Reserve market scheduling considering both capacity and energy bids of reserve

Bahmanzadeh, Majid; Foroud, Asghar Akbari

doi:10.1016/j.ijepes.2016.01.054

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…In (16), ξ is value-at-risk (VaR) which is given by (1 − β) times the marginal price distribution, p kt is the probability distribution of the marginal prices variable, λ kt is the marginal prices for the contingency k and time interval t. In (17) and (18), δ kt is the difference between VaR and the marginal prices.…”

Section: Cvar As An Optimal Placement Of Wind Farms In An Uncertain Cmentioning

confidence: 99%

“…The reserve market model is therefore important for maximising the profit of microgrid [15][16][17] or optimising the cost of virtual power plant [18] by submitting their bids to independent system operator (ISO) in the ancillary services market. The uncertainties considered for the microgrid or virtual power plant in the reserve scheduling are the wind power and demand.…”

Section: Introductionmentioning

confidence: 99%

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Risk assessment by security‐constrained unit commitment for hybrid wind‐thermal by pair copula approach in reserve power market: a stochastic approach

Panda

Tiwari

2020

IET gener. transm. distrib.

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High volatility renewable penetration increases the security risk of the market participants. In order to ensure the security of the reserve market under uncertainties, the authors have proposed a novel optimal bidding strategy based on bivariate pair copula-based security-constrained unit commitment economic dispatch (SCUCED) for the generation companies (GENCOs) while considering the volatility of wind power generations and load variations under different (N − k) contingencies in ancillary service market. The expected profit of GENCOs are based on the ramp-up and ramp-down optimal reserve curves in each time interval are submitted to the independent system operator. The profit of wind farms is obtained and the risks due to uncertainties with wind farms are also analysed while participating in the reserve market. Conditional value-at-risk is considered for the optimal placement of wind farms in the network. The risk-based SCUCED model is solved by mixed integer linear programming. Moreover, a factor called joint Pareto tail factor has been also developed to provide information regarding the fat tails in the lower end of the bivariate marginal distribution of different GENCOs. The effectiveness of the proposed approach has been tested on a modified IEEE 30 bus system.

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Section: Cvar As An Optimal Placement Of Wind Farms In An Uncertain Cmentioning

confidence: 99%