• Energy Research

This paper focuses on applications in the area of WAMS (wide area measurement systems) and WACS (wide area control systems) development. A main issue is the report of an international cooperation oriented to test new algorithms and methods in this field. A feature of the cooperation has been the planning and carrying out of the common full-scale experiment in power systems aimed to investigate the feasibility in the implementation of new technologies of monitoring and control. The cooperation experience of the full-scale experiment at the Russian Far East Interconnected Power System is described in the paper.

This paper focuses on applications in the area of WAMS (wide area measurement systems) and WACS (wide area control systems) development. A main issue is the report of an international cooperation oriented to test new algorithms and methods in this field. A feature of the cooperation has been the planning and carrying out of the common full-scale experiment in power systems aimed to investigate the feasibility in the implementation of new technologies of monitoring and control. The cooperation experience of the full-scale experiment at the Russian Far East Interconnected Power System is described in the paper.

Abstract This paper proposes a new optimization algorithm, namely Self-Adoptive Learning with Time Varying Acceleration Coefficient-Gravitational Search Algorithm (SAL-TVAC-GSA), to solve highly nonlinear, non-convex, non-smooth, non-differential, and high-dimension single- and multi-objective Energy Hub Economic Dispatch (EHED) problems. The presented algorithm is based on GSA considering three fundamental modifications to improve the quality solution and performance of original GSA. Moreover, a new optimization framework for economic dispatch is adapted to a system of energy hubs considering different hub structures, various energy carriers (electricity, gas, heat, cool, and compressed air), valve-point loading effect and prohibited zones of electric-only units, as well as the different equality and inequality constraints. To show the effectiveness of the suggested method, a high-complex energy hub system consisting of 39 hubs with 29 structures and 76 energy (electricity, gas, and heat) production units is proposed. Two individual objectives including energy cost and hub losses are minimized separately as two single-objective EHED problems. These objectives are simultaneously minimized in the multi-objective optimization. Results obtained by SAL-TVAC-GSA in terms of quality solution and computational performance are compared with Enhanced GSA (EGSA), GSA, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to demonstrate the ability of the proposed algorithm in finding an operating point with lower objective function.

Abstract This paper proposes a new optimization algorithm, namely Self-Adoptive Learning with Time Varying Acceleration Coefficient-Gravitational Search Algorithm (SAL-TVAC-GSA), to solve highly nonlinear, non-convex, non-smooth, non-differential, and high-dimension single- and multi-objective Energy Hub Economic Dispatch (EHED) problems. The presented algorithm is based on GSA considering three fundamental modifications to improve the quality solution and performance of original GSA. Moreover, a new optimization framework for economic dispatch is adapted to a system of energy hubs considering different hub structures, various energy carriers (electricity, gas, heat, cool, and compressed air), valve-point loading effect and prohibited zones of electric-only units, as well as the different equality and inequality constraints. To show the effectiveness of the suggested method, a high-complex energy hub system consisting of 39 hubs with 29 structures and 76 energy (electricity, gas, and heat) production units is proposed. Two individual objectives including energy cost and hub losses are minimized separately as two single-objective EHED problems. These objectives are simultaneously minimized in the multi-objective optimization. Results obtained by SAL-TVAC-GSA in terms of quality solution and computational performance are compared with Enhanced GSA (EGSA), GSA, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to demonstrate the ability of the proposed algorithm in finding an operating point with lower objective function.

This paper deals with the development of a nonlinear programming methodology for evaluating available transfer capability. The main feature of the approach is the capability to treat static and dynamic security constraints in a unique integrated piece of software. The algorithm has been implemented and tested on an actual power system.

This paper deals with the development of a nonlinear programming methodology for evaluating available transfer capability. The main feature of the approach is the capability to treat static and dynamic security constraints in a unique integrated piece of software. The algorithm has been implemented and tested on an actual power system.

Summary form only given. As power systems become more stressed due to limited resources and economic pressure such as competitive market of energy, there is an increasing interest in improving dynamic performances. Power system performances depend upon a large number of decisions and a typical problem is to choose the "best" set of decisions to achieve a particular objective. So the essential optimization problem is to find the set of decisions which minimize the cost function. Both the model building and the optimization phases require large amounts of calculation and these calculations increase dramatically as the order of the problem increases. Starting from an overview of the dynamic optimization in the continuous time domain, this paper aims to show how a new methodology, based on discretized dynamic optimization, can be applied for assessing preventive control actions to guarantee dynamic security of power systems. The idea is to discretize from the very beginning the differential problem and just solve it through nonlinear programming techniques or gradient-based methods used for static optimization problems. The proposed approach entails the ability to force the system trajectories in an acceptable state space domain under a set of severe but credible contingencies and gives indications about preventive actions when necessary. The approach is sufficiently general to improve the transient behavior of power system with regard to different objectives. In the paper, numerical results are provided to show the feasibility of the approach for an actual Italian power grid.

Summary form only given. As power systems become more stressed due to limited resources and economic pressure such as competitive market of energy, there is an increasing interest in improving dynamic performances. Power system performances depend upon a large number of decisions and a typical problem is to choose the "best" set of decisions to achieve a particular objective. So the essential optimization problem is to find the set of decisions which minimize the cost function. Both the model building and the optimization phases require large amounts of calculation and these calculations increase dramatically as the order of the problem increases. Starting from an overview of the dynamic optimization in the continuous time domain, this paper aims to show how a new methodology, based on discretized dynamic optimization, can be applied for assessing preventive control actions to guarantee dynamic security of power systems. The idea is to discretize from the very beginning the differential problem and just solve it through nonlinear programming techniques or gradient-based methods used for static optimization problems. The proposed approach entails the ability to force the system trajectories in an acceptable state space domain under a set of severe but credible contingencies and gives indications about preventive actions when necessary. The approach is sufficiently general to improve the transient behavior of power system with regard to different objectives. In the paper, numerical results are provided to show the feasibility of the approach for an actual Italian power grid.