This paper presents a methodology to obtain flexible future capacity expansion plans under diverse types and sources of uncertainty classified as global and local. Planning flexibility is defined as the capability of a long-term planning solution to adapt cost-effectively to any of the conditions of characterizing the identified scenarios. Global (or high-impact) uncertainties allow us to create scenarios that guide the flexibility-based planning model; whereas local uncertainties allow us to create uncertainty sets that model the imperfect knowledge of each global uncertainty (GU). Our methodology, rather than choosing the most flexible plan among a set of candidate solutions, designs a flexible system that is less sensitive to the choice of scenarios. In addition to minimizing the investment and operational cost, the model minimizes its future adaptation cost to the conditions of other identified scenarios via adjustable robust optimization. Results obtained with our methodology in a 5-region US system under a 40-year planning horizon show how a flexible system adapts to future high-impact uncertainties at reasonably low costs with a low number of adaptation actions. A folding horizon process where GUs are guided by Markov chains was performed to assess the degree of flexibility of the system and its cost under multiple operation conditions.

This paper proposes a strategy for sizing a battery energy storage system (BESS) that supports primary frequency regulation (PFR) service of solar photo-voltaic plants. The strategy is composed of an optimization model and a performance assessment algorithm. The optimization model includes not only investment costs, but also a novel penalty function depending on the state of charge (SoC). This function avoids the existence of a potential inappropriate SoC trajectory during BESS operation that could impede the supply of PFR service. The performance assessment algorithm, fed by the optimization model sizing results, allows the emulation of BESS operation and determines either the success or failure of a particular BESS design. The quality of a BESS design is measured through number of days in which BESS failed to satisfactorily provide PFR and its associated penalization cost. Battery lifetime, battery replacements, and SoC are also key performance indexes that finally permit making better decisions in the election of the best BESS size. The inclusion of multiple BESS operational restrictions under PFR is another important advantage of this strategy since it adds a realistic characterization of BESS to the analysis. The optimization model was coded using GAMS/CPLEX, and the performance assessment algorithm was implemented in MATLAB. Results were obtained using actual frequency data obtained from the Colombian power system; and the resulting BESS sizes show that the number of BESS penalties, caused by failure to provide PFR service, can be reduced to zero at minimum investment cost.

This paper proposes and implements robust optimization methodologies for making investment decisions in the capacity expansion planning (CEP) of power systems in an uncertain environment. Uncertainties of fuel prices, demand, and transmission capacity are captured in an uncertainty set. With adjustable robust optimization (ARO), we represent all the decision variables as affine functions of multiple uncertain data. This adjustability of decisions provides that the ARO solution has significant less price of robustness than in traditional robust optimization (RO). ARO models uncertainty in terms of parameter ranges, called “uncertainty sets.” An attractive attribute of utilizing uncertainty sets is that they facilitate computational tractability when simulating scenarios with multiple uncertainties. We study the 40-year planning of a 5-region, 13-technology US energy portfolio. Results show that 1) by appropriately selecting the decision rules in ARO, the price of robustness can be significantly reduced while maintaining the same levels of robustness; and 2) the RO-based models maintain high levels of robustness even under operational conditions provided by data coming from larger sizes of the uncertainty sets.

This paper analyses the financial implications, from the point of view of an investor in renewable energy, which sells the energy for an uncertain price of electricity and decides to take advantage of the Colombian tax policy over the renewable energy. The policy, known as Investment Tax Allowance (ITA), encourages installation of renewable projects in a country traditionally dominated by hydro power. Price is modeled as a non-stationary autoregressive stochastic process with normally distributed error terms. Costs, and uncertain revenue and taxes are considered to assess the financial impact on a solar project when the policy is implemented. Since impact varies according to project ownership, two cases are evaluated: a generation company (GENCO-1) that only owns the solar project; and, an existent generation company (GENCO-2) that owns a portfolio of projects. Results indicate that if ITA is applied, it is likely that the GENCO-1 cannot take the full advantage of the incentive, as opposed to the GENCO-2. Although this policy might not satisfy planner objectives since it does not guarantee the construction of significantly high capacity of new renewable energy projects, it definitely represents an attractive mechanism to decrease tax obligations at the GENCO-2 level. Finally, a theoretical analysis shows that investment cost affects the mean of the present value; whereas tax rates impacts both its mean and standard deviation.