A Computationally Efficient Formulation for a Flexibility Enabling Generation Expansion Planning

Abstract

This paper presents a new mixed-integer linear programming formulation for combined generation, storage and demand response expansion planning. The proposed formulation captures flexibility dynamics in integrated energy systems in long horizons with large temporal resolution. The objective function considers costs related to: investment, operation, emission penalties, fixed and variable maintenance. The considered units are: generators, storage (batteries and hydro-pumped) and demand response units (desalination, power-to-X, electric vehicles and electrolysers). The operation is modelled as an hourly unit commitment including time-dependent startup costs and intertemporal constraints such as ramping limits and minimum up and down times. This flexibility enabling formulation boosts renewable integration and contributes towards decarbonisation of other energy sectors such as transport. A commercial solver is used to evaluate the formulation for large scale cases considering 20 years horizon and 12 full weeks per year imposing minimum renewable shares of 50 and 100% in year 9 and 19, respectively. The formulation exceeds tractability limits of previous works, achieves lower over-installation and renewable curtailment by integrating flexibility in the generation expansion problem, and allows to size demand responsive units if desired.

Decarbonizing energy islands with flexibility-enabling planning: The case of Santiago, Cape Verde

“…If this proportionality is not maintained, units with lower investment costs (which tend to present higher operational costs) would be chosen instead of those with higher investment and lower operation (which potentially pay-off over time). Since the basis of the formulation is adapted from [30], the size of each candidate unit is determined as an integer factor 𝑁 multiplied by the modular size. This approach presents two advantages, first simplifying the computational burden of finding the optimal size, while at the same time depicting a more realistic power system expansion.…”

“…In this work, we employ a recently developed flexibility-enabling generation expansion planning (FGEP) formulation to switch the focus from electric planning towards IES planning [30]. This way, the FGEP can be used, not only to plan the most suitable generation and storage mix, but also to shed light on how different policies aiming for the electrification of other energy sectors will affect the overall IES.…”

“…Therefore, flexibility facilitates electrification and renewable integration; minimizing the curtailment, load-shedding, and storage needs. Flexibility is limited by a number of general parameters/attributes, such as the minimum and maximum capacity limits, ramping rates regarding regular operation, start-up and shut-down manoeuvres (which are not necessarily symmetric), on/off state, and minimum and maximum on/off times [30]. In this respect, a high degree of flexibility is related to: an smaller minimum capacity, a larger maximum capacity, a higher operating ramping rates, smaller start-up and shut-down rates, and shorter minimum and maximum on/off times [36].…”

The Significance of Time Constraints in Unit Commitment Problems

“…Similarly, [15] presents a neural network-based method to simplify a unit commitment problem involving transmission constraints, capturing the temporal relationship between past line loading levels and removed transmission lines. [16] introduces a new mixed-integer linear programming formulation for generation expansion, targeting the identification of flexible technologies to optimize the utilization of variable renewable generation. On the other hand, [17] tackles security-constrained unit commitment for generation expansion planning, suggesting an efficient solution by identifying redundant constraints.…”