(637h) A Mid-Term, Market-Based Power Systems Planning Model

The integration of all the European national power markets into a single one is of top priority on the European Commission’s political agenda. The security of the supply was one of the key drivers in the early stages of the power market integration in Europe. Indeed, the first electricity interconnections among neighbouring countries were constructed to cover emergency situations. The advantages of the electricity markets’ integration include the diversification of the power generation mix, the reduction in the electricity prices by creating more competitive and transparent markets, and the fact that it can provide access to additional power generation capacity in case of a shortage in any one country.

Moreover, the expansion of grid interconnections is of paramount importance in order to facilitate the transition towards a power generation mix with very high levels of renewables penetration and lower CO₂ emission content (Hasan et al., 2015). As a direct consequence, the increased transmission capacity enables the more efficient utilization of the flexibility of the power plant fleet by increasing the flexibility to balance the intermittent wind and solar output (Hadera et al., 2015). Not surprisingly, the deployment of renewable energy at regions with weak interconnections infrastructure and far from the main load consuming centres often results in overloading of transmission lines (Xiao et al., 2011).

In that context, this work presents a generic Mixed Integer Linear Programming (MILP) model integrating a Mid-term Energy Planning model, which implements generation and transmission system planning at a yearly level (Koltsaklis et al., 2014), with a Unit Commitment model (Koltsaklis and Georgiadis, 2015), which simulates the Day-Ahead Electricity Market. The proposed modelling framework identifies the implementation (or not) of the interconnection of specific autonomous power networks to the mainland electric system, as well as the optimum interconnection capacity. It also systematically quantifies the effects in the Day-Ahead electricity market and on the energy mix. The proposed model can provide useful insights into the strategic and challenging decisions to be determined by investors and/or policy makers at a national and/or regional level, by providing the optimal energy roadmap and management, as well as clear price signal on critical energy projects under real operating and design constraints.

Keywords: Mixed integer linear programming; Unit commitment problem; Mid-term energy planning; Electric interconnections; Power markets

References

Hadera, H., Harjunkoski, I., Sand, G., Grossmann, I. E. & Engell, S. 2015. Optimization of steel production scheduling with complex time-sensitive electricity cost. Computers & Chemical Engineering, 76, 117-136.

Hasan, M. M. F., First, E. L., Boukouvala, F. & Floudas, C. A. 2015. A multi-scale framework for CO2 capture, utilization, and sequestration: CCUS and CCU. Computers & Chemical Engineering, 81, 2-21.

Koltsaklis, N. E., Dagoumas, A. S., Kopanos, G. M., Pistikopoulos, E. N. & Georgiadis, M. C. 2014. A spatial multi-period long-term energy planning model: A case study of the Greek power system. Applied Energy, 115, 456-482.

Koltsaklis, N. E. & Georgiadis, M. C. 2015. A multi-period, multi-regional generation expansion planning model incorporating unit commitment constraints. Applied Energy, 158, 310-331.

Xiao, J., Hodge, B.-M. S., Pekny, J. F. & Reklaitis, G. V. 2011. Operating reserve policies with high wind power penetration. Computers & Chemical Engineering, 35, 1876-1885. References