This paper summarizes the technical activities of the IEEE Task Force on Solving Large Scale Optimization Problems in Electricity Market and Power System Applications. This Task Force was established by the IEEE Technology and Innovation Subcommittee to first review the state-of-the-art of the security-constrained unit commitment (SCUC) business model, its mathematical formulation, and solution techniques in solving electricity market clearing problems. The Task Force then investigated the emerging challenges of future market clearing problems and presented efforts in building benchmark mathematical and business models.

Connecting a large number of intermittent distributed generators (DGs) and electric vehicles (EVs) to a distribution network increases uncertainty and seriously threatens the safety and economic operation of that distribution network. Robust optimization is an important method of hedging against the adverse effects of uncertainty. However, the existing literature on robust optimization of distribution networks has not taken into account constraints on the travel distances of regulating equipment. In response to this deficiency, this paper considers constraints on the cycle period of energy storage systems (ESSs), travel distances of switchable capacitors and reactors, and load tap changers (OLTCs) and step voltage regulators (SVRs). A two-stage multi-period mixed-integer second-order cone robust model for coordinated active and reactive power optimization of a distribution network is formulated based on branch flow equations. There are not any dummy variables in the second stage model. In contrast to the deficiency of the low computational rate of the column-and-constraint generation (CCG) method, an iterative solution to the first and second stage models is proposed based on the cutting plane. Increases to the variables and constraints are not needed to solve the first stage model. For the second stage multi-period model, only the model of each single period needs to be solved first. Then the results of the two models are accumulated. Overall, the computational speed is significantly enhanced. The capabilities of the proposed method are validated by three simulation cases.

The fluctuation and intermittency of the wind and solar power outputs results in the regulation pressure of thermal units in power systems. The adjustable energy-intensive loads (such as the Electrolytic aluminium and Steel plants) have great potentialities to participate in demand response (DR) programs. This paper proposes an optimal scheduling method of the unit commitment (UC) with the consideration of the energy-intensive loads participating in wind and solar power consumption. It adopts the nonparametric kernel density estimation method to model the wind and solar power outputs. And then it uses Frank-Copula function to describe the correlation between the scenarios of wind and solar power outputs. A stochastic unit commitment (SUC) model introduces chance constrained theory of the reserve coefficient to describe time-variant scenarios, on the basis of the deviation between the typical and simulative scenarios. The simulation results based on IEEE 118-bus system shows that the energy-intensive load in the SUC model can flexibly adjust and respond to the change of wind and solar power output, reduce the impact of wind and solar power output’s uncertainty and promote the consumption of wind and solar power.