Analysis and management of security constraints in overstressed power systems

Management of operational security constraints is one of the important tasks performed by system operators, which must be addressed properly for secure and economic operation. Constraint management is becoming an increasingly complex and challenging to execute in modern electricity networks for three main reasons. First, insufficient transmission capacity during peak and emergency conditions, which typically result in numerous constraint violations. Second, reduced fault levels, inertia and damping due to power electronic interfaced demand and stochastic renewable generation, which are making network more vulnerable to even small disturbances. Third, re-regulated electricity markets require the networks to operate much closer to their operational security limits, which typically result in stressed and overstressed operating conditions. Operational security constraints can be divided into static security limits (bus voltage and branch thermal limits) and dynamic security limits (voltage and angle stability limits). Security constraint management, in general, is formulated as a constrained, nonlinear, and nonconvex optimization problem. The problem is usually solved by conventional gradient-based nonlinear programming methods to devise optimal non-emergency or emergency corrective actions utilizing minimal system reserves. When the network is in emergency state with reduced/insufficient control capability, the solution space of the corresponding nonlinear optimization problem may be too small, or even infeasible. In such cases, conventional non-linear programming methods may fail to compute a feasible (corrective) control solution that mitigate all constraint violations or might fail to rationalize a large number of immediate post-contingency constraint violations into a smaller number of critical constraints. Although there exists some work on devising corrective actions for voltage and thermal congestion management, this has mostly focused on the alert state of the operation, not on the overstressed and emergency conditions, where, if appropriate control actions are not taken, network may lose its integrity. As it will be difficult for a system operator to manage a large number of constraint violations (e.g. more than ten) at one time, it is very important to rationalize the violated constraints to a minimum subset of critical constraints and then use information on their type and location to implement the right corrective actions at the right locations, requiring minimal system reserves and switching operations. Hence, network operators and network planners should be equipped with intelligent computational tools to “filter out” the most critical constraints when the feasible solution space is empty and to provide a feasible control solution when the solution space is too narrow. With an aim to address these operational difficulties and challenges, this PhD thesis presents three novel interdependent frameworks: Infeasibility Diagnosis and Resolution Framework (IDRF), Constraint Rationalization Framework (CRF) and Remedial Action Selection and Implementation Framework (RASIF). IDRF presents a metaheuristic methodology to localise and resolve infeasibility in constraint management problem formulations (in specific) and nonlinear optimization problem formulations (in general). CRF extends PIDRF and reduces many immediate post-contingency constraint violations into a small number of critical constraints, according to various operational priorities during overstressed operating conditions. Each operational priority is modelled as a separate objective function and the formulation can be easily extended to include other operational aspects. Based on the developed CRF, RASIF presents a methodology for optimal selection and implementation of the most effective remedial actions utilizing various ancillary services, such as distributed generation control, reactive power compensation, demand side management, load shedding strategies. The target buses for the implementation of the selected remedial actions are identified using bus active and reactive power injection sensitivity factors, corresponding to the overloaded lines and buses with excessive voltage violations (i.e. critical constraints). The RASIF is validated through both static and dynamic simulations to check the satisfiability of dynamic security constraints during the transition and static security constraints after the transition. The obtained results demonstrate that the framework for implementation of remedial actions allows the most secure transition between the pre-contingency and post-contingency stable equilibrium points

Multi objective economic dispatch using Pareto frontier differential evolution

Abstract: Multi Objective Economic dispatch (MOED) problem has gained recent attention due to the deregulation of power industry and environmental regulations. So generating utilities should optimize their emission in addition to the operating cost. In this paper a Pareto frontier Differential Evolution (PDE) technique is developed to solve MOED problem, which provides a set of feasible solutions to the problem. To evaluate the performance and applicability of the proposed method, it is implemented on the standard IEEE-30 bus system having six generating units including valve point effects. The results obtained demonstrate the effectiveness of the proposed method for solving the Multi Objective economic dispatch problem considering security constraints. Keywords: Multi objective economic dispatch, emission dispatch, valve point effects, Pareto frontier differential evolution

On convergence of conventional and meta-heuristic methods for security-constrained OPF analysis

Security-constrained optimal power flow (SCOPF) studies are used for assessing network performance during both planning and operational stages. The requirements for increased flexibility and improved security necessitate to use robust and computationally efficient SCOPF methods, which are crucial for "smart grid" applications requiring (close to) real-time network control. Conventional SCOPF methods solve the corresponding nonlinear power flow equations using gradient-based iterative approaches and are computationally efficient, but sensitive to selection of initial values and might suffer from convergence problems. Metaheuristic SCOPF methods are based on various approaches that search over the system state space and do not suffer from convergence problems, but are more computationally demanding. While network planners and operators regularly use conventional SCOPF methods, meta-heuristic methods are rarely implemented in practice, even for off-line analysis during the planning stage. Using as an example the IEEE 30-bus test network, this paper analyses and compares conventional and meta-heuristic methods for security-constrained OPF studies, showing that meta-heuristic methods can be used when conventional methods fail to converge and/or to provide a global optimum solution

Comparison of Conventional and Meta-Heuristic Methods for Security-Constrained OPF Analysis

Development and implementation of accurate, robust and computationally efficient analytical and modelling tolls is very important for the anticipated transformation of existing networks into the future “smart grids”. These tools for network analysis are used at both planning and operating stages, in order to ensure optimal design and configuration of power supply systems, in terms of the requirements for higher flexibility, increased security and improved overall techno-economic performance of modelled networks. In this context, particularly important are “smart grid” applications requiring (close to) realtime controls of large and interconnected power supply systems under serious contingency scenarios and other “highly stressed” network operating conditions. This paper provides a detailed discussion and analysis of both conventional and meta-heuristic methods for security-constrained optimal power flow (SCOPF) studies. The comparison of performance of two conventional SCOPF methods and three meta-heuristic SCOPF algorithms is illustrated on IEEE 14-bus and IEEE 30-bus test networks. The analysis and optimization of objective functions in considered SCOPF methods include minimization of constraint violations in post-contingency states, as well as minimization of fuel costs, active power losses, and CO2 emissions.Development and implementation of accurate, robust and computationally efficient analytical and modelling tolls is very important for the anticipated transformation of existing networks into the future "smart grids". These tools for network analysis are used at both planning and operating stages, in order to ensure optimal design and configuration of power supply systems, in terms of the requirements for higher flexibility, increased security and improved overall techno-economic performance of modelled networks. In this context, particularly important are "smart grid" applications requiring (close to) real-time controls of large and interconnected power supply systems under serious contingency scenarios and other "highly stressed" network operating conditions. This paper provides a detailed discussion and analysis of both conventional and meta-heuristic methods for security-constrained optimal power flow (SCOPF) studies. The comparison of performance of two conventional SCOPF methods and three meta-heuristic SCOPF algorithms is illustrated on IEEE 14-bus and IEEE 30-bus test networks. The analysis and optimization of objective functions in considered SCOPF methods include minimization of constraint violations in post-contingency states, as well as minimization of fuel costs, active power losses, and CO2 emissions