Design and analysis of multi-source multi-area deregulated power system for automatic generation control using quasi-oppositional harmony search algorithm

The present article focuses on the study of automatic generation control (AGC) of a realistic power system having a distinct combination of multi-area multi-source generating units in each control area under deregulated framework. An attempt is made in this paper to integrate reheat thermal, hydro and gas generating unit in a single control area and, then, extended this combination to five control areas. In this work, six reheat thermal, six hydro and three gas generating units are taken into account for the modeling of five-area power system. Some important physical constraints like time delay, governor dead band and generation rate constraint are imposed in the power system dynamics to get an accurate perception of the deregulated AGC subject. The highlighting features of the present work are to model, simulate, optimize and co-relate their inter-related dynamic performances for the purpose of AGC study. For such a complex AGC model, the vital role of the proposed quasi-oppositional harmony search (QOHS) algorithm, as an optimizing tool, is signified while solving the AGC problem in deregulated regime. The simplicity of the structure and acceptability of the responses of the well-known proportional–integral–derivative controller, inherently, enforces to employ in this work. The three classes of extensive deregulated cases (in the presence of load following and physical constraints) are demonstrated by examining the closed loop performance of the studied model. The simulation results show that the designed power system model may be a feasible one and the proposed QOHS algorithm may be a promising optimization technique under these circumstances.     

Cost impact of dynamically managing generation reserves

Generation reserves are needed to maintain the real time balance between power supply and power demand. Because power is noninventoriable, power generation follows power demand. Demand for power varies considerably depending on the time of day, day of the week and season. The predictable portion of power demand is met by purchasing firm energy on a day ahead or real time market. The random unpredictable portion of demand is met by purchasing a set of online and offline generation reserves on an ancillary market. The total energy purchasing cost includes payments for firm energy and payments for generation reserves. The latter include fixed capacity payments for reserve generation capacities and variable payments for the random energy produced from these reserves. The main contribution of this paper is to present an optimization model that captures the dynamism in the selection of the dispatch interval to determine the amount of firm energy and reserve capacities given a set of market prices. This is done by explicitly including in the model the duration of the dispatch period and the frequency this decision is reevaluated. In this model the randomness of the demand is captured by using a Doubly Truncated Normal Distribution. The cost incurred to activate generation reserves is modeled as a Poisson process. The total model captures the price differences from using different reserve sources. An empirical example is presented to illustrate the cost benefits of using the method proposed in this research with two different strategies: a static strategy and a dynamic strategy. It is shown that dynamically setting generation reserves results in cost savings.     

Prediction of reserve requirements in generation planning

An effective load-carrying capability prediction model is developed which preserves a linear structure of the generation planning problem. By this means, optimum investment plans can be found which link economic decisions to measures of system reliability such as the loss of load probability index. An example is described in full which compares investment decision and reliability analyses for this capability prediction method with corresponding results from the fixed reserve margin approach commonly used.     

Impact of correlation on reserve requirements of high wind-penetrated power systems

This paper investigates the effects of two important factors, i.e., correlation of wind farms output with load and wind speed coincidence in determining the required static reserve in a high wind-penetrated power system. To this end, it suggests an analytical approach to involve the effects of these two factors in probabilistic analytical multi-state models of wind farms output generation. Based on an optimization framework with the objective of reaching the desirable level of correlation, the key idea is to calculate some joint probabilities for equivalent models of wind farms. As a result, these models become compatible with the ones for conventional generation units in adequacy studies of power systems. The proposed analytical approach, then, continues with evaluating the reliability level of power systems exactly and also practically employing these multi-state models once dealing with wind farms at multiple locations. The proposed analytical approach is applied to the modified IEEE-RTS and the obtained results and discussions offered demonstrate the unavoidable effects of these important factors (load and wind speed coincidence) in real world applications.     

Analysis of interruptible electric power in deregulated power systems

Assuring appropriate levels of generation capacity adequacy at the minimum possible cost using market-based approaches is a contemporary issue attracting much attention in deregulated power systems. Introduction of interruptible electricity contracts could provide a possible solution to such a problem. However, theoretical frameworks for examining the impacts of these contractual arrangements on system operations have not been maturely developed. This paper presents a probabilistic production simulation based framework for modeling and analyzing interruptible electricity contracts. The interdependence between the pricing models of interruptible electric power and the operations of the whole system is addressed and solved by an iterative coordinating algorithm. The probabilistic production simulation framework allows the uncertainties inherent in both the forced outages of generators and system load demand at the contract delivery time to be taken into account. The proposed approach is capable of achieving comprehensive analysis and systematic pricing for interruptible electric power. A numerical case study is presented to illustrate the effectiveness and validity of the methodology.     

Scenario selection in composite reliability assessment of deregulated power systems

Power market analysis should be incorporated in reliability assessments of deregulated power systems. For the Nordic power system, this is done by using The Multi-area Power-market Simulator (EMPS) for long-term power market analysis, where EMPS finds the optimal socio-economic dispatch on a weekly basis, with respect to, e.g., hydro reservoir levels. The EMPS analysis results in a set of load and generation scenarios, and these scenarios are interpreted as a sample of future power market behaviour, and is used as basis for a reliability assessment. These load and generation scenarios are referred to as power market scenarios.The power market analysis produces a large number of power market scenarios, and to include all these scenarios in a reliability assessment results in excessive computation time. The scenario selection method is presented and discussed. Scenario selection is used to pick out a subset of the generated power market scenarios, to only use this subset of scenarios as a basis for the reliability assessment. The paper provides some general guidelines for application of the scenario selection method. It is shown that the scenario selection method can reduce the scenario set by about 90%, with little loss of accuracy in the reliability assessment.     

Optimization of biomass fuelled systems for distributed power generation using Particle Swarm Optimization

With sufficient territory and abundant biomass resources Spain appears to have suitable conditions to develop biomass utilization technologies. As an important decentralized power technology, biomass gasification and power generation has a potential market in making use of biomass wastes. This paper addresses biomass fuelled generation of electricity in the specific aspect of finding the best location and the supply area of the electric generation plant for three alternative technologies (gas motor, gas turbine and fuel cell-microturbine hybrid power cycle), taking into account the variables involved in the problem, such as the local distribution of biomass resources, transportation costs, distance to existing electric lines, etc. For each technology, not only optimal location and supply area of the biomass plant, but also net present value and generated electric power are determined by an own binary variant of Particle Swarm Optimization (PSO). According to the values derived from the optimization algorithm, the most profitable technology can be chosen. Computer simulations show the good performance of the proposed binary PSO algorithm to optimize biomass fuelled systems for distributed power generation.     

软交换平台在下一代电力通信网络中的应用

软交换平台是下一代网络的核心功能实体之一,为下一代网络提供具有实时性要求的业务呼叫控制和连接控制功能。文章介绍了下一代网络的基本概念,重点论述了软交换平台的体系结构、以软交换技术为基础的下一代交换网络的电力发展策略,以及在下一代电力通信网络中的重要应用,强调了下一代网络将成为未来几年电力通信发展和建设的重要方向。     

Interval type-2 fuzzy modeling of wind power generation in Genco's generation scheduling

Wind power is a promising source of electric power generation since it has tremendous environmental and social benefits. The generation scheduling (GS) problem encounters several uncertainties in terms of the system's parameters such as load, reserve and available wind power generation. The modeling of those uncertainties is an important issue in power system scheduling. A fuzzy based modeling approach can be used to develop the generation schedule under an uncertain environment.In this paper, the type-2 fuzzy membership function (MF) is implemented to model the linguistic uncertainty of type-1 MF of available wind power generation which stems from opinions of different experts. The proposed approach is applied to two test systems (six and twenty-six conventional generating units both with two wind farms) and the results of generation scheduling using both fuzzy modeling type-1, and type-2 are presented. These results demonstrate that the advantage of using the type-2 fuzzy to model the linguistic uncertainty of the type-1 MF. This paper demonstrates how unit scheduling in an uncertain environment of type-1 fuzzy MF modeling can be performed just by using a single type-2 fuzzy MF when all type-1 MF were in the footprint of uncertainty (FOU) of type-2 MF.     

Evaluation of the operating reserve in hydrothermal generation systems

The demand for an accurate evaluation of power reserves has become very important especially since the installation of large generating units such as nuclear power plants. The features of the power reserve and its partition into non-spinning and spinning components are discussed. Attention is paid to the regulating margin of the generating units and to the problem of evaluating the magnitude and duration of the operating reserve in a hydrothermal generating system under uncertain conditions.Practical examples are given to illustrate evaluation of the fast and slow operating reserves for the hydro and thermal subsystems of the Egyptian Unified Power System. The relation between the uncertainty in the amount of the required operating reserve on the one hand, and the amount of statistical data and size of added generating units on the other are discussed.     

Automatic generation control of power system using a novel quasi-oppositional harmony search algorithm

The present work approaches a novel quasi-oppositional harmony search (QOHS) algorithm, as an optimization technique, for its optimum performance in the subject area of automatic generation control (AGC) of power system. The proposed QOHS algorithm is applied with an aim to converge rapidly towards the optimal solution(s) that houses both the characters of two guesses, i.e. opposite-point and quasi-opposite point. The area of concern of this study is to discuss the multi-objective problems of an interconnected power system for the benefits of AGC. The proposed QOHS algorithm is, individually, applied to single-area, precede to two-area considering the non-linearity effects of governor dead band and generation rate constraint and, finally, extended to four-area power system showing the consequences of multiple load disturbances. A case of robustness and stability analysis are also investigated for the studied two-area power system model. The control strategy, for the dynamic power system model, is based on area control error. The simplicity of the structure and acceptability responses of the well-known proportional–integral–derivative controller enforces to implement as a controller in this work. The comparative evaluation of the proposed QOHS algorithm is carried out by the way of comparing the dynamic performances of the studied power system model with those offered by other algorithms reported in the recent state-of-the-art literature. The simulation works, presented in the paper, reveal that the proposed QOHS algorithm may be effectively utilized for the purpose of AGC study of power system having varying degrees of complexities and non-linearities. Moreover, the proposed QOHS based control strategy adopted in this work provides a robust and stable speed control mechanism.     

电力市场中基于序贯仿真的用户电价研究

在厂网分开的电力市场中，用户电价不仪与电厂报价和电网支出有关，还与系统的可靠水平有关。电网运行方式、负荷水平、设备的检修和随机故障，都对用户电价和电网收益产生重要影响。本文采用时间序列负荷模型，在电网运行中考虑了运行方式、负荷水平和设备停运，采用序贯仿真模拟电网的长期运行，建立优化调度策略，引入停电赔偿电价和峰谷电价，计算相应的收支费用。在IEEE-14测试系统的计算结果表明，算法反映了安全因素和经济因素的关系，有助于建立电力市场中合理的用户电价体系。     

Impact of high solar and wind penetrations and different reliability targets on dynamic operating reserves in electricity generation expansion planning

Wind and solar are increasingly cost-competitive as well as environmentally less harmful alternatives to the fossil-fuel generation that dominates most electricity industries. However, their highly variable and somewhat unpredictable output still requires high levels of dispatchable plants to ensure demand can be met at times of low renewables availability. While this capacity overhead has associated costs, it does offer potentially useful outcomes for dynamic operating reserves. We present a method for assessing these potential outcomes in electricity industry planning. We use an evolutionary programming-based capacity expansion model, NEMO, that solves least-cost generation mixes through full operational dispatch of candidate solutions, using high-temporal resolution demand and wind and solar profiles, over a year or more. We apply our method through a case study of the Java-Bali grid, considering future scenarios both with and without variable renewables, and under different carbon pricing scenarios, reliability targets, and minimum operating reserves requirements. Our study suggests that not only might high renewable penetrations reduce industry costs and emissions, their inclusion provides significantly higher operating reserves over most of the year, hence the ability to cover unexpected plant failures and other disruptions. Lower reliability targets reduce this capacity overhang but still see improved operating reserves.     

Probabilistic evaluation of reserve requirements of generating systems with renewable power sources: The Portuguese and Spanish cases

This paper presents an application of probabilistic methodologies to evaluate the reserve requirements of generating systems with a large penetration of renewable energy sources. The idea is to investigate the behavior of reliability indices, including those from the well-being analysis, when the major portion of the renewable sources comes from wind power and other intermittent sources. A new simulation process to address operating reserve adequacy is introduced, and the correspondent reliability indices are observed. Case studies on the Portuguese and Spanish generating systems are presented and discussed.     

Load frequency control of power system under deregulated environment using optimal firefly algorithm

In this paper, a novel Firefly Algorithm (FA) optimized hybrid fuzzy PID controller with derivative Filter (PIDF) is proposed for Load Frequency Control (LFC) of multi area multi source system under deregulated environment by considering the physical constraints such as Generation Rate Constraint (GRC) and Governor Dead Band (GDB) nonlinearity. As the effectiveness of FA depends on algorithm control parameters such as randomization, attractiveness, absorption coefficient and number of fireflies are systematically investigated, the control parameters of FA are tuned by carrying out multiple runs of algorithm for each control parameter variation then the best FA control parameters are suggested. Additionally, the superiority of the FA is demonstrated by comparing the results with tuned Genetic Algorithm (GA). To investigate the effectiveness of the proposed approach, time domain simulations are carried out considering different contracted scenarios and the comparative results are presented. Further, sensitivity analysis is performed by varying the system parameters and operating load conditions. It is observed from the simulation results that the designed controllers are robust and the optimum gains of proposed controller need not be reset even if the system is subjected to wide variation in loading condition and system parameters. Finally, the effectiveness of the proposed control scheme is evaluated under random step load disturbance.     

A novel hybrid PSO-PS optimized fuzzy PI controller for AGC in multi area interconnected power systems

In this paper, a novel hybrid Particle Swarm Optimization (PSO) and Pattern Search (PS) optimized fuzzy PI controller is proposed for Automatic Generation Control (AGC) of multi area power systems. Initially a two area non-reheat thermal system is used and the gains of the fuzzy PI controller are optimized employing a hybrid PSO and PS (hPSO-PS) optimization technique. The superiority of the proposed fuzzy PI controller has been shown by comparing the results with Bacteria Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA), conventional Ziegler Nichols (ZN), Differential Evolution (DE) and hybrid BFOA and PSO based PI controllers for the same interconnected power system. Additionally, the proposed approach is further extended to multi source multi area hydro thermal power system with/without HVDC link. The superiority of the proposed approach is shown by comparing the results with some recently published approaches such as ZN tuned PI, Variable Structure System (VSS) based ZN tuned PI, GA tuned PI, VSS based GA tuned PI, Fuzzy Gain Scheduling (FGS) and VSS based FGS for the identical power systems. Further, sensitivity analysis is carried out which demonstrates the ability of the proposed approach to wide changes in system parameters, size and position of step load perturbation The proposed approach is also extended to a non-linear power system model by considering the effect of governor dead band non-linearity and the superiority of the proposed approach is shown by comparing the results of hybrid BFO-PSO and craziness based PSO approach for the identical interconnected power system. Finally, the study is extended to a three area system considering both thermal and hydro units with different controllers in each area and the results are compared with hybrid BFO-PSO and ANFIS approaches.     

Scheduling of generation in deficit power systems

Scheduling studies are carried out on a deficit hydrothermal power system. The problem is formulated as one of minimizing the severity of power cuts. The maintenance schedule of the thermal plants is selected by minimizing its deviation from an ideal, unrealizable schedule. The thermal plants are production-scheduled on the basis of their maintenance schedule. The influence of the stochastic nature of the load and the hydro inflows on the scheduling is evaluated. The hydro plants are scheduled through the extension of the techniques of the surplus case. Illustrative studies on an actual deficit hydro-thermal system are described     

Evaluation of costs for procuring reserve capacity in a deregulated power system using multi‐agent model

In this paper, we assume 2 models for securing reserve capacity. One is “Commitment‐based Security Model” and the other is “Reserve Market‐based Security Model.” In Commitment‐based security model, ISO commits procurement of reserve energy to a particular generation company. Meanwhile, in Reserve market‐based security model, ISO procures reserve energy through reserve market. The main object of this research is to investigate which model will be preferable for the viewpoint of consumer's cost. To compare these models, two things are considered in this paper. One is bidding behavior of agents which bids to energy market and reserve market. To consider this, Q‐Learning of multi‐agent model is used. Also, the Unit Commitment (UC) is considered to calculate generation cost. This is to calculate the cost for securing reserve power more precisely. © 2009 Wiley Periodicals, Inc. Electr Eng Jpn, 167(1): 18– 25, 2009; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20705     

A hybrid firefly algorithm and pattern search technique for automatic generation control of multi area power systems

In this paper, a novel hybrid Firefly Algorithm and Pattern Search (hFA–PS) technique is proposed for Automatic Generation Control (AGC) of multi-area power systems with the consideration of Generation Rate Constraint (GRC). Initially a two area non-reheat thermal system with Proportional Integral Derivative (PID) controller is considered and the parameters of PID controllers are optimized by Firefly Algorithm (FA) employing an Integral Time multiply Absolute Error (ITAE) objective function. Pattern Search (PS) is then employed to fine tune the best solution provided by FA. The superiority of the proposed hFA–PS based PID controller has been demonstrated by comparing the results with some recently published modern heuristic optimization techniques such as Bacteria Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA) and conventional Ziegler Nichols (ZN) based PI/PID controllers for the same interconnected power system. Furthermore, sensitivity analysis is performed to show the robustness of the optimized controller parameters by varying the system parameters and operating load conditions from their nominal values. Finally, the proposed approach is extended to multi area multi source hydro thermal power system with/without considering the effect of physical constraints such as time delay, reheat turbine, GRC, and Governor Dead Band (GDB) nonlinearity. The controller parameters of each area are optimized under normal and varied conditions using proposed hFA–PS technique. It is observed that the proposed technique is able to handle nonlinearity and physical constraints in the system model.     

Demand and generation cost uncertainty modelling in power system optimization studies

This paper describes the formulations and the solution algorithms developed to include uncertainties in the generation cost function and in the demand on DC OPF studies. The uncertainties are modelled by trapezoidal fuzzy numbers and the solution algorithms are based on multiparametric linear programming techniques. These models are a development of an initial formulation detailed in several publications co-authored by the second author of this paper. Now, we developed a more complete model and a more accurate solution algorithm in the sense that it is now possible to capture the widest possible range of values of the output variables reflecting both demand and generation cost uncertainties. On the other hand, when modelling simultaneously demand and generation cost uncertainties, we are representing in a more realistic way the volatility that is currently inherent to power systems. Finally, the paper includes a case study to illustrate the application of these models based on the IEEE 24 bus test system.