Yuting Ji

Large scale charging of Electric Vehicles

The problem of scheduling for large scale charging of Electric Vehicles (EVs) is considered. As part of the future EV infrastructure, a Large Scale Charging (LSC) facility is capable of charging hundreds of electric vehicles simultaneously. As an intelligent load in the future smart grid, LSC requires properly designed pricing and scheduling algorithms that take into account the electricity consumed, the arrival-departure characteristics, and overall charging capacity. The scheduling of LSC is formulated as a deadline scheduling problem. Utility functions that combine both amount of charge and tightness of the deadline are proposed. Under arbitrary (and deterministic) arrival, departure, and charging characteristics, a scheduling policy referred to as deadline scheduling with admission control is proposed. The proposed algorithm achieves the highest competitive ratio (against the best offline scheduling) for the utility function linear in charging level among all online scheduling algorithms. It also offers significant gain over benchmark scheduling algorithms such as the Earliest Deadline First (EDF) scheduling and the First Come First Serve (FCFS) scheduling in terms of average performance for general utility functions when tested with randomly generated charging requests.

Deadline scheduling for large scale charging of electric vehicles with renewable energy

The problem of scheduling for the large scale charging of electric vehicles with renewable sources is considered. A new online charging algorithm referred to as Threshold Admission with Greedy Scheduling (TAGS) is proposed by formulating the charging problem as one of deadline scheduling with admission control and variable charging capacities. TAGS has low computation cost and requires no prior knowledge on the distributions of arrival traffic, battery charging (service) time, and available energy from renewable sources. It has a reserve dispatch algorithm designed to compensate the intermittency of renewable sources. Performance of TAGS is compared with benchmark scheduling algorithms such as the Earliest Deadline First (EDF) and the First Come First Serve (FCFS) with aggressive and conservative reserve dispatch algorithms.

Probabilistic Forecasting of Real-Time LMP and Network Congestion

The short-term forecasting of real-time locational marginal price (LMP) and network congestion is considered from a system operator perspective. A new probabilistic forecasting technique is proposed based on a multiparametric programming formulation that partitions the uncertainty parameter space into critical regions from which the conditional probability distribution of the real-time LMP/congestion is obtained. The proposed method incorporates load/generation forecast, time varying operation constraints, and contingency models. By shifting the computation associated with multiparametric programs offline, the online computational cost is significantly reduced. An online simulation technique by generating critical regions dynamically is also proposed, which results in several orders of magnitude improvement in the computational cost over standard Monte Carlo methods.

Probabilistic Forecast of Real-Time LMP via Multiparametric Programming

The problem of short-term probabilistic forecast of real-time locational marginal price (LMP) is considered. A new forecast technique is proposed based on a multiparametric programming formulation that partitions the uncertainty parameter space into critical regions from which the conditional probability mass function of the real-time LMP is estimated using Monte Carlo techniques. The proposed methodology incorporates uncertainty models such as load and stochastic generation forecasts and system contingency models. With the use of offline computation of multiparametric linear programming, online computation cost is significantly reduced.

Improving Multi-job MapReduce Scheduling in an Opportunistic Environment

As a state-of-the-art programming model for big data analytics, MapReduce is well suited for parallel processing of large data sets in opportunistic environments. Existing research on MapReduce in opportunistic environment has focused on improving single job performance, the issue of fairness that is critical in the more dominant scenario of multiple concurrent jobs remains unexplored. We address this problem by proposing an opportunistic fair scheduling algorithm, which extends the broadly adopted Fair Scheduler to an environment where nodes are intermittently available with possibly different availability patterns. The proposed scheduler maintains statistics specific to the opportunistic environment, e.g., node availability rates and pairwise availability correlations, and utilizes this information in scheduling decisions to improve fairness. Using a Hadoop-based implementation, we compare our scheduler with the current Hadoop Fair Scheduler on representative benchmarks. Our experiments verify that our scheduler can significantly reduce the variability in job completion times.

Forecasting real-time locational marginal price: A state space approach

The problem of forecasting the real-time locational marginal price (LMP) by a system operator is considered. A new probabilistic forecasting framework is developed based on a time in-homogeneous Markov chain representation of the realtime LMP calculation. By incorporating real-time measurements and forecasts, the proposed forecasting algorithm generates the posterior probability distribution of future locational marginal prices with forecast horizons of 6-8 hours. Such a short-term forecast provides actionable information for market participants and system operators. A Monte Carlo technique is used to estimate the posterior transition probabilities of the Markov chain, and the real-time LMP forecast is computed by the product of the estimated transition matrices. The proposed forecasting algorithm is tested on the PJM 5-bus system. Simulations show marked improvements over benchmark techniques.

Multi-proxy interchange scheduling under uncertainty

The problem of inter-regional interchange scheduling using a multiple proxy bus representation is considered. A new scheduling technique is proposed for the multi-proxy bus system based on a stochastic optimization that captures uncertainty in renewable generation and stochastic load. In particular, the proposed algorithm iteratively optimizes the interchange across multiple proxy buses using a vectorized notion of demand and supply functions. The proposed technique leverages the operators capability of forecasting locational marginal prices (LMPs) and obtains the optimal interchange schedule directly without iterations between operators.

Probabilistic Forecasting and Simulation of Electricity Markets via Online Dictionary Learning

The problem of probabilistic forecasting and online simulation of real-time electricity market with stochastic generation and demand is considered. By exploiting the parametric structure of the direct current optimal power flow, a new technique based on online dictionary learning (ODL) is proposed. The ODL approach incorporates real-time measurements and historical traces to produce forecasts of joint and marginal probability distributions of future locational marginal prices, power flows, and dispatch levels, conditional on the system state at the time of forecasting. Compared with standard Monte Carlo simulation techniques, the ODL approach offers several orders of magnitude improvement in computation time, making it feasible for online forecasting of market operations. Numerical simulations on large and moderate size power systems illustrate its performance and complexity features and its potential as a tool for system operators.

Stochastic Interchange Scheduling in the Real-Time Electricity Market

The problem of inter-regional interchange scheduling in the presence of stochastic generation and load is considered. An interchange scheduling technique based on a two-stage stochastic minimization of expected operating cost is proposed. Because directly solving the stochastic optimization is intractable, an equivalent problem that maximizes the expected social welfare is formulated. The proposed technique leverages the operators capability of forecasting locational marginal prices and obtains the optimal interchange schedule without iterations among operators. Several extensions of the proposed technique are also discussed.

Stochastic coordinated transaction scheduling via probabilistic forecast

The problem of real-time interchange scheduling between two independently operated regions is considered. An optimal scheduling scheme is proposed by maximizing the expected economic surplus based on Coordinated Transaction Scheduling (CTS) mechanism. The proposed technique incorporates probabilistic forecasts of renewable generation to optimize the interchange schedule using a parametric programming formulation, from which statistical real-time generation supply offer curves are constructed.