effect of pricing mechanisms has been carried out by the industrial sector and in academia.…”

Section: Regional Marginal Pricing [29] Locational Marginal Pricingmentioning

confidence: 99%

Key Issues and Technical Applications in the Study of Power Markets as the System Adapts to the New Power System in China

et al. 2021

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To reach the “30·60” decarbonization target (where carbon emissions start declining in 2030 and reach net zero in 2060), China is restructuring its power system to a new energy-based one. Given this new situation, this paper reviews previous studies on the power market and highlights key issues for future research as we seek to adapt to the new power system (NPS). Based on a systematic literature review, papers on the operational efficiency of the power market, participants’ bidding strategies and market supervision were identified. In a further step, papers with high relevance were analyzed in more detail. Then, key studies that focused on market trading under China’s new power system were picked out for further discussion. New studies were searched for that pertained to new energy mechanisms and bidding, the transition from coal-fired power, flexible resources and the technical applications of simulations. The quantitative analysis supports the construction of a basic paradigm for the study of power markets that is suitable for the new power system. Finally, the theoretical basis and application suggestions for power market simulations are introduced. This study summarized the existing research on the power market and further explored the key issues relating to the power market as it adapts to the NPS, hoping to inspire better research into China’s power sector, and promote safe, low-carbon, and sustainable development in China’s power industry.

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“…A coevolutionary computation approach was used in Ladjici and Boudour (2008) for finding the nash-cournot equilibrium of the oligopolistic electricity market under transmission constraints. In Bompard et al (2006), the effect of transmission constraints on the Genco's market power was estimated using two different indices. A hierarchical bidding process with congestion management was proposed in a game theoretic framework in Peng and Tomsovic (2003).…”

Section: Evolutionary Neural Networkmentioning

confidence: 99%

Evolutionary neural networks for strategic bidding in electricity markets

Hota

Bajpai

Pratihar

2012

International Journal of Energy Sector Management

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Purpose -The purpose of this paper is to introduce a neural network-based market agent, which develops optimal bidding strategies for a power generating company (Genco) in a day-ahead electricity market. Design/methodology/approach -The problem of finding optimal bidding strategy for a Genco is formulated as a two-level optimization problem. At the top level, the Genco aims at maximizing its total daily profit, and at the bottom level, the independent system operator obtains the power dispatch quantity for each market participant with the objective of maximizing the social welfare. The neural network is trained using a particle swarm optimization (PSO) algorithm with the objective of maximizing daily profit for the Genco. Findings -The effectiveness of the proposed approach is established through several case studies on the benchmark IEEE 30-bus test system for the day-ahead market, with an hourly clearing mechanism and dynamically changing demand profile. Both block bidding and linear supply function bidding are considered for the Gencos and the variation of optimal bidding strategy with the change in demand is investigated. The performance is also evaluated in the context of the Brazilian electricity market with real market data and compared with the other methods reported in the literature. Practical implications -Strategic bidding is a peculiar phenomenon observed in an oligopolistic electricity market and has several implications on policy making and mechanism design. In this work, the transmission line constraints and demand side bidding are taken into account for a more realistic simulation. Originality/value -To the best of the authors' knowledge, this paper has introduced, for the first time, a neural network-based market agent to develop optimal bidding strategies of a Genco in an electricity market. Simulation results obtained from the IEEE 30-bus test system and the Brazilian electricity market demonstrate the superiority of the proposed approach, as compared to the conventional PSO-based method and the genetic fuzzy rule-based system approach, respectively.

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Assessing the market power due to the network constraints in competitive electricity markets

Cited by 16 publications

References 11 publications

Capacity withholding analysis in transmission‐constrained electricity markets

Capacity withholding analysis in transmission‐constrained electricity markets

Key Issues and Technical Applications in the Study of Power Markets as the System Adapts to the New Power System in China

Evolutionary neural networks for strategic bidding in electricity markets

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