Optimizing Incentive Plans for Renewable Energy Growth in the Electricity Market

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Pac A. B., ŞEKER E. Z.

IEEE Access, vol.12, pp.38830-38848, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 12
  • Publication Date: 2024
  • Doi Number: 10.1109/access.2024.3374337
  • Journal Name: IEEE Access
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Page Numbers: pp.38830-38848
  • Keywords: Generation expansion planning, incentive policies, mixed-integer programming, multicriteria, Pareto optimal, renewable energy
  • Yıldız Technical University Affiliated: Yes


The competitive edge of renewable energy depends on financial support from central planners. An effective intervention with reasonable burden on taxpayers requires anticipating the choice of profit maximizing investors regarding capacity installation and electricity generation from certain locations for solar, wind and fossil-based power plants in response to technology, cost, price and incentive parameters. A 10-year generation expansion planning horizon is favored, during which capacity factors, cost projections, and electricity prices remain reasonably predictable. Investment costs within the horizon are accounted for using a depreciation model. Scenarios are considered for technology, costs, demand, wholesale prices and depreciation rates for investigating outcomes of intervention by investment subsidies and generation incentives. A mixed-integer model is devised for optimal investor decisions. Pareto analysis is conducted for each scenario setting over the optimal solutions at different incentive and subsidy rates for wind and solar plants considering three criteria: cost of intervention, renewable shares in installed capacity and overall energy generation. Under a moderate scenario, sharing 20% of the commissioning and operation costs, the central planner elicits nearly 30% increase in the shares of renewable plants in installed capacity to 72%, and electricity generation to 80%. An overall optimistic scenario achieves 75% renewables with similar interventions, while an overall pessimistic scenario attains 60%. Most of this variability is accountable to the depreciation scheme, scenarios on renewable technology and cost are partially effective, while fluctuations in demand, wholesale prices, technology and cost of the natural gas alternative are shown to have negligible impact on outcomes of the intervention.