Operation Planning Of Power Systems Research Articles

Hybrid Approach for Power System Operational Planning With Smart Grid and Small-Signal Stability Enhancement Considerations

Operational planning of power systems, especially in terms of overall reliability and security, is a key issue in the smart grid development. Hence, it is necessary to develop new strategies to cope with increasing uncertainties arising from the fast changing ways power systems are being operated. This paper presents a comprehensive approach to determine an optimal transmission network expansion plan considering the enhancement of small-signal stability through wide-scale deployment of the existing and planned transmission system assets. The dynamic model of the transmission network operational planning (TNOP) is solved based on a combination of the Mean-Variance Mapping Optimization (MVMO), and the classic dynamic programming method embedded with a heuristic procedure. Besides, a probabilistic eigenanalysis-based recursive method is proposed to determine the optimal control strategies that are highly relevant to the enhancement of the system small-signal stability performance throughout the planning horizon. Numerical results demonstrate the viewpoint and the effectiveness of the proposed approach in providing optimal strategies of minimum cost while avoiding the instability risk associated to poorly damped low-frequency electromechanical oscillations.

A comparative study on particle swarm optimisation algorithms for economic dispatch with multiple fuels

Economic dispatch (ED) is one of the essential functions in the operational planning of power systems that is to determine the optimum scheduling of generation at a particular time that minimises the total production cost while satisfying the operating constraints. A number of computation techniques have progressively been proposed to solve this important issue. One of them is a particle swarm optimisation (PSO), which belongs to the evolutionary computation techniques, and shows superiority to other evolutionary computation techniques in terms of less computation time, easy implementation with high quality solution, stable convergence characteristic and independent from initialisation. Accordingly, this paper presents the three traditional PSO algorithms to solve the ED problem with smooth cost function and multiple fuels. Furthermore, the comparative study on the three PSO algorithms have been implemented so as to investigate the ability among the PSO algorithms in terms of solution quality, frequency of convergence, convergence characteristic, as well as diversity characteristic. The simulation results are also compared with those obtained by the state-of-the-art algorithms.

A new memetic algorithm approach for the price based unit commitment problem

Unit commitment (UC) is a very important optimization task, which plays a major role in the daily operation planning of electric power systems that is why UC is a core research topic attracting a lot of research efforts. An innovative method based on an advanced memetic algorithm (MA) for the solution of price based unit commitment (PBUC) problem is proposed. The main contributions of this paper are: (i) an innovative two-level tournament selection, (ii) a new multiple window crossover, (iii) a novel window in window mutation operator, (iv) an innovative local search scheme called elite mutation, (v) new population initialization algorithm that is specific to PBUC problem, and (vi) new PBUC test systems including ramp up and ramp down constraints so as to provide new PBUC benchmarks for future research. The innovative two-level tournament selection mechanism contributes to the reduction of the required CPU time. The method has been applied to systems of up to 110 units and the results show that the proposed memetic algorithm is superior to other methods since it finds the optimal solution with a high success rate and within a reasonable execution time.

Short-term scheduling solved with a particle swarm optimiser

Unit commitment problem is an important optimising task in daily operational planning of power systems which can be mathematically formulated as a large scale non-linear mixed-integer minimisation problem for which there is no exact solution technique. The solution to the problem can be obtained only by complete enumeration, often at a prohibitively computation time requirement for realistic power systems. This research discusses the planning of short-term scheduling on thermal power systems through the particle swarm optimisation technique. The formulation of the problem considers the operating costs of the units, the minimum up and down times, the spinning reserve and the power balance. A methodology is presented, and a computational tool is developed to solve the problem of unit commitment, which handles the problem constraints with correction operators. In the research also is proposed a new way to update the swarm individuals and a mutation stage of the best solution. The method is applied to standard systems and the results are compared with the ones previously reported in the literature. The results obtained by applying the proposed algorithm to the Chile Large Northern Interconnected System are also discussed.

Optimal scenario tree reduction for stochastic streamflows in power generation planning problems

The mid-term operation planning of hydro-thermal power systems needs a large number of synthetic sequences to represent accurately stochastic streamflows. These sequences are generated by a periodic autoregressive model. If the number of synthetic sequences is too big, the optimization planning problem may be too difficult to solve. To select a small set of sequences representing the stochastic process well enough, this work employs two variants of the Scenario Optimal Reduction technique. The first variant applies such a technique at the last stage of a tree defined a priori for the whole planning horizon while the second variant combines a stage-wise reduction, preserving the periodic autoregressive structure, with resampling. Both approaches are assessed numerically on hydrological sequences generated for real configurations of the Brazilian power system.

Development of Bayesian belief network model for electrical load demand

Load forecasting is significantly important for the electricity production in the deregulated economy. It has many applications including energy purchasing and generation, load switching, contract evaluation, and infrastructure development. A large variety of mathematical methods have been developed for load forecasting. This paper presents a load forecast methodology using the Bayesian Belief Network (BBN) that is suitable for power system operational planning studies. Bayesian Belief Network is a powerful tool for combining different knowledge sources with various degrees of uncertainty in a mathematical sound and computationally efficient way. In this paper bayesian estimation is used for predicting maximum load growth per year.

Stochastic Dynamic Programming Applied to Hydrothermal Power Systems Operation Planning Based on the Convex Hull Algorithm

This paper presents a new approach for the expected cost-to-go functions modeling used in the stochastic dynamic programming (SDP) algorithm. The SDP technique is applied to the long-term operation planning of electrical power systems. Using state space discretization, the Convex Hull algorithm is used for constructing a series of hyperplanes that composes a convex set. These planes represent a piecewise linear approximation for the expected cost-to-go functions. The mean operational costs for using the proposed methodology were compared with those from the deterministic dual dynamic problem in a case study, considering a single inflow scenario. This sensitivity analysis shows the convergence of both methods and is used to determine the minimum discretization level. Additionally, the applicability of the proposed methodology for two hydroplants in a cascade is demonstrated. With proper adaptations, this work can be extended to a complete hydrothermal system.

The impact of water quality as an environmental constraint on operation planning of a hydro-thermal power system

This work presents the development of a model that applies water quality restriction to the long term planning hydro-thermal power system operation. Taking water quality into account in the long term planning, guarantees the multiple water usage. The objective is to investigate the sensibility of the long term planning hydro-thermal power system to the environmental variables focusing water quality. Among water quality indicators the option was chlorophyll- a and the nutrient phosphorus establishing a relation with the reservoir storage.

Load Shedding Strategies Using Optimal Load Flow With Relaxation of Restrictions

This paper proposes an optimization model to minimize the load curtailments necessary to restore the equilibrium of operating point with relaxation of restrictions. This model can be used for the operation planning of electrical power systems. The developed methodology minimizes the load cuts, limiting them to their importance and establishing different stages for them through the relaxation of the minimum limits of voltage and maximum limits of active power flows through the transformers. For instance, when an emergency occurs, if some operational limits are violated for a short period, fewer loads can be cut at a first stage than would be necessary if those violations would not be admitted. If the emergency does not cease, the remaining cuts necessary to recover the violations are made as a second stage of the strategy. On the other hand, if the contingency ceases, within this break, the system is recovered, and at this second stage, unnecessary cuts can be avoided, admitting only small breakings of operational restrictions. This methodology was tested at a system of 291 buses that is equivalent, in heavy load, to the electric network of the State of Parana in Brazil.

Pareto optimality-based multi-objective transmission planning considering transmission congestion

In the deregulated environment, transmission congestion is one major problem that needs to be handled in power system operation and network expansion planning. This paper aims to enhance the transmission system capability and have the congestion alleviated using the multi-objective transmission expansion planning (MOTEP) approach. A system congestion index called the congestion surplus is presented to measure the congestion degree of the transmission system. The proposed MOTEP approach optimizes three objectives simultaneously, namely the congestion surplus, investment cost and power outage cost. An improved strength Pareto evolutionary algorithm (SPEA) is adopted to solve the proposed model. A ranking method based on Euclidean distance is presented for decision-making in the Pareto-optimal set. The effectiveness of both the improved SPEA and the proposed multi-objective planning approach has been tested and proven on the 18-bus system and the 77-bus system, respectively.

Inclusão de restrições dinâmicas no problema de planejamento de potência reativa

Este artigo apresenta uma nova metodologia para o planejamento da operação dos Sistemas Elétricos de Potência (SEP), na qual restrições dinâmicas de impactos em geradores são incorporadas ao problema de Fluxo de Potência Ótimo (FPO). Neste novo modelo, o transitório no instante da contingência é incorporado ao FPO como uma restrição operacional a fim de manter os impactos nos eixos dos geradores nos níveis recomendados pelo Operador Nacional do Sistema (ONS). Desta forma o tradicional problema de planejamento de potencia reativa incorpora os impactos nos eixos dos geradores do SEP quando ocorre uma contingência. O problema é formulado usando a metodologia primal-dual de pontos interiores associada à técnica de decomposição matemática de Benders. Estudos de casos com os sistemas IEEE-14 e IEEE-118 são realizados para mostrar o desempenho da metodologia proposta.

AN ADVANCED RADIAL BASE STRUCTURE FOR WIND POWER FORECASTING1

This article presents an advanced Wind Power Forecasting method based on applications of artificial intelligence techniques. The method belongs to the statistical methods. It requires as input past wind power measurements and meteorological forecasts of wind speed and direction interpolated in the site of the wind farm. A self-organized map is trained to classify the forecasted local wind speed provided by the meteorological services. For each class of wind speed, a radial basis neural network is used to predict the windfarm power output. Different radial basis networks are applied for short-term and for long-term prediction. The proposed model is suitable for operational planning of power systems with increased wind power penetration, i.e. 48 hours and for Wind farm operators trading in electricity markets. Application of the forecasting method on the power production of actual wind farms shows the validity of the method.

An Advanced Statistical Method for Wind Power Forecasting

This paper presents an advanced statistical method for wind power forecasting based on artificial intelligence techniques. The method requires as input past power measurements and meteorological forecasts of wind speed and direction interpolated at the site of the wind farm. A self-organized map is trained to classify the forecasted local wind speed provided by the meteorological services. A unique feature of the method is that following a preliminary wind power prediction, it provides an estimation of the quality of the meteorological forecasts that is subsequently used to improve predictions. The proposed method is suitable for operational planning of power systems with increased wind power penetration, i.e., forecasting horizon of 48 h ahead and for wind farm operators trading in electricity markets. Application of the forecasting method on the power production of an actual wind farm shows the validity of the method

Sensitivity Analysis Applied on the Electrical Power Systems Operation Planning

This paper proposes a study of electrical power system operation planning by Sensitivity Analysis (SA). The SA implemented was oriented by Optimal Power Flow (OPF). An optimal operation point was obtained by OPF. If some perturbation occurs in the power system, a new operation point will have to be evaluated to keep the feasibility of the power grid. This new operation point will be evaluated by SA. The SA was methodologically based in the theorem proposed by Fiacco. The set of non-linear equations generated by SA was solved by Newton´s method. Sparsity techniques were used. The principals contributions of this paper are the inclusion of transmission line constrained in the SA model and the complete use of the Fiacco´s theorem. Studies of cases were carried out in the IEEE 14, 118 and 300 buses, where the efficiency of the SA was verified.

ANT COLONY OPTIMIZATION FOR ACTIVE/REACTIVE OPERATIONAL PLANNING

This paper proposes the application of Ant Colony Optimization (ACO) for active/reactive operational planning of power systems. The ACO is a newly developed method belonging to the class of evolutionary computation methods inspired from real ants life. Specifically, ACO algorithm aims to determine the optimal settings of control variables, such as generator outputs, generator voltages, transformer taps and shunt VAR compensation devices, considered as nodes of an Ant-System (AS) graph. Results are compared to those given by Simulated Annealing for the IEEE 30-bus test system, exhibiting superior performance.

OPTIMAL PLANNING OF GENERATING UNITS IN POWER SYSTEM CONSIDERING UNCERTAINTY OF INFORMATION

The task of optimal planning of generating units (OPGU) for optimal operation and expansive planning of all-thermal power system is considered in this paper. The OPGU problem is dealt with as a two-stage problem. During the first stage the optimal dispatching of units when the unit commitment is given takes place. During the second stage the optimal combination of units considering optimal dispatching of units on the first stage will be determined. Taking into consideration the incompleteness of information, besides the deterministic models of OPGU the min-max models will be discussed. Also some procedures of solving the tasks of OPGU and illustrative examples are presented.

Improved modelling of external networks in operation and operational planning of interconnected power systems

During online operation and for operational planning the European transmission system operators have detailed information about current and future states of their own network. The interaction with foreign networks is described in reduced form by network equivalents. Increasing international bulk power trade in the competitive market leads to stronger network utilisation, especially near international interconnections. Therefore the accuracy of foreign network models must be increased to maintain network security.

Energetic Operation Planning Using Genetic Algorithms

This paper investigates the application of genetic algorithms to optimize large, nonlinear complex systems, particularly the optimization of the operation planning of hydrothermal power systems. Several of the current studies to solve this kind of problem are based on nonlinear programming. This approach presents some deficiencies, such as difficult convergence, oversimplification of the original problem, or difficulties related to the objective function approximation. Aiming to find more efficient solutions for this class of problems, this paper proposes and investigates the use of a genetic approach. The characteristics of the genetic algorithms, such as simplicity, parallelism, and generality, can provide an effective solution to these problems. The paper presents the adaptation of the technique and an actual application on the optimization of the operation planning for a cascade system composed by interconnected hydroelectric plants

An evolution strategy for short-term operation planning of hydrothermal power systems

This paper reports on a new approach to solve the short-term operation planning problem of hydrothermal power systems. It uses evolution strategies to determine the optimal schedules of all hydroelectric and thermal power plants in a closed optimization. A decomposition of the optimization problem is not necessary. Numerical experiments show, that the proposed method is able to solve the complex nonlinear optimization problem with its wealth of constraints in acceptable computation time.

Risk due to load forecast uncertainty in short term power system planning

This paper presents a methodology to analyze the risk of short term power system operational planning in the presence of electrical load forecast uncertainty. As the authors' methodology requires an estimate of the load forecast variance, a Bayesian load forecaster is used in the practical implementation. They express their results as a function of forecast lead time from one to five days into the future, in terms of $/MWH. The risk due to load forecast uncertainty is based on the forecast variance, and found by determining the expected cost of perfect information. They illustrate their risk evaluation method in a case study with utility-derived power system data and temperature forecast data from the US National Weather Service.