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.     

Wind power system state estimation with automatic differentiation technique

A wind power system state estimation method based on automatic differentiation technique is presented in this paper. A simplified RX model of wind turbine is introduced and used to be equivalent to wind power generator. As one of the state variables, the slip of wind power generator is introduced into state estimation process and the corresponding mathematical model is established. The proposed method takes into account the own characters of asynchronous generator and can easily combine with the original state estimation program. Furthermore, unlike the traditional method which needs to deduce all the formulas of derivatives and code them by hand, the Jacobian matrix is built automatically using automatic differentiation with sparse technique in the proposed method, the truncation error is avoided effectively, and the computational efficiency, speed and precision can be significantly improved. This paper demonstrates that the burdens of software developers are relieved greatly by automatic differentiation technique instead of hand-coded derivatives and the simplified RX model instead of the traditional RX model. Finally, it follows from the simulation results that the proposed method possesses good performance and can be well applied to state estimation for power system containing wind power generators.     

Insertion of wind generators in electrical power systems aimed at active losses reduction using sensitivity analysis

The study evaluates the electrical power system behaviour when wind turbines are inserted into the power grid. The assessment is made using a sensitivity analysis technique applied to the power flow solution. Unlike the typical algorithms, the sensitivity analysis technique does not require an iterative process, resulting in a fast method with great precision. This proposed method make easy to check the wind turbine behaviour to the changing of wind speed. Initially, the power flow solution is obtained and identified as the base case. When there are perturbations in the generators, the new solution is obtained directly by sensitivity analysis technique. The technique was applied in 34-bus, 70-bus and 126-bus test distribution system. The places chosen to connect the wind turbines were determined by the Incremental Transmission Losses method. The results demonstrate the effectiveness of the methodology. When wind turbines are inserted in the studied systems, active and reactive losses are reduced and voltage profile is improved.     

Towards reactive power dispatch within a wind farm using hybrid PSO

With the increasing penetration of wind power generation, the stringent grid codes are imposed by the system operators insisting the wind turbines to behave similar to that of synchronous generators. As a part of fulfilling the grid code in terms of steady state reactive power injection at point of common coupling, in this research work, the problem of optimal reactive power dispatch within a wind farm is formulated and a hybrid particle swarm optimization algorithm is proposed to get the optimal solution. The effectiveness of the proposed method is tested by simulations of the power collection grid of practical off-shore wind farm. The test results are compared with interior-point method based non-linear constrained optimization tool, fmincon, in Matlab. The analysis is carried out considering with and without wake effects on the wind farm at various grid bus voltage conditions. The test results demonstrate the effectiveness of the proposed method in achieving optimal solution for various operating conditions.     

Stochastic mid-term generation scheduling incorporated with wind power

A challenge now facing system operator is how to schedule optimally the generation units in a wind integrated power system over a one year time horizon considering the effects of wind forecasting and variability; also, regarding the effects of load uncertainty. By the same token, this paper first develops a new formulation for Stochastic Mid-term Generation Scheduling (SMGS). In the formulation, 2m + 1 point estimate method is developed to accurately estimate the output variables of Mid-term Generation Scheduling (MGS) problem. Then, the formulation is combined with adaptive modified gravitational search algorithm and a novel self-adaptive wavelet mutation strategy for the establishment of new robust algorithm for the present problem. It is noteworthy to say that the classical methods considered certain wind information in the deterministic solution of the MGS problem which is not the realistic approach. However, this study improves modeling of wind–thermal system in the MGS problem by considering possible uncertainties when scheduling the generators of power system. The proposed model is capable of taking uncertainty of load and wind into account. The proposed method is applied on two test cases and the numerical results confirmed the efficiency and stability of the proposed algorithm.     

Disconnection control of wind power generators for the purpose of reducing frequency fluctuation

This paper proposes a new approach for disconnection control of wind power generators to reduce the frequency fluctuation caused by wind power. The approach is based on the correlation between frequency and total output of wind power generators and the turbulence degrees of individual wind power generators. At a control center, frequency and all the outputs of wind power generators are monitored, and the correlation and the turbulence degrees are computed on‐line. If the large variation of frequency is detected, then whether the variation comes from the wind power generators or loads is checked by using the correlation. When the wind power generators cause the frequency variation, the wind power generator with maximum turbulence degree is searched and disconnected from the power systems. This search and disconnection process is repeated until the correlation reaches the level where the frequency variation observed is not caused by the wind power generators. The effectiveness of the proposed approach was confirmed with the simulation studies of a two‐area interconnected power system including many wind power generators. The results show that the correlation and the turbulence degree are useful for the reduction of frequency fluctuation so as not to disconnect too many wind power generators. © 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 171(1): 10–18, 2010; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20911     

Dynamic security issues in autonomous power systems with increasing wind power penetration

Technical requirements set by the network operators nowadays include various aspects, such as fault ride-through capability of wind turbines during faults, voltage-reactive power control and overall control of the wind farms as conventional power plants. Detailed models for the power system as well as for the wind farms are therefore essential for power system studies related to these issues, especially when applied to non interconnected systems with high wind power penetration. Detailed generic models for three different wind turbine technologies - Active Stall Induction Generator (ASIG), Doubly Fed Asynchronous Generator (DFAG) and Permanent Magnet Synchronous Generator (PMSG) - are applied and issues regarding interaction with the power system are investigated. This paper provides conclusions about the dynamic security of non-interconnected power systems with high wind power penetration based on a complete model representation of the individual components of the system; three different types of conventional generators are included in the model, while the protection system is also incorporated. The load shedding following faults is finally discussed.     

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.     

Identifying the optimum wind capacity for a power system with interconnection lines

This paper presents a general framework for identifying the optimum wind capacity to be integrated in a power system with interconnection lines. Wind generation is treated as negative demand, together with the load demand, which must be covered by the conventional power generation and the energy interchange with neighboring systems. Under the presumption that the time-varying wind and load together with the trading price with neighboring systems could be statistically modeled by specified probability distributions, a chance constrained programming (CCP) model is formulated to address the wind capacity planning problem. The objective is to maximize the yearly net profit of the entire power system subject to the operational constraints and the reliability requirement. Wind power curtailment and load shedding are allowed to improve the system flexibility but incur penalty costs. The optimization problem is solved using a hybrid intelligence algorithm incorporating Monte Carlo simulations, a neural network and a genetic algorithm. The feasibility of the proposed approach is verified by a case study on a given power system.     

The impact of wind power plants on slow voltage variations in distribution networks

The connection of a wind power plant to a distribution network affects the voltage conditions in that part of the network. However, rather than assessing the worst possible cases regarding the voltage range, in this paper a more accurate methodology is presented for calculating the annual distribution network steady-state voltage profile and slow voltage variations after wind power plant connection. Commonly used simulation methods for the same problem based on probabilistic load flow analysis are only able to give probability distributions of the node voltages and network power flows. Here, a method is proposed which can also give chronological insight into the considered network state. This is realized through wind speed data modeling and generation using a Markov chain transition probability matrix and the Monte Carlo simulation method. The proposed methodology is tested using data from an existing distribution network in Croatia, a wind power plant planned for connection, and one year wind data measurements.     

Impact of FACTS controllers on the stability of power systems connected with doubly fed induction generators

The increasing power demand has led to the growth of new technologies that play an integral role in shaping the future energy market. Keeping in view of the environmental constraints, grid connected wind turbines are promising in increasing system reliability. This paper presents the impact of FACTS controllers on the stability of power systems connected with wind energy conversion systems. The wind generator model considered is a variable speed doubly fed induction generator model. The stability assessment is made first for a three phase short circuit without FACTS controllers in the power network and then with the FACTS controllers. The dynamic simulation results yield information on (i) the impact of faults on the performance of induction generators/wind turbines, (ii) transient rating of the FACTS controllers for enhancement of rotor speed stability of induction generators and angle stability of synchronous generators. EUROSTAG is used for executing the dynamic simulations.     

Stochastic optimal power flow incorporating offshore wind farm and electric vehicles

In this paper, an optimal power flow model of a power system, which includes an offshore wind farm and plug-in electric vehicles (PEVs) connected to grid, is presented. The stochastic nature of wind power and the uncertainties in the EV owner’s behavior are suitably modelled by statistical models available in recent literatures. The offshore wind farms are assumed to be composed of doubly fed induction generators (DFIGs) having reactive power control capability and are connected to offshore grid by HVDC link. In order to obtain the optimal active power schedules of different energy sources, an optimization problem is solved by applying recently introduced Gbest guided artificial bee colony algorithm (GABC). The accuracy of proposed approach has been tested by implementing AC–DC optimal power flow on modified IEEE 5-bus, IEEE 9-bus, and IEEE 39-bus systems. The results obtained by GABC algorithm are compared with the results available in literatures. This paper also includes AC–DC optimal power flow model, implemented on modified IEEE-30 bus test system by including wind farm power and V2G source. It has been shown that the uncertainty associated with availability of power from wind farm and PEVs affects the overall cost of operation of system.     

考虑风电消纳多向量流系统的遗传优化方法

随着全球能源短缺问题的加剧,风电的大规模消纳显得十分重要。为了克服风电大规模消纳困难的问题,提出一种多矢量储氢发电系统的遗传优化方法。分别研究分析风力风电、氢储能系统、储气罐中的等效荷电状态的数学模型,建立以风电本地消纳最大化为目标的联合优化模型,结合多种约束条件,通过遗传算法实现能量流最优解的求取。以东北某地区的实际测量数据为基础,进行了案例分析。通过对系统在运行过程中的风电消纳效果进行比较,验证了所提方法能够有效减少交互电量,实现了风电本地消纳最大化。     

Heuristic methods for wind energy conversion system positioning

This paper considers the problem of determining the locations of wind generators in a wind farm consisting of many generators. The objective is to find a generator placement that maximizes profit, which is the product of the cost efficiency of the generators and the total power output from the wind farm. Generator placement is significant because if generator A is located close to generator B and is located downwind of generator B then the power output of generator A is reduced by an amount that varies with the distance between the generators. The problem can be formulated using mathematical programming but to solve the problem one cannot employ traditional optimization methods. Therefore, a greedy improvement heuristic methodology is developed and described in detail. The effectiveness of the proposed heuristic is demonstrated on a suite of test problems. These results indicate that the proposed method represents an effective solution strategy for this problem.     

基于风力发电机简化RX模型的电力系统状态估计

为了充分考虑风能随机性对电网安全稳定性的影响,更精确地反映风力发电机组工作机理区别于传统电源的特殊性,对含风力发电机的电力系统进行状态估计时,文章用简化RX模型来等效风力发电机,把滑差作为状态量引入到状态估计过程,建立了相应的数学模型。这种方法考虑了异步发电机本身的特性,可以与原有的状态估计程序进行高效融合,比基于传统RX模型进行状态估计的计算量小,且计算精度满足要求。算例计算结果表明:该方法性能良好,在含有风力发电机的电力系统状态估计中有较好的应用前景。     

兼顾系统调频需求的分布式风电分散自治调控策略

越来越多的风力发电将分布在配电系统中,它们无法像常规电源一样由输电网调度中心集中调度和控制,通常需要采用分散自治的调控方式。然而,风电机组完全不顾输电系统的运行需求充分自治,会在某些方式下导致系统运行状态恶化。因此,需要研究风电机组新型调控策略,使充分自治转变成能够兼顾系统某些特殊运行需求的有限自治,配合系统渡过难关。针对此问题,提出了能够兼顾系统调频需求的分布式风电机组分散自治调控策略,该策略根据风电机组运行信息和系统频率将风电机组调控区划分为并网控制区、正常调控区、异常调控区、紧急调控区和脱网控制区五类,并给出了异常调控、紧急调控和故障调控三种新的调控模式。算例表明:风电机组能够较好地根据系统调频需求,在最大可用输出功率范围内调整自身输出功率,一定程度上给予系统积极的支持。     

An improved fast decoupled power flow model considering static power–frequency characteristic of power systems with large‐scale wind power

Large‐scale wind power (LSWP) integration may cause significant impact on power system frequency, so it is necessary to take frequency regulation issues into account in power system steady‐state operation analysis. An improved fast decoupled power flow model considering static power–frequency characteristic of power systems with LSWP is proposed in this paper. In this scheme, the active power of generators and loads are presented with their static power–frequency characteristics. The slack bus degenerates to the nodal voltage phase angle reference bus of the system, and all the generators with frequency regulation capability participate in unbalanced power regulation. The power flow calculation results can reveal the impact to the system frequency of operation mode change and load variation, and present the output adjustment of the generators. The proposed model can be solved conveniently by the block solving technology based on the fast decoupled power flow algorithm. The scheme presented in this paper has been tested on the IEEE standard 30‐bus test system by simulating basic operation and primary and secondary frequency regulation of the generators, which demonstrated the validity by the method. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Renewable energy credit driven wind power growth for system reliability

Environmental concerns over electric power generation from conventional sources has led to widespread public support for renewable energy sources. Governments throughout the world have responded by providing various forms of financial incentives to promote power generation from renewable energy sources. The rapid growth of wind power since the last decade has primarily been driven by governmental subsidies. Long-term growth of wind power should, however, be driven by sustainable market mechanisms. A potential solution is to recognize monetary values to the environmental benefits from renewable energy sources, and to specify targets for their growth. The environmental benefits from wind sources can be leveraged to allow market competition of these sources with the less costly conventional generating sources. A probabilistic method to evaluate the impact of renewable energy credit and wind penetration level on the cost and adequacy of power generating systems is presented in this paper. The technique incorporates reliability and economic analyses and is applied to a published test system to illustrate the results and their influence on key system variables. The paper provides useful information to system planners and policy makers on wind energy application in electric power systems.     

Optimal power flow by a fuzzy based hybrid particle swarm optimization approach

This paper presents a fuzzy based hybrid particle swarm optimization (PSO) approach for solving the optimal power flow (OPF) problem with uncertainties. Wind energy systems are being considered in the study power systems. OPF is an optimization problem which minimizes the total thermal unit fuel cost, total emission, and total real power loss while satisfying physical and technical constraints on the network. When performing the OPF problem in conventional methods, the load demand and wind speed must be forecasted to prevent errors. However, actually there are always errors in these forecasted values. A characteristic feature of the proposed fuzzy based hybrid PSO method is that the forecast load demand and wind speed errors can be taken into account using fuzzy sets. Fuzzy set notations in the load demand, wind speed, total fuel cost, total emission, and total real power loss are developed to obtain the optimal setting under an uncertain environment. To demonstrate the effectiveness of the proposed method, the OPF problem is performed on the IEEE 30- and 118-Bus test systems.     

Short-term wind power forecasting using ridgelet neural network

Rapid growth of wind power generation in many countries around the world in recent years has highlighted the importance of wind power prediction. However, wind power is a complex signal for modeling and forecasting. Despite the performed research works in the area, more efficient wind power forecast methods are still demanded. In this paper, a new prediction strategy is proposed for this purpose. The forecast engine of the proposed strategy is a ridgelet neural network (RNN) owning ridge functions as the activation functions of its hidden nodes. Moreover, a new differential evolution algorithm with novel crossover operator and selection mechanism is presented to train the RNN. The efficiency of the proposed prediction strategy is shown for forecasting of both wind power output of wind farms and aggregated wind generation of power systems.