Uncertain Nature Of Wind Power Research Articles

Grid integration of wind farms and the interconnection requirements for the U.S. grid

Abstract The integration of wind turbines and wind farms into the power grid presents significant challenges, including the variable and uncertain nature of wind power, the need for efficient transmission and distribution of power, and the need for advanced control and monitoring systems. Recent advances in wind turbine and wind farm technology have greatly improved their ability to integrate into the power grid. This includes the use of variable speed generators and power electronics, such as inverters, which allow for more accurate control of the power output and better alignment with the grid’s frequency. Additionally, advanced control algorithms, such as model predictive control and feedback linearization, have been developed to improve the stability and reliability of wind power systems. Communication systems have also been integrated into wind power systems to allow for real-time monitoring and control of the wind farm’s performance. This improves the ability of the wind farm to provide ancillary services, such as frequency regulation and voltage control. In this work, the major interconnection requirements for the U.S. grid for the expected mass integration of wind farms were discussed focusing on sustainable and reliable control strategies. The paper explores various control schemes that are used for providing response for grid reliability and sustainability. The analysis also included the future steps that OEMs of wind turbines need to take and the regulatory framework that is needed for ancillary services to provide an equitable platform for all generating units – conventional and renewables.

Distributed chance-constrained based total energy supply capability evaluation method for integrated power and natural gas system

With the development of gas turbines and power to gas technologies, the modern energy infrastructure has evolved into a coupled power and natural gas system. To fully exploit the maximum total energy supply capability (TESC) of the integrated power distribution and natural gas system (IDGS), a distributed chance-constrained based TESC evaluation method with consideration of wind power uncertainties is proposed in this paper, which can be utilized as a guideline for the planning, operation and reliability assessment of the IDGS. Specifically, to preserve the privacy of the operation data shared among power distribution system (PDS) and natural gas system (NGS), the established TESC model is solved in a distributed manner based on the analytical target cascading (ATC) algorithm. In the PDS and NGS sub-problems, the difference-of-convex programming (DCP) algorithm is utilized to deal with the nonconvex power flow and Weymouth gas flow equations to guarantee the feasibility of the solution results. Moreover, the uncertain nature of wind power is incorporated into the model and represented by a set of joint chance constraints (JCCs) with a predefined confidence level, which can be effectively solved via a novel Boolean reformulation method. Numerical studies are performed on a modified 94-bus distribution system and 20-node natural gas system to verify the effectiveness of the proposed model and solution methodology.

MPC-based approach for online demand side and storage system management in market based wind integrated power systems

Ever-increasing rate of wind power generation as an uncertain and variable source of energy, face new challenges in power system operation. For this reason, optimal real-time operation of wind integrated power systems in profit based markets considering the effects of probabilistic future variations of wind speed is one of the main concerns of system operators. For this purpose, some solutions such as demand response (DR) programs and energy storage systems (ESS) are widely being used to cover and manage wind generation uncertainty. But some of them such as DR programs can add some extra sources of uncertainty due to unpredictable customer’s behavior. To this end, this paper proposes an online model-based predictive control approach for optimal real-time operation of wind integrated power systems including DR and ESS facilities. Discrete-time manner, re-optimization characteristic, and adaptability are the main features of the proposed MPC method which make it well-suited to address high uncertainties regarding wind power generation and customer’s behavior. Besides, MPC considers all interactive effects of the control facilities in accordance with the expected wind farm output power in the future prediction horizon to maximize wind power utilization and so enhance social welfare. In addition, the uncertain nature of wind power is modeled using Markov chain Monte Carlo method. For efficiency evaluation of the proposed approach, simulation is implemented in MATLAB software using YALMIP optimization toolbox for the 8-bus test system. Results confirm the acceptable performance of the proposed approach in reducing operation cost through optimal uncertainties management.

Probabilistic Forecast for Multiple Wind Farms Based on Regular Vine Copulas

The uncertain nature of wind power causes difficulties in power system operation scheduling. Probabilistic descriptions of the uncertainty have been studied for decades. However, probabilistic forecasts designed for the regional multiple wind farms are few. Although the traditional methods for the single wind farm can still be used, they have the limitations in capturing the spatial correlations among wind farms, and they are less robust when multivariate observations are not so complete. To improve the forecast quality in this case, we combine the multivariate distribution modeling and probabilistic forecasts in this paper. An advanced model—the regular vine copula, which can describe the wind farms’ dependence structure precisely and flexibly with various bivariate copulas as blocks, is used in this paper. Enough simulation data can be generated from the model, which can be easily used to form the conditional forecast distributions under multiple forecast conditions. A case of 10 wind farms in East China has been used to compare the proposed method with its competitors. The results showed the method's advantages of providing reliable and sharp forecast intervals, especially in the case with limited observations available.

A probabilistic method for cost minimization in a day-ahead electricity market considering wind power uncertainties

During recent decades, using wind power as one of the most substantial renewable energy sources has been vastly intensified in power systems. Increasing wind power penetration in the power grid can have both detrimental and beneficial effects. Due to the uncertain nature of wind power, the system operator is faced with many challenges to make proper decisions for the electricity market. In this paper, a probabilistic method is proposed which not only effectively manages wind power sources along with other producers, but also takes the uncertain behavior of wind power in defined intervals into account. Using wind farm information in each hour, a new approach for calculating the probability density function (PDF) in uncertainty bounds is presented. By utilizing the calculated PDF two objective functions are proposed for minimizing the total expected cost during 24 h without and with considering consumers payments. The proposed objective functions have been minimized through a two-level optimization method using three different heuristic algorithms (Teaching-Learning-Based Optimization (TLBO), Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSO-GSA), and Ant Lion Optimization (ALO)) on an 8-bus sample transmission network. The results show the total expected cost has been decreased up to 20% in some scenarios. In addition, the proposed method can satisfy consumers by keeping their payments close to the lowest amount according to the different scenarios.

An overview and comparative analysis of recent bio-inspired optimization techniques for wind integrated multi-objective power dispatch

Abstract Over the last few decades, bio-inspired (BI) evolutionary optimization techniques have experienced overwhelming popularity, extraordinary growth and large number of applications, particularly, in the field of engineering and technology. These techniques present a tough competition to traditional numerical methods which suffer from convexity and continuity assumptions and which normally employ a gradient based search that is sensitive to the initial solution. While initial BI techniques suffered from limitations such as premature convergence and dependence on control parameters, eventually, these issues were specifically addressed by improved variants and many novel BI methods. The population based computing methods are particularly attractive for solving multi-objective (MO) problems due to their capability of producing a large number of Pareto-optimal solutions in one run. In this paper, an integrated ranking index (IRI) composed of TOPSIS and fuzzy-min concept is proposed as a performance metrics to aggregate the different objectives. The performance of eight handpicked recent BI techniques is compared for the solution of wind integrated multi-objective optimal power dispatch (MOOD) problem for simultaneous minimization of fuel cost and emission. Due to the uncertain nature of wind power (WP), the effect of its over and underestimation on both economic as well as environmental aspects, has also been considered. Six standard test cases having non-convex, multi-modal and discontinuous objective functions, dynamic operation and complex equality/inequality constraints, are selected for testing Flower Pollination Algorithm (FPA), Mine Blast Algorithm (MBA), Backtracking Search Algorithm (BSA), Symbiotic Organisms Search (SOS), Ant Lion Optimizer (ALO), Moth-Flame Optimization (MFO), Stochastic Fractal Search (SFS) and Lightning Search Algorithm (LSA).

Energy Reserve Trade Optimization for Wind Generators Using Black and Scholes Options in Small-Size Power Systems

Tremendous attention has been paid to wind energy. This renewable source of clean energy has become very appealing to investors, engineers, and environmentalists. From the power system operations perspective, the uncertain nature of wind power poses serious challenges. To reduce the effect of wind power uncertainty, a reserve procurement methodology using financial derivative such as “Options” is very promising. This paper proposes a new optimization model to procure secondary reserves to overcome wind power uncertainty by using Black and Scholes financial options while estimating short-term wind energy forecast errors by using the Gaussian distribution function. The proposed model integrates power system security constraints. At the optimal solution, the lambda multiplier associated with real power balance equations provides option premiums and strike prices.

DAY-AHEAD OPTIMAL OPERATION PLANNING OF WIND AND HYDROTHERMAL GENERATION WITH OPTIMAL SPINNING RESERVE ALLOCATION

This paper presents a computational model for the day-aheadscheduling of integrated hydrothermal and wind power generation in which is suggested an optimal spinning reserve requirement allocation to mitigate the intermittent and uncertain nature of wind power.

A stochastic framework for the grid integration of wind power using flexible load approach

Wind power integration has always been a key research area due to the green future power system target. However, the intermittent nature of wind power may impose some technical and economic challenges to Independent System Operators (ISOs) and increase the need for additional flexibility. Motivated by this need, this paper focuses on the potential of Demand Response Programs (DRPs) as an option to contribute to the flexible operation of power systems. On this basis, in order to consider the uncertain nature of wind power and the reality of electricity market, a Stochastic Network Constrained Unit Commitment associated with DR (SNCUCDR) is presented to schedule both generation units and responsive loads in power systems with high penetration of wind power. Afterwards, the effects of both price-based and incentive-based DRPs are evaluated, as well as DR participation levels and electricity tariffs on providing a flexible load profile and facilitating grid integration of wind power. For this reason, novel quantitative indices for evaluating flexibility are defined to assess the success of DRPs in terms of wind integration. Sensitivity studies indicate that DR types and customer participation levels are the main factors to modify the system load profile to support wind power integration.

Control of BESS Using Frequency-Linked Pricing with High Wind Penetration

Recently, many countries have been pushing for a higher share of renewable energy sources, especially wind, in their generation mix. However, the intermittent and uncertain nature of wind power imposes a limit on the extent it can replace the conventional generation resources. In a high wind penetration scenario, the Battery Energy Storage System (BESS) offers a solution to the grid operation problems. The purpose of this paper is to evaluate the merits of price-based operation of BESS in a real-time market with high wind penetration using frequency-linked pricing. The authors propose a real-time market in which real-time prices are based on the grid frequency. A model for real-time price-based operation of a conventional generator and a BESS is presented. Simulations for different wind penetration scenarios are carried out on an isolated area test system. Wind speed sequence is generated using composite wind speed model. A simplified model of wind speed to power conversion is adopted to observe the impact of increase in wind power generation on the grid frequency and the real-time prices.

Optimal power flow solution of wind integrated power system using modified bacteria foraging algorithm

Owing to the intermittent nature of wind flow, optimal power flow in a power system with considerable wind energy penetration is a challenging issue. In this paper an OPF solution is proposed for IEEE 30 bus power system modified by replacing three conventional generators with equivalent wind energy conversion systems (WECS).The uncertain nature of wind power has the risk of over or under estimating the capacity of available wind power. This uncertainty has been suitably modeled and included in the OPF framework. Moreover, to justify the limitation of reactive power generation capability of doubly fed induction generator based WECS during under estimation, additional cost component corresponding to external reactive power (Q) generating sources has been added in the objective function. The scheduling problem of WECS integrated power system is solved by formulating it as an optimization problem. Genetic algorithm and a modified bacteria foraging algorithm are employed separately to determine the optimal schedule. The optimal solution obtained with a modified version of BFA has been found to give better results compared to GA. The results depict the impact of wind and thermal scheduling on total system cost and reiterate the need of additional support of reactive power resources to maintain stable voltage profiles of the wind–thermal system.

Operation and control of BESS using frequency‐linked pricing in real‐time market with high wind penetration

PurposeRecently, many countries have been pushing for a higher share of renewable energy sources, especially wind, in their generation mix. However, the intermittent and uncertain nature of wind power imposes a limit on the extent it can replace the conventional generation resources. In a high wind penetration scenario, the Battery Energy Storage System (BESS) offers a solution to the grid operation problems. The purpose of this paper is to evaluate the merits of price‐based operation of BESS in a real‐time market with high wind penetration using frequency‐linked pricing.Design/methodology/approachThe authors propose a real‐time market in which real‐time prices are based on the grid frequency. A model for real‐time price‐based operation of a conventional generator and a BESS is presented. Simulations for different wind penetration scenarios are carried out on an isolated area test system. Wind speed sequence is generated using composite wind speed model. A simplified model of wind speed to power conversion is adopted to observe the impact of increase in wind power generation on the grid frequency and the real‐time prices.FindingsThe result of simulations show that BESS not only helps in dealing with uncertainty in wind power forecasts, but also reduces the fluctuations in frequency due to wind power's intermittency. Price‐based operation of BESS results in higher operating revenues by discharging it at peak prices and reduces operating costs by charging it at minimum prices.Social implicationsThe study helps in achieving the societal goal of replacing fossil fuel generation by environment friendly generation and reducing green house gas emissions.Originality/valueThe novelty of this paper lies in the use of frequency‐linked pricing in real‐time market and proposing a control algorithm for operating BESS using these price signals.

A New Approach to Quantify Reserve Demand in Systems With Significant Installed Wind Capacity

With wind power capacities increasing in many electricity systems across the world, operators are faced with new problems related to the uncertain nature of wind power. Foremost of these is the quantification and provision of system reserve. In this paper a new methodology is presented which quantifies the reserve needed on a system taking into account the uncertain nature of the wind power. Generator outage rates and load and wind power forecasts are taken into consideration when quantifying the amount of reserve needed. The reliability of the system is used as an objective measure to determine the effect of increasing wind power penetration. The methodology is applied to a model of the all Ireland electricity system, and results show that as wind power capacity increases, the system must increase the amount of reserve carried or face a measurable decrease in reliability.