The need to efficiently manage different reserve types, inertia, and the largest credible contingency is critical to

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Part I presents a formulation to optimize reserves for contingent events while explicitly including their response times. Part II highlights the implications of using this formulation in a reserves market. There are four aspects of the formulation that are of interest: (1) the performance of the solver, (2) the importance of inertia and contingency size. These implications are highlighted through two examples. (3) The proposed formulation is compared against a reserve optimization which does not distinguish between different response speeds, but adjusts the reserve requirement to satisfy the same frequency limits. Differentiating between response speeds within the optimization can provide a 23% reduction in total reserve requirement on average, which improves to a reduction of 52% in low inertia conditions. (4) A marginal pricing methodology is developed which prices reserve in a market context, and gives a unique price to each reserve provider depending on response speed. This gives incentive for reserve providers to improve response speed.

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Power flow solvers typically start from an initial point of power injection. This paper constructs a system of multiple initial points (SMIP) to enable selection of an appropriate initial point, with the objective to achieve a balanced improvement in the solution speed and accuracy, for problems with a large number of power flows. The intent is to recover time cost of forming the SMIP through the improvements to each power flow. The SMIP is tested on a time series based Monte Carlo study of Electric Vehicle (EV) hosting capacity in a low voltage distribution network, which has 5.4 million power flows. SMIP is applied to two power flow solvers: a Taylor series approximation and a Z-bus method. The accuracy of the quadratic Taylor series approximation was improved by a factor of 30 with a 27% increase in the solve time when compared against a single no-load initial point. A Z-bus solver with SMIP, limited to two iterations, gave the best performance for the EV hosting capacity case study.