Search

This paper proposes an optimization-based method of planning reactive power control for electric transmission systems to endow them with the capability of being reconfigured to a secure configuration under a list of contingencies. The overall objective function is to minimize the installation cost of new controls such as mechanically switched capacitors, while satisfying the requirements of voltage stability margin and voltage magnitude under a contingency list. The backward/forward search algorithm with linear complexity is used to select candidate locations for switched capacitors. Optimal locations and amounts of new switch controls are obtained by solving a sequence of mixed integer programming problems. The modified New England 39-bus system and a North American power system with 6358 buses are adopted to illustrate the effectiveness of the proposed method. Index Terms--Mixed integer programming, reactive power planning, reconfiguration, voltage stability.

We propose a component model for the scheduling of combined-cycle gas turbine (CCGT) units by mixed-integer programming (MIP) in which combustion turbines (CTs) and steam turbines (STs) are modeled as individual units. The hourly schedule of CCGT based on the component model is compared with that of the mode model. The modeling of modes, which includes a combination of CTs and STs, would require certain approximations for representing fuel input-power output curves, ramping rate limits, minimum operating time limits, etc. The approximations can result in sub-optimal schedules. Furthermore, the commitment and dispatch of CCGTs based on the mode model will require a real-time dispatch to individual CT and ST components of CCGT. In comparison, the mode modeling approximations will no longer be required in the component model as individual CTs and STs are modeled and dispatched. The enhancement tools such as duct burners, foggers, and peak firing for increasing the CCGT output can be easily modeled in the component model. Case studies show that the proposed component model is effective for representing CCGTs, and verify that the proposed component model can potentially save CCGT operating costs. Numerical simulations in this paper also demonstrate the application of the component model of CCGT to schedule a cogeneration unit. Index Terms--Combined-cycle gas turbine, component model, mixed-integer programming, mode model, short-term unit commitment, state transition diagram.

Phasor measurement units (PMUs) are considered as a promising tool for future monitoring, protection and control of power systems. In this paper, a unified approach is proposed in order to determine the optimal number and locations of PMUs to make the system measurement model observable and thereby can be used for power system state estimation. The PMU placement problem is formulated as a binary integer linear programming (BILP), in which the binary decision variables (0, 1) determine whether to install a PMU at each bus, while preserving the system observability and lowest system metering economy. The proposed approach integrates the impacts of both existing conventional power injection/flow measurements (if any) and the possibility of single or multiple PMU loss into the decision strategy of the optimal PMU allocation. Unlike other available techniques, the network topology remains unaltered for the inclusion of conventional measurements, and therefore the network connectivity matrix is built only once based on the original network topology. The mathematical formulation of the problem maintains the original bus ordering of the system under study, and therefore the solution directly points at the optimal PMU locations. Simulations using Matlab are conducted on a simple testing seven-bus system, as well as on different IEEE systems (14-bus, 30-bus, 57-bus, and 118-bus) to prove the validity of the proposed method. The results obtained in this paper are compared with those published before in literature. Index Terms--Integer programming, optimal location, PMUs, state estimation.

This paper presents a stochastic midterm risk-constrained hydrothermal scheduling algorithm in a generation company (GENCO). The objective of a GENCO is to maximize payoffs and minimize financial risks when scheduling its midterm generation of thermal, cascaded hydro, and pumped-storage units. The proposed schedule will be used by the GENCO for bidding purposes to the ISO. The optimization model is based on stochastic price-based unit commitment. The proposed GENCO solution may be used to schedule midterm fuel and natural water inflow resources for a few months to a year. The proposed stochastic mixed-integer programming solution considers random market prices for energy and ancillary services, as well as the availability of natural water inflows and generators in Monte Carlo scenarios. Financial risks associated with uncertainties are considered by applying expected downside risks which are incorporated explicitly as constraints. Variable time-steps are adopted to avoid the exponential growth in solution time and memory requirements when considering midterm constraints. A single water-to-power conversion function is used instead of several curves for representing water head and discharge parameters. Piecewise linearized head-dependant water-to-power conversion functions are used for computational efficiency. Illustrative examples examine GENCOs' midterm generation schedules, risk levels, fuel and water usage, and hourly generation dispatches for bidding in energy and ancillary services markets. The paper shows that GENCOs could decrease their financial risks by adjusting expected payoffs. Index Terms--Financial and physical risks, generating companies, midterm operation, mixed integer programming, Monte Carlo simulation, stochastic price-based unit commitment, variable time step.

This paper proposes an integrated model for assessing the impact of interdependency of electricity and natural gas networks on power system security. The integrated model incorporates the natural gas network constraints into the optimal solution of security-constrained unit commitment. The natural gas network is modeled by daily and hourly limits on pipelines, sub-areas, plants, and generating units. The application of fuel diversity (e.g., generating units with fuel switching capability) is presented as an effective peak shaving strategy for natural gas demand which could hedge price volatilities of natural gas and electric power. The proposed model can be used by a vertically integrated utility for the commitment and dispatch of generating units and the allocation of natural gas for the next day utilization. The proposed model can also be used for measuring the security of social services by modeling the interdependency of natural gas and electric power system infrastructures. If the proposed model is used by GENCOs, gas constraints will be submitted to electricity markets as energy constraints. Illustrative examples show the impact of natural gas supply infrastructure on the economic operation of a vertically integrated utility. The examples also discuss the impact of generating units with fuel switching capability on the power system security when the supply of natural gas is limited. Index Terms--Fuel switching, interdependency, mixed integer programming, natural gas pipelines and storage, power system operation, security-constrained unit commitment.