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Generating units, participating in the secondary frequency control of a control area, are usually spinning units already connected to the network and operating outside their range of optimal performance. This paper deals with an alternative method of providing secondary frequency control called Rapid-Start (RS). It consists in assigning a regulation band to several off-line units (RS units) which are capable of being started and connected rapidly, therefore allowing the online units to operate closer to their nominal power. As RS operation may have economic benefits, an appropriate algorithm to start up an RS unit needs to be developed. This paper proposes a methodology to evaluate, compare, and choose the most appropriate RS algorithm among the ones developed for a certain AGC control area. An optimization model provides a reference by determining the hypothetically ideal start-ups and shut-downs of RS units. In addition, the definition of performance indexes to evaluate, compare, and choose the different RS algorithms is proposed. The proposed methodology is illustrated for a secondary frequency control zone within the Spanish power system by using real data signals. [ABSTRACT FROM PUBLISHER]

This paper seeks to improve the computational time needed to solve transmission switching problems. Transmission switching provides an effective way to reduce operating costs in power system operations by altering the topology of the transmission network. However, determining the optimal set of lines to switch creates an enormous computational burden. Transmission switching formulations add binary decision variables for many transmission lines in the system to indicate if they are switched. This creates a very weak formulation that is difficult to solve. Altering the transmission topology by switching lines can affect the reliability of the network, for instance, by creating islands. Additional reliability constraints need to be added to the problem formulation. These constraints can potentially make an already difficult problem even harder. This paper takes a different approach to the transmission switching problem. Rather than preventing islanding by using constraints in the problem formulation, we look at how they can be used to improve the solution process. Specifically, we develop a cutting plane algorithm to generate valid inequalities and fix variables based on the fact that optimal solutions to the transmission switching problem that do not contain islands exist. [ABSTRACT FROM AUTHOR]

Security-constrained unit commitment (SCUC), as one of key components in power system operation, is being widely applied in vertically integrated utilities and restructured power systems. The efficient solution framework is to implement iterations between a master problem (unit commitment) and subproblems (network security evaluations). In industrial applications, both Lagrangian relaxation and mixed-integer programming are commonly applied for the unit commitment problem, and both linear sensitivity factor and Benders cut methods are used to generate additional constraints in the phase of network security evaluations. This paper evaluates capabilities and performances of each algorithm through technical discussion and numerical testing. Special topics on the large-scale SCUC engine development are also discussed in this paper, such as input data screening, inactive constrains elimination, contingency management, infeasibility handling, parallel computing, and model simplification. This paper will benefit academic researchers, software developers, and system operators when they design, develop and assess effective models and algorithms for solving large-scale SCUC problems. [ABSTRACT FROM PUBLISHER]

This paper investigates a very practical and commonly encountered problem in state estimation. It is related to the placement of measurements in order to make an otherwise unobservable system observable. Typically, this is a situation that is frequently encountered during the daily operation as one or more of the measurements become unavailable due to various unexpected reasons. Under such circumstances, the state estimator resorts to the use of pseudo-measurements in order to recover observability and solve the state estimation problem. Pseudo-measurements that will make the system observable can be selected by applying one of the well-developed network observability analysis techniques in a repetitive fashion adding one candidate measurement at a time until the system is no longer unobservable. As the system size increases, this approach may prove computationally expensive. A nonrepetitive solution has also been proposed. However, its computational requirements grow rapidly with increasing number of observable islands that may exist in large scale systems. This paper presents an alternative approach which will be shown to be suitable for large scale power systems providing a compromise between the two existing techniques. Examples are given to highlight the main benefits of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper presents a stochastic midterm risk-con- strained 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. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive expansion planning algorithm of generation and transmission components in multi-area power systems. The objective is to minimize the total system cost in the planning horizon, comprising investment and operation costs and salvage values subject to long-term system reliability and short-term operation constraints. The multi-area expansion planning problem is decomposed into a planning problem and annual reliability subproblems. The planning decisions calculated in the planning problem would also satisfy the short-term operation constraints. A detailed model of thermal and hydro units is considered using the mixed-integer programming (MIP) formulation. In addition, a multi-state representation for the expansion planning of renewable energy units is explored. The proposed approach considers customers' demand response as an option for reducing the short-term operation costs. The planning problem solution is applied to the annual reliability subproblems which examine system reliability indices as a post-processor. If the reliability limit is not satisfied, additional reliability constraints will be introduced which are based on the sensitivity of system reliability index to investment decisions. The new reliability constraints are added to the next iterations of the planning problem to govern the revised plan for the optimal expansion. Numerical simulations indicate the effectiveness of the proposed approach for solving the operation-constrained multi-area expansion planning problem of practical power systems. [ABSTRACT FROM PUBLISHER]

This paper presents both application and comparison of the metaheuristic techniques to Generation Expansion Planning (GEP) problem. The Metaheuristic techniques such as the Genetic Algorithm, Differential Evolution, Evolutionary Programming, Evolutionary Strategy, Ant Colony Optimization, Particle Swarm Optimization, Tabu Search, Simulated Annealing, and Hybrid Approach are applied to solve GEP problem. The original GEP problem is modified using the proposed methods Virtual Mapping Procedure (VMP) and Penalty Factor Approach (PFA), to improve the efficiency of the metaheuristic techniques. Further, Intelligent Initial Population Generation (IIPG), is introduced in the solution techniques to reduce the computational time. The VMP, PFA, and IIPG are used in solving all the three test systems. The GEP problem considered synthetic test systems for 6-year, 14-year, and 24-year planning horizon having five types of candidate units. The results obtained by all these proposed techniques are compared and validated against conventional Dynamic Programming and the effectiveness of each proposed methods has also been illustrated in detail. [ABSTRACT FROM AUTHOR]

This paper is concerned with developing an algorithm for solving the coordination problem that arises in a new equilibrium model [8], which for the purpose of this presentation applies to a static (no-time coupling costs or constraints) electricity pool market with price inelastic demand and no network. The new equilibrium model has the following main properties: i) every scheduled generator satisfies its minimum surplus (or bid profit) condition; ii) the energy price is a system marginal cost (Lagrange multiplier associated with the power balance constraint in the related economic dispatch problem where all of the discrete variables are fixed to their optimal values); iii) the power balance and all the generators' technical constraints are satisfied. To solve the coordination problem, which is a subproblem of the new equilibrium model, It Is mathematically convenient to cast the former as a three-level nested optimization problem. We substitute the ensuing lower-level subproblems with an equivalent set of explicit algebraic equalities and inequalities. Hence, we obtain a one-level problem, which is a discrete-continuous mathematical program with complementarity or equilibrium constraints (MPEC). Finally, we transform the one-level mathematical program into mixed-integer linear form by substitution of the complementarity terms and the remaining nonlinear terms. [ABSTRACT FROM AUTHOR]