An Approximately Optimal Algorithm for Scheduling Phasor Data Transmissions in Smart Grid Networks

In this paper, we devise a scheduling algorithm for ordering transmission of synchrophasor data from the substation to the control center in as short a time frame as possible, within the realtime hierarchical communications infrastructure in the electric grid. The problem is cast in the framework of the classic job scheduling with precedence constraints. The optimization setup comprises the number of phasor measurement units (PMUs) to be installed on the grid, a weight associated with each PMU, processing time at the control center for the PMUs, and precedence constraints between the PMUs. The solution to the PMU placement problem yields the optimum number of PMUs to be installed on the grid, while the processing times are picked uniformly at random from a predefined set. The weight associated with each PMU and the precedence constraints are both assumed known. The scheduling problem is provably NP-hard, so we resort to approximation algorithms which provide solutions that are suboptimal yet possessing polynomial time complexity. A lower bound on the optimal schedule is derived using branch and bound techniques, and its performance evaluated using standard IEEE test bus systems. The scheduling policy is power grid-centric, since it takes into account the electrical properties of the network under consideration.Comment: 8 pages, published in IEEE Transactions on Smart Grid, October 201

Optimal security-constrained power scheduling by Benders decomposition

This paper presents a Benders decomposition approach to determine the optimal day-ahead power scheduling in a pool-organized power system, taking into account dispatch, network and security constraints. The study model considers the daily market and the technical constraints resolution as two different and consecutive processes. The daily market is solved in a first stage subject to economical criteria exclusively and then, the constraints solution algorithm is applied to this initial dispatch through the redispatching method. The Benders partitioning algorithm is applied to this constraints solution process to obtain an optimal secure power scheduling. The constraints solution includes a full AC network and security model to incorporate voltages magnitudes as they are a critical factor in some real power systems. The algorithm determines the active power committed to each generator so as to minimize the energy redispatch cost subject to dispatch, network and security constraints. The solution also provides the reactive power output of the generators, the value of the transformers taps and the committed voltage control devices. The model has been tested in the IEEE 24-bus Reliability Test System and in an adapted IEEE 118-bus Test System. It is programmed in GAMS mathematical modeling language. Some relevant results are reported.Publicad

Short-term generation scheduling in a hydrothermal power system.

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A Stochastic Model for Self-scheduling Problem

The unit commitment (UC) problem is a typical application of optimization techniques in the power generation and operation. Given a planning horizon, the UC problem is to find an optimal schedule of generating units, including on/off status and production level of each generating unit at each time period, in order to minimize operational costs, subject to a series of technical constraints. Because technical constraints depend on the characteristics of energy systems, the formulations of the UC problem vary with energy systems. The self-scheduling problem is a variant of the UC problem for the power generating companies to maximize their profits in a deregulated energy market. The deterministic self-scheduling UC problem is known to be polynomial-time solvable using dynamic programming. In this thesis, a stochastic model for the self-scheduling UC problem is presented and an efficient dynamic programming algorithm for the deterministic model is extended to solve the stochastic model. Solutions are compared to those obtained by traditional mixed integer programming method, in terms of the solution time and solution quality. Computational results show that the extended algorithm can obtain an optimal solution faster than Gurobi mixed-integer quadratic solver when solving a stochastic self-scheduling UC problem with a large number of scenarios. Furthermore, the results of a simulation experiment show that solutions based on a large number of scenarios can generate more average revenue or less average loss