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Karl-Kiên Cao

Publications and source records attributed to Karl-Kiên Cao.

3 recordsLinked to original sources

Improving Operational Feasibility in Large-Scale Power System Planning

Large-scale power system planning mostly uses linearized, active power only approximations of the power flow equations, ignores many operational constraints, and tests the operational feasibility of the resulting systems only under strongly simplifying assumptions. We propose an approach to obtain solutions to large instances of the alternating current capacity expansion problem via redispatch and reinforcement of an initial solution. The problem formulation considers simultaneous expansion of generators, reactive compensation devices, storage systems, and transmission. Furthermore, it includes operational constraints via startup procedures and capability curves of power sources and simplified stability limits via constraints on voltage angle differences and voltage magnitudes. To obtain initial solutions, we test several established and partly modified power flow approximations and integrate them into an approach for iterative transmission expansion planning, thereby obtaining convex formulations. We demonstrate the approach on large problem instances covering the islands of Great Britain and Ireland at the transmission level, for which we extend the open data source to model reactive power. We find that including transmission losses to determine the initial solution is most decisive, as the amount of redispatch and reinforcements necessary to obtain an alternating current feasible solution is reduced, whereas incorporating reactive power constraints did not lead to further improvements. Our approach ensures an alternating current feasible system under weak assumptions, thus guaranteeing steady-state voltage stability and allowing subsequent dynamic grid simulations, which is instrumental for planning stable future inverter-dominated power systems.

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The Benefits of an Integrated Approach for Stability-Constrained Power System Planning

Increasing penetration of inverter-based resources in today's power systems requires substitution of the contribution from synchronous generators to dynamic voltage stability and inertial response. However, established approaches for power system planning are sequential, as stabilising measures are only considered at a later stage of the planning process. We investigate the advantages of an integrated approach for power system planning, where stabilising measures are considered simultaneously with the expansion of generation, transmission, and storage systems via simplified stability constraints on inertia and voltage stability. We find that system costs are reduced with the integrated approach and that the dual-use option of grid-forming battery energy storage systems is favoured over other stabilising measures like static synchronous compensators.

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Accelerating Stochastic Energy System Optimization Models: Temporally Split Benders Decomposition

Stochastic programming can be applied to consider uncertainties in energy system optimization models for capacity expansion planning. However, these models become increasingly large and time-consuming to solve, even without considering uncertainties. For two-stage stochastic capacity expansion planning problems, Benders decomposition is often applied to ensure that the problem remains solvable. Since stochastic scenarios can be optimized independently within subproblems, their optimization can be parallelized. However, hourly-resolved capacity expansion planning problems typically have a larger temporal than scenario cardinality. Therefore, we present a temporally split Benders decomposition that further exploits the parallelization potential of stochastic expansion planning problems. A compact reformulation of the storage level constraint into linking variables ensures that long-term storage operation can still be optimized despite the temporal decomposition. We demonstrate this novel approach with model instances of the German power system with up to 87 million rows and columns. Our results show a reduction in computing times of up to 60% and reduced memory requirements. Additional enhancement strategies and the use of distributed memory on high-performance computers further improve the computing time by over 80%.

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