Ross Baldick

Analysis of electric grid security under terrorist threat

We describe new analytical techniques to help mitigate the disruptions to electric power grids caused by terrorist attacks. New bilevel mathematical models and algorithms identify critical system components (e.g., transmission lines, generators, transformers) by creating maximally disruptive attack plans for terrorists assumed to have limited offensive resources. We report results for standard reliability test networks to show that the techniques identify critical components with modest computational effort.

Theory and Application of Linear Supply Function Equilibrium in Electricity Markets

We consider a supply function equilibrium (SFE) model of interaction in an electricity market. We assume a linear demand function and consider a competitive fringe and several strategic players having capacity limits and affine marginal costs. The choice of SFE over Cournot equilibrium and other models and the choice of affine marginal costs is reviewed in the context of the existing literature. We assume that bid rules allow affine or piecewise affine non-decreasing supply functions by firms and extend results of Green and Rudkevitch concerning the linear SFE solution. An incentive compatibility result is proved. We also find that a piecewise affine SFE can be found easily for the case where there are non-negativity limits on generation. Upper capacity limits, however, pose problems and we propose an ad hoc approach. We apply the analysis to the England and Wales electricity market, considering the 1996 and 1999 divestitures. The piecewise affine SFE solutions generally provide better matches to the empirical data than previous analysis.

Coarse-grained distributed optimal power flow

We present an approach to parallelizing optimal power flow (OPF) that is suitable for coarse-grained distributed implementation and is applicable to very large interconnected power systems. We demonstrate the approach on several medium size systems, including IEEE Test Systems and parts of the ERCOT system. Our simulations demonstrate the feasibility of distributed implementation of OPF. Rough estimates are made of parallel efficiencies and speed-ups.

A comparison of distributed optimal power flow algorithms

We present an approach to parallelizing optimal power flow (OPF) that is suitable for coarse-grained distributed implementation and is applicable to very large interconnected power systems. The proposed distributed scheme can be used to coordinate a heterogeneous collection of utilities. Three mathematical decomposition coordination methods are introduced to implement the proposed distributed scheme: the auxiliary problem principle (APP), the predictor-corrector proximal multiplier method (PCPM), and the alternating direction method (ADM). We demonstrate the approach on several medium size systems, including IEEE Test Systems and parts of the ERCOT (Electric Reliability Council of Texas) system.

The generalized unit commitment problem

The authors formulate a generalized version of the unit commitment problem that can treat minimum up- and down-time constraints, power flow constraints, line flow limits, voltage limits, reserve constraints, ramp limits, and total fuel and energy limits on hydro and thermal power generating units. They propose an algorithm for this problem, based on Lagrangian decomposition, and demonstrate the algorithm with reference to a simple model system. >

Initial review of methods for cascading failure analysis in electric power transmission systems IEEE PES CAMS task force on understanding, prediction, mitigation and restoration of cascading failures

Large blackouts are typically caused by cascading failure propagating through a power system by means of a variety of processes. Because of the wide range of time scales, multiple interacting processes, and the huge number of possible interactions, the simulation and analysis of cascading blackouts is extremely complicated. This paper defines cascading failure for blackouts and gives an initial review of the current understanding, industrial tools, and the challenges and emerging methods of analysis and simulation.

A fast distributed implementation of optimal power flow

The authors describe a distributed implementation of optimal power flow on a network of workstations. High performance is achieved on large real-world systems, including a 2587 line representation of the ERCOT system. The approach illustrates a general framework for parallelizing power system optimization problems.

Worst-Case Interdiction Analysis of Large-Scale Electric Power Grids

This paper generalizes Benders decomposition to maximize a nonconcave objective function and uses that decomposition to solve an ldquoelectric power grid interdiction problem.rdquo Under one empirically verified assumption, the solution to this bilevel optimization problem identifies a set of components, limited by cardinality or ldquointerdiction resource,rdquo whose destruction maximizes economic losses to customers (and can thereby guide defensive measures). The decomposition subproblem typically incorporates a set of DC optimal power-flow models that cover various states of repair after an attack, along with a load-duration curve. Test problems describe a regional power grid in the United States with approximately 5000 buses, 6000 lines, and 500 generators. Solution time on a 2-GHz personal computer is approximately one hour.

Dynamic Demand Response Controller Based on Real-Time Retail Price for Residential Buildings

Demand response and dynamic retail pricing of electricity are key factors in a smart grid to reduce peak loads and to increase the efficiency of the power grid. Air-conditioning and heating loads in residential buildings are major contributors to total electricity consumption. In hot climates, such as Austin, Texas, the electricity cooling load of buildings results in critical peak load during the on-peak period. Demand response (DR) is valuable to reduce both electricity loads and energy costs for end users in a residential building. This paper focuses on developing a control strategy for the HVACs to respond to real-time prices for peak load reduction. A proposed dynamic demand response controller (DDRC) changes the set-point temperature to control HVAC loads depending on electricity retail price published each 15 minutes and partially shifts some of this load away from the peak. The advantages of the proposed control strategy are that DDRC has a detailed scheduling function and compares the real-time retail price of electricity with a threshold price that customers set by their preference in order to control HVAC loads considering energy cost. In addition, a detailed single family house model is developed using OpenStudio and Energyplus considering the geometry of a residential building and geographical environment. This HVAC modeling provides simulation of a house. Comfort level is, moreover, reflected into the DDRC to minimize discomfort when DDRC changes the set-point temperature. Our proposed DDRC is implemented in MATLAB/SIMULINK and connected to the EnergyPlus model via building controls virtual test bed (BCVTB). The real-time retail price is based on the real-time wholesale price in the ERCOT market in Texas. The study shows that DDRC applied in residential HVAC systems could significantly reduce peak loads and electricity bills with a modest variation in thermal comfort.

Dispatchable transmission in RTO markets

We consider transmission owners that bid capacity, under appropriate Regional Transmission Organization (RTO) market rules, at a positive price into forward and spot (dispatch) auctions to derive congestion revenues. This can encompass daily, monthly, or multimonthly auctions, allowing for commitment of transmission to reflect market needs in different time periods, e.g., seasons. We provide two and three node examples and a general formulation of the auction model.