Generation Expansion Planning With Large Amounts of Wind Power via Decision-Dependent Stochastic Programming

Abstract

Abstract-Power generation expansion planning needs to deal with future uncertainties carefully, given that the invested generation assets will be in operation for a long time. Many stochastic programming models have been proposed to tackle this challenge. However, most previous works assume predetermined future uncertainties (i.e., fixed random outcomes with given probabilities). In several recent studies of generation assets' planning (e.g., thermal versus renewable), new findings show that the investment decisions could affect the future uncertainties as well. To this end, this paper proposes a multistage, decisiondependent stochastic optimization model for long-term, largescale generation expansion planning where large amounts of wind power are involved. In the decision-dependent model, the future uncertainties are not only affecting but also affected by the current decisions. In particular, the probability distribution function is determined by not only input parameters but also decision variables. To deal with the nonlinear constraints in our model, a quasi-exact solution approach is then introduced to reformulate the multistage stochastic investment model to a mixed-integer linear programming (MILP) model. The wind penetration, investment decisions, and the optimality of the decisiondependent model are evaluated in a series of multistage case studies. The results show that the proposed decision-dependent model provides effective optimization solutions for long-term generation expansion planning.

Cost minimization of large-scale infrastructure for electricity generation and transmission

“…, N is parameterized based on the average load or the peak load, since the analysis can be easily repeated with different values. Hence, we follow earlier literature (Billionnet et al, 2016;Zhan et al, 2017) and demonstrate our EGTP based on the (averaged) historic grid load.…”

“…For instance, there are works that study operational issues such as dispatching (Zugno & Conejo, 2015) or valuation problems (Ernstsen & Boomsma, 2018), including the effects of different support schemes (Boomsma et al, 2012). Other research also considers uncertain electricity prices (Zhan et al, 2017) and contributes equilibrium approaches as tailored expansion strategies for renewable generation (Pineda et al, 2018). Furthermore, prior literature has leveraged electricity drivers in regular production planning (Golari et al, 2017).…”

Condition-based maintenance for multi-component systems: Modeling, structural properties, and algorithms

“…Endogenous uncertainty implies that the underlying stochastic process is influenced by the decisions. Therefore, the probabilities of scenarios are decisiondependent and usually nonlinear [65,66]. There is a lack of efficient method to solve this type of problem.…”

Optimal Planning of Electric Power Systems

“…With the power sector being constantly subjected to changes driven by economical, technical, technological and environmental issues, the body of GEP literature has persistently expanded to accommodate the new requirements, through a variety of modeling and solution methods. Some of the developments include: improvements in the details considered, such as reserve requirements [8,9], reliability and maintenance [8,[10][11][12], policy developments such as the restructuring of the power sector and the introduction of competition [10,[13][14][15], CO 2 mitigation solutions [16,17], renewable energy resources integration and support schemes [15,[18][19][20][21], uncertainty and stochasticity in generation production and demand [10,19,[22][23][24][25], demand side management (DSM) [26,27], and smartgrids [28], among others. Reviews of the GEP problem can be found in [6,29,30], and a comprehensive recent review in [31].…”