Underestimation Of Available Wind Power Research Articles

Optimal power flow using multi-objective glowworm swarm optimization algorithm in a wind energy integrated power system

ABSTRACTThis paper solves a multi-objective optimal power flow (MO-OPF) problem in a wind-thermal power system. Here, the power output from the wind energy generator (WEG) is considered as the schedulable, therefore, the wind power penetration limits can be determined by the system operator. The stochastic behavior of wind power and wind speed is modeled using the Weibull probability density function (PDF). In this paper, three objective functions, i.e., total generation cost, transmission losses, and voltage stability enhancement index are selected. The total generation cost minimization function includes the cost of power produced by the thermal and WEGs, costs due to over-estimation and the under-estimation of available wind power. Here, the single objective optimization problems are solved using the Glowworm Swarm Optimization (GSO) algorithm, whereas the MO-OPF problems are solved using the multi-objective GSO (MO-GSO) algorithm. The proposed optimization problem is solved on a modified IEEE 30 and 300 bus test systems with wind farms located at different buses in the system. The simulation results obtained show the suitability and effectiveness of proposed MO-OPF approach.

Multi-objective optimal power flow in the presence of intermittent renewable energy sources

This paper solves a multi-objective optimal power flow (MO-OPF) problem in a wind-thermal power system. Here, the power output from the wind energy generator (WEG) is considered as the schedulable, therefore the wind power penetration limits can be determined by the system operator. The stochastic behavior of wind power and wind speed is modeled using the Weibull probability density function. In this paper, three objective functions i.e., total generation cost, transmission losses and voltage stability enhancement index are selected. The total generation cost minimization function includes the cost of power produced by the thermal and WEGs, costs due to over-estimation and the under-estimation of available wind power. Here, the MO-OPF problems are solved using the multi-objective glowworm swarm optimiza-tion (MO-GSO) algorithm. The proposed optimization problem is solved on a modified IEEE 30 bus system with two wind farms located at two different buses in the system.

A novel gravitational acceleration enhanced particle swarm optimization algorithm for wind–thermal economic emission dispatch problem considering wind power availability

Abstract To reduce the pollutant atmospheric emission level, a Wind–thermal Economic Emission Dispatch (WTEED) model considering the coordination of power allocation from thermal and wind power generators is established. Among the model formulation, the fuel cost and emission level of thermal units and the operating cost caused by wind power availability are comprehensively investigated here. Also, the cost of wind energy including overestimation and underestimation of available wind power using Weibull-based probability density function is also given in a closed-form expression according to the incomplete gamma function to characterize the impact of wind power. To seek the optimum fuel cost, optimum emission level and best compromise solution, a newly developed optimization approach, known as gravitational acceleration enhanced particle swarm optimization algorithm (GAEPSO), has been adopted to solve the model in this work. The approach adopts co-evolutionary technique to simultaneously update particles velocity with PSO velocity and GSA acceleration and fully incorporates the ability of exploration in PSO and the ability of exploitation in GSA. GAEPSO, therefore, is expected to obtain an efficient balance between exploration and exploitation. The potential of the proposed algorithm is assessed in terms of the minimum fuel cost, minimum emission and best compromise solution obtained for conventional thermal generators and modified wind–thermal generators test systems. The results obtained validate the feasibility and effectiveness of the proposed algorithm compared to PSO, GSA and other recently developed approaches. Both the Pareto-optimal set and the convergence speed of the proposed algorithm are also found to be better than, or at least comparable to other algorithms.

Joint Energy and Spinning Reserve Market Clearing Incorporating Wind Power and Load Forecast Uncertainties

This paper proposes an energy and spinning reserve market clearing (ESRMC) mechanism for wind-thermal power system, considering uncertainties in wind power and load forecasts. Two different market models for the ESRMC are proposed. One model includes reserve offers from the conventional thermal generators, and the other includes reserve offers from both thermal generators and demand/consumers. The stochastic behavior of wind speed and wind power is represented by the Weibull probability density function (pdf), and that of the load is represented by a normal pdf. This paper considers two objectives: total cost minimization and the system-risk-level minimization. The first objective includes the cost of energy provided by thermal and wind generators, and the cost of reserves provided by thermal generators and loads. It also includes costs due to overestimation and underestimation of available wind power and load demand. The system risk level is considered as another objective as wind power is highly uncertain. Multiobjective Strength Pareto Evolutionary Algorithm 2+ (SPEA 2+) has been used to solve the ESRMC problem. The results of the IEEE 30 bus system demonstrate the utility of the proposed approach.

Environmental/economic power dispatch with wind power

Economic environmental dispatch (EED) is a significant optimization problem in electric power system. With more wide spread use of wind power, it is necessary to include wind energy conversion system (WECS) in the EED problem. This paper presents a model to solve the EED problem incorporating wind power. In addition to the classic EED factors, the factors accounting for overestimation and underestimation of available wind power in both economic and environmental aspects are also considered. In order to obtain some quantitative results, the uncertain characteristic of available wind power and the performance of WECS are determined on the basis of the statistical characteristic of wind speed. The optimization problem is numerically solved by a scenario involving two conventional generators and two wind-powered generators. The results demonstrate that the allocation of system generation capacity may be influenced by multipliers related to the cost for overestimation and underestimation of available wind power, and by the multiplier related to the emissions for underestimation of available wind power. Nevertheless, the multiplier related to the emissions for overestimation of available wind power has little impact on the allocation. Taking account of economic factors, environmental factors and impacts of wind power penetration, the proposed EED model is beneficial to finding the right balance between radical and conservative strategy for wind power development.

Multi-objective market clearing of electrical energy, spinning reserves and emission for wind-thermal power system

This paper proposes energy and spinning reserve market clearing mechanism for wind-thermal power system, including uncertainties in wind power generation and load demand forecasts. The impact of wind power and load demand volatility on the energy and spinning reserve market is taken into account. This paper considers reserve offers from the conventional thermal generators. The stochastic behavior of wind speed, and wind power is represented by Weibull probability density function (PDF), and the load demand uncertainty is represented by Normal PDF. This paper considers two objectives: energy and spinning reserves cost minimization, and emission minimization. The energy and spinning reserves cost minimization objective includes cost of energy provided by conventional thermal generators and wind generators, cost of reserves provided by conventional thermal generators. It also includes costs due to over-estimation and under-estimation of available wind power, and load demand. The proposed market clearing model provides a compromise solution by optimizing both the objectives simultaneously using multi-objective Strength Pareto Evolutionary Algorithm 2+ (SPEA 2+). The effectiveness of the proposed approach is established from the results on IEEE 30 bus system.

Energy and spinning reserve scheduling for a wind-thermal power system using CMA-ES with mean learning technique

The goal of the paper is to solve economic dispatch problem and to find optimal scheduling/allocation of energy and spinning reserves among the thermal and wind generators available to serve the demand. There is a considerable need for the alternative energy sources in the economic dispatch problem, hence wind energy generators are used. The stochastic behavior of wind speed and wind power is represented by Weibull probability density function. The total cost minimization objective considered in this paper includes cost of energy provided by conventional thermal generators and wind generators, cost of reserves provided by conventional thermal generators. It also includes costs due to over-estimation and under-estimation of available wind power. Covariant Matrix Adaptation with Evolution Strategy (CMA-ES) with mean learning technique (MLT) is used to solve the proposed economic dispatch problem for both conventional power system, and wind-thermal power system considering the provision for spinning reserves. In order to show the effectiveness and feasibility of the proposed frame work, various case studies are presented for two different test systems.

Probabilistic multiobjective wind-thermal economic emission dispatch based on point estimated method

In this paper, wind power generators are being incorporated in the multiobjective economic emission dispatch problem which minimizes wind-thermal electrical energy cost and emissions produced by fossil-fueled power plants, simultaneously. Large integration of wind energy sources necessitates an efficient model to cope with uncertainty arising from random wind variation. Hence, a multiobjective stochastic search algorithm based on 2 m point estimated method is implemented to analyze the probabilistic wind-thermal economic emission dispatch problem considering both overestimation and underestimation of available wind power. 2 m point estimated method handles the system uncertainties and renders the probability density function of desired variables efficiently. Moreover, a new population-based optimization algorithm called modified teaching-learning algorithm is proposed to determine the set of non-dominated optimal solutions. During the simulation, the set of non-dominated solutions are kept in an external memory (repository). Also, a fuzzy-based clustering technique is implemented to control the size of the repository. In order to select the best compromise solution from the repository, a niching mechanism is utilized such that the population will move toward a smaller search space in the Pareto-optimal front. In order to show the efficiency and feasibility of the proposed framework, three different test systems are represented as case studies.

An Economic Dispatch Model Incorporating Wind Power

In solving the electrical power systems economic dispatch (ED) problem, the goal is to find the optimal allocation of output power among the various generators available to serve the system load. With the continuing search for alternatives to conventional energy sources, it is necessary to include wind energy conversion system (WECS) generators in the ED problem. This paper develops a model to include the WECS in the ED problem, and in addition to the classic economic dispatch factors, factors to account for both overestimation and underestimation of available wind power are included. With the stochastic wind speed characterization based on the Weibull probability density function, the optimization problem is numerically solved for a scenario involving two conventional and two wind-powered generators. Optimal solutions are presented for various values of the input parameters, and these solutions demonstrate that the allocation of system generation capacity may be influenced by multipliers related to the risk of overestimation and to the cost of underestimation of available wind power.

An Economic Dispatch Model Incorporating Wind Power

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In solving the electrical power systems economic dispatch (ED) problem, the goal is to find the optimal allocation of output power among the various generators available to serve the system load. With the continuing search for alternatives to conventional energy sources, it is necessary to include wind energy conversion system (WECS) generators in the ED problem. This paper develops a model to include the WECS in the ED problem, and in addition to the classic economic dispatch factors, factors to account for both overestimation and underestimation of available wind power are included. With the stochastic wind speed characterization based on the Weibull probability density function, the optimization problem is numerically solved for a scenario involving two conventional and two wind-powered generators. Optimal solutions are presented for various values of the input parameters, and these solutions demonstrate that the allocation of system generation capacity may be influenced by multipliers related to the risk of overestimation and to the cost of underestimation of available wind power. </para>