Irina Ciornei

A GA-API Solution for the Economic Dispatch of Generation in Power System Operation

This work proposes a novel heuristic-hybrid optimization method designed to solve the nonconvex economic dispatch problem in power systems. Due to the fast computational capabilities of the proposed algorithm, it is envisioned that it becomes an operations tool for both the generation companies and the TSO/ISO. The methodology proposed improves the overall search capability of two powerful heuristic optimization algorithms: a special class of ant colony optimization called API and a real coded genetic algorithm (RCGA). The proposed algorithm, entitled GAAPI, is a relatively simple but robust algorithm, which combines the downhill behavior of API (a key characteristic of optimization algorithms) and a good spreading in the solution space of the GA search strategy (a guarantee to avoid being trapped in local optima). The feasibility of the proposed method is first tested on a number of well-known complex test functions, as well as on four different power test systems having different sizes and complexities. The results are analyzed in terms of both quality of the solution and the computational efficiency; it is shown that the proposed GAAPI algorithm is capable of obtaining highly robust, quality solutions in a reasonable computational time, compared to a number of similar algorithms proposed in the literature.

Hybrid Ant Colony-Genetic Algorithm (GAAPI) for Global Continuous Optimization

Many real-life optimization problems often face an increased rank of nonsmoothness (many local minima) which could prevent a search algorithm from moving toward the global solution. Evolution-based algorithms try to deal with this issue. The algorithm proposed in this paper is called GAAPI and is a hybridization between two optimization techniques: a special class of ant colony optimization for continuous domains entitled API and a genetic algorithm (GA). The algorithm adopts the downhill behavior of API (a key characteristic of optimization algorithms) and the good spreading in the solution space of the GA. A probabilistic approach and an empirical comparison study are presented to prove the convergence of the proposed method in solving different classes of complex global continuous optimization problems. Numerical results are reported and compared to the existing results in the literature to validate the feasibility and the effectiveness of the proposed method. The proposed algorithm is shown to be effective and efficient for most of the test functions.

Efficient hybrid optimization solution for the economic dispatch with nonsmooth cost function

This paper proposes a novel global optimization technique to solve the nonconvex economic load dispatch (NCELD) problem. The foraging strategy of the pachycondyla apicalis ant (API) is hybridized with a genetic algorithm (GA) strategy to incorporate key features of both API and GA and form a relatively simple but robust algorithm, entitled GAAPI. The novel algorithm proposed in this paper combines the downhill behavior of API (a key characteristic of optimization algorithms) and a good spreading in the solution space of the GA search strategy (a guarantee to avoid being trapped in local optima). The feasibility of the proposed method is tested for three different test systems having different size and complexity. The results are calculated in terms of solution quality and computational efficiency; it is shown that the proposed GAAPI is capable of obtaining highly robust, quality solutions in a reasonable computational time.

Heuristic solution for the nonconvex dispatch of generation in power systems with high wind power share

Wind energy has drawn much attention recently due to its competitiveness compared to other renewable sources of energy, as well as its high benefits to the reduction of emissions and to the reduction of the dependency on fossil fuels. In this paper, a stochastic economic dispatch formulation is proposed to incorporate the impact of the variability of wind generation on the ramp rate limits constraints. The purpose of this method is to limit the probability of generation plus reserve not meeting the load due to the aggregated variability of the wind generation and the load demand. To better represent the conditions in real power systems, the cost of modern thermal units with multiple valves is considered. The problem is nonconvex and complex; therefore, a hybrid heuristic solution method is used. Numerical results, including sensitivity analysis, are reported and discussed based on a modified IEEE 30-bus test system.

Test System for Mapping Interdependencies of Critical Infrastructures for Intelligent Management in Smart Cities

The critical infrastructures such as power distribution networks (PDN), water networks, transportation and telecommunication networks that are settled within the area of a city produce a large amount of data from applications such as AMI, SCADA, Renewable Energy Management Systems, Asset Management Systems, and weather data. To convert these massive data into useful information, visualization is an effective solution. Visualizing this large amount of data in a holistic view of critical infrastructures mapping at a city level is a missing link. Visualization means here to convert the flow of continuous coming data into useful information. In this paper we propose a technique to visualize critical infrastructure data by using a system that consists of Geographic Information System (GIS) for buffer spatial analysis and Google Earth in sync with realistic planning and operation methodologies specific for each infrastructure modelled. The major goal of this work is to design, model and validate a benchmark system that is capable to visualize and map as well as to prepare the next inter-linking phase of modelling and analysis of interdependencies of several critical infrastructures. Furthermore, we aim to provide the grounds for a theoretical framework that can capture the interdependencies between critical infrastructures using techniques from graph theory, machine learning, econometric science and operation research. The proposed framework for modeling the interdependencies between several infrastructures within a city territory is based on hybrid system automata and it is among the first steps needed in developing fundamental mechanisms for resilient management of critical infrastructures and the safe operation of smart cities. An example on how this framework can be applied is also presented.

On the complexities of interdependent infrastructures for wide area monitoring systems

Electric power systems (EPS) evolved over years from local independent entities towards large interconnected networks monitored and controlled by sophisticated ICT technologies, and which, eventually will be transformed into Smart Grids where also distributed energy sources, storage, electric vehicles and appliances will be active components of the system. Thus, the scale of complexity involved in present and future power systems architectures is significantly greater than in the past. This paper aims to discuss the interdependency between electric power and communication systems under the system of systems concept. The nature and scale of interdependency between these two critical systems is then further analyzed using an example of disturbance on communications and observing the cascading effects on the power systems monitoring process. Specifically, the impact on the state estimation accuracy is investigated, as a result of a communication node failure, which is responsible for transferring Phasor Measurement Unit (PMU) data to the control center.

Impact of loss calculation on different loss allocation procedures

Transmission loss allocation is an important problem in markets where the clearing market procedure neglects transmission losses, such as the majority of European electricity markets. Different allocation methods have been proposed and implemented in different markets, and all these procedures are usually post market actions, which may imply an increase in financial volatility for both generators and consumers. Therefore, a more suitable approach would be to have both market clearing and transmission loss allocation on a real-time base. This paper discusses a simple but effective method for real-time estimation of transmission losses and performs a sensitivity analysis in applying this method in conjunction with different allocation methods according to different market designs.

Closure on “A GA-API Solution for the Economic Dispatch of Generation in Power System Operation”

The authors thank the commenters to the above-named article [ibid., vol. 27, no. 1, pp. 233¿242, Feb. 2012] and provide clarification for all the issues raised. The table numbering in the closure is the same as in the original article.