Integration of Wind Power Generation into a Double- sided Competitive Electricity Market For Profit and Social Welfare Maximization

Bouddou, Riyadh; Benhamida, Farid; Zeggai, Amine; Belgacem, Moussa; Khatir, Mohamed

doi:10.1109/iceit48248.2020.9113173

Cited by 1 publication

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“…The level of wind energy penetration is computed according to the maximum wind energy (Pw = 64.865 MW) injected in the system with wind speed variation and estimated with 4 levels: 25 %, 50 %, 75%, and 100% Pw [30], as given in Table 6. The wind generator's cost coefficient is calculated from the equivalent coefficient of the conventional unit, with 37.55% [31,32]. The bid-based dispatch was carried out for each wind speed, considering the discussed constraints in Eq.…”

Section: Case 2: Bbded With Wind Energy Integrationmentioning

confidence: 99%

Simulated Annealing Algorithm for Dynamic Economic Dispatch Problem in the Electricity Market Incorporating Wind Energy

Bouddou¹,

Benhamida²,

Haba³

et al. 2020

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In this paper, a bid-based dynamic economic dispatch (BBDED) problem is solved in the electricity market system under bidding strategies, including wind energy penetration using simulated annealing (SA) algorithm. The multi-objective dispatch model allows generating companies (GENCOs) and their customers to submit supply and demand bids to a market controller known as the independent system operator (ISO) and follow a bidding strategy. ISO is responsible for the market clearing and settlement to maximize the social profit and benefit for GENCOs and customers during trading periods. To study the effect and advantages of wind energy integration in the BBDED problem, the wind energy generation is computed using the forecasted wind speeds and included in the dispatch model. In this regard, the ISO's dispatch model is formulated as a bilevel nonlinear optimization problem. The higher-level is solving the market-clearing with and without wind energy, and the lower level is maximizing GENCO's social profit. The proposed SA algorithm is evaluated for optimality, convergence, robustness, and computational efficiency tested on an IEEE 30-bus test system. The simulation results are compared with those found using different algorithm-based approaches, considering various constraints like power balancing, generator limits, ramp rate limits, and transmission losses.

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Section: Case 2: Bbded With Wind Energy Integrationmentioning

confidence: 99%