Wind Power Integration Research Articles

A new multi-timescale optimal scheduling model considering wind power uncertainty and demand response

When wind power is connected to a power grid, intermittency and uncertainty increase the difficulty of power system dispatching and operation. A multi-timescale optimal scheduling model based on wind power uncertainty and demand response are proposed to address the uncertainty of wind power integration. The demand response program is first implemented on the customer side to adjust the electricity consumption pattern and reduce the peak-valley load difference. Then, the robust optimization based on extreme scenarios is used in the day-ahead scheduling stage according to the characteristic that the wind power prediction accuracy gradually improves step by step with the refinement of timescales so that the decision variable meets requirements in all scenarios. In the intraday scheduling stage, the method based on stochastic chance-constrained programming is used, and the out-of-bounds phenomenon is allowed in extreme cases. During real-time scheduling, the deviation remaining after intraday scheduling is corrected. Finally, a 10-unit system is used as an example to demonstrate the feasibility and effectiveness of the proposed scheduling model. The simulation results show that the total economic cost, reserve cost and wind power curtailments of the real-time scheduling model are reduced by 18.21%, 66.06% and 66.57%, respectively, compared with the day-ahead scheduling model. In addition, compared with the intraday scheduling model, the wind power curtailments of the real-time scheduling model are reduced by 20.00%, which proves the necessity of multi-timescale coordinated scheduling.

Energy storage capacity configuration in multi-energy complementary systems

To solve the problems of high peak shaving pressure, low energy utilization rate and poor economy of the multi-energy complementary system caused by the integration of wind and solar power into the grid, the research builds a two-layer optimization model of energy storage and routine power peak shaving based on the predicted output and load of wind and solar power on a typical day. The outer layer aims at the maximum annual net profit of the muit energy complementary system, and the inner layer aims at the lowest daily operating cost. The improved PSO and nonlinear programming are used to coordinate and solve the problem to determine the multi-energy complementary system’s intra-day peak-shaving scheduling method, which is reasonable and reasonable. Finally, an example of an actual power grid is analyzed, and the results show that the multi-energy complementary system after optimal configuration of energy storage can greatly raise the level of new energy consumption and has good economy.

Collaborative optimization of VRB-PS hybrid energy storage system for large-scale wind power grid integration

In order to achieve the carbon peak and neutrality goals, wind power in China has been vigorously developed. However, the random volatility and intermittence of wind power output may threaten the safe and stable operation of the power system. Energy storage, as a flexible resource, can play an important role in promoting the large-scale integration of wind power. In this paper, a two-stage collaborative optimization method for the Hybrid Energy Storage System (HESS) composed of Vanadium Redox flow Battery (VRB) and Pumped Storage (PS) is proposed. In the first stage, VRB is configured to suppress wind power output fluctuations, and in the second stage, a two-layer capacity configuration and operation optimization model for PS is developed to support the safe and economic operation of the system. Finally, the effectiveness of the proposed collaborative optimization method is verified. The simulation results show that the VRB can suppress high frequency fluctuations of wind power, and the PS can promote the wind power utilization rate and improves the economy, safety and flexibility of system operation, that is, the proposed HESS has better regulation ability and operating economy than the single energy storage.

Evaluating a Hybrid Circuit Topology for Fault-Ride through in DFIG-Based Wind Turbines

Large-scale wind power integration has raised concerns about the reliability and stability of power systems. The rotor circuit of a doubly fed induction generator (DFIG) is highly vulnerable to unexpected voltage dips, which can cause considerable electromotive force in the circuit. Consequently, the DFIG must fulfil the fault-ride through (FRT) criteria to ensure the system's performance and contribute to voltage regulation during severe grid outages. This paper provides a hybrid solution for DFIG wind turbines with FRT capabilities, using both a modified switch-type fault current limiter (MSFTCL) and a direct current (DC) chopper. The proposed system has the merit of keeping the rotor current and the DC-link voltage within the permissible limits, enhancing the FRT capability of generators. Moreover, the boundness of supply voltage into its reference value ensures dynamic stability during symmetric and asymmetric grid failures. Further, electromagnetic torque variations are significantly reduced during fault events. Finally, the performance validation of the proposed scheme is performed in a simulation setup, and the results are compared with the existing sliding mode control (SMC) and proportional-integral (PI) controller-based approaches. The comparison results show that a hybrid strategy with advanced controllers provides superior performance for all critical parameters.

A Review on Multi-Terminal High Voltage Direct Current Networks for Wind Power Integration

With the growing pressure to substitute fossil fuel-based generation, Renewable Energy Sources (RES) have become one of the main solutions from the power sector in the fight against climate change. Offshore wind farms, for example, are an interesting alternative to increase renewable power production, but they represent a challenge when being interconnected to the grid, since new installations are being pushed further off the coast due to noise and visual pollution restrictions. In this context, Multi-Terminal High Voltage Direct Current (MT-HVDC) networks are the most preferred technology for this purpose and for onshore grid reinforcements. They also enable the delivery of power from the shore to offshore Oil and Gas (O&G) production platforms, which can help lower the emissions in the transition away from fossil fuels. In this work, we review relevant aspects of the operation and control of MT-HVDC networks for wind power integration. The review approaches topics such as the main characteristics of MT-HVDC projects under discussion/commissioned around the world, rising challenges in the control and the operation of MT-HVDC networks and the modeling and the control of the Modular Multilevel Converter (MMC) stations. To illustrate the challenges on designing the control system of a MT-HVDC network and to corroborate the technical discussions, a simulation of a three-terminal MT-HVDC network integrating wind power generation and offshore O&G production units to the onshore grid is performed in Matlab’s Simscape Electrical toolbox. The results highlight the main differences between two alternatives to design the control system for an MT-HVDC network.

Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer

Abstract. With the increasing level of offshore wind energy investment, it is correspondingly important to be able to accurately characterize the wind resource in terms of energy potential as well as operating conditions affecting wind plant performance, maintenance, and lifespan. Accurate resource assessment at a particular site supports investment decisions. Following construction, accurate wind forecasts are needed to support efficient power markets and integration of wind power with the electrical grid. To optimize the design of wind turbines, it is necessary to accurately describe the environmental characteristics, such as precipitation and waves, that erode turbine surfaces and generate structural loads as a complicated response to the combined impact of shear, atmospheric turbulence, and wave stresses. Despite recent considerable progress both in improvements to numerical weather prediction models and in coupling these models to turbulent flows within wind plants, major challenges remain, especially in the offshore environment. Accurately simulating the interactions among winds, waves, wakes, and their structural interactions with offshore wind turbines requires accounting for spatial (and associated temporal) scales from O(1 m) to O(100 km). Computing capabilities for the foreseeable future will not be able to resolve all of these scales simultaneously, necessitating continuing improvement in subgrid-scale parameterizations within highly nonlinear models. In addition, observations to constrain and validate these models, especially in the rotor-swept area of turbines over the ocean, remains largely absent. Thus, gaining sufficient understanding of the physics of atmospheric flow within and around wind plants remains one of the grand challenges of wind energy, particularly in the offshore environment. This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. Such phenomena include horizontal temperature gradients that lead to strong vertical stratification; consequent features such as low-level jets and internal boundary layers; highly nonstationary conditions, which occur with both extratropical storms (e.g., nor'easters) and tropical storms; air–sea interaction, including deformation of conventional wind profiles by the wave boundary layer; and precipitation with its contributions to leading-edge erosion of wind turbine blades. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.

Economic Enhancement of Wind–Thermal–Hydro System Considering Imbalance Cost in Deregulated Power Market

Studying the property of the combination of renewable energy sources in the existing power systems is of great importance, and especially in the case of deregulated systems. The uncertainty of renewable sources is the largest barrier to integrating renewable-energy-producing units into the existing electrical infrastructure. Due to its uncertainty, integrating wind power into an existing power system requires extra consideration. In this work, the impacts of wind farm (WF) integration and a pumped hydroelectric storage system (PHES) on the electric losses, voltage profiles, generation costs, and system economy in a deregulated power market were studied. A comparative study was performed to determine the impact of wind farm integration on regulated and deregulated environments. Four locations in India were chosen at random for this work, and we used the real-time statistics for the actual wind speeds (AWSs) and forecasted wind speeds (FWSs) for each chosen location. To determine the system economy, surplus charge rates and deficit charge rates were developed to evaluate the imbalance cost resulting from the mismatch between the predicted and actual wind speeds. Considering the effect of the imbalance cost, the system profit/day varies by an average of 1.6% for the locations studied. Because of the reorganization of the power system, consumers constantly look for reliable and affordable power that is also efficient. As a result, the system security limit may be breached, or the system may run in a dangerous state. Lastly, in this paper, an economic risk analysis is presented with the help of heuristic algorithms (i.e., artificial bee colony algorithm (ABC) and moth–flame optimization algorithm (MFO)), along with sequential quadratic programming (SQP), and the way in which the PHES is used to compensate for the deviation in the WF integration in the real-time electricity market is also presented. The value at risk (VaR) and conditional value at risk (CVaR) were used as the economic risk analysis tools. According to the work, with the increase in the wind generation, the system risk improves. The results show that, as the wind generation increases by three times, there is an improvement in the risk coefficient values by 1%. A modified IEEE 14-bus test system was used for the validation of the entire work.

Voltage Profile Improvement by Integrating Renewable Resources with Utility Grid

There are three main parts of an electric power system—power generation, transmission, and distribution. For electric companies, it is a tough challenge to reduce losses of the power system and deliver lossless and reliable power from the generating station to the consumer end. Nowadays, modern power systems are more complex due to gradually increasing loads. In the electrical power system, especially in transmission and distribution networks, there are power losses due to many reasons such as overloading of the line, long distribution lines, low power factors, corona losses, and unsuitable conductor size. The main performance factor of the power system is reliability. Reliability means continuity of the power supply without any interruptions from the generating station to the demand side. Thus, due to these power losses, there are voltage stability problems and economic losses in the electrical system. The voltage stability of the power system can be increased by improving the voltage profile. In this paper, different techniques are analyzed that include the integration of wind power, the integration of photovoltaic power, and reactive power injection by integrating FACTS devices. These techniques are applied to the IEEE 57 bus system with standard data using simulation models developed in MATLAB. Thus, the results of the analysis of these techniques have been compared with each other.

Solution of constrained mixed‐integer multi‐objective optimal power flow problem considering the hybrid multi‐objective evolutionary algorithm

An Optimal power flow (OPF) is non-linear and constrained multi-objective problem. OPF problems are expensive and evolutionary algorithms (EAs) are computationally complex to obtain uniformly distributed and global Pareto front (PF). Therefore, here, hybrid two-phase algorithm integrated with parameter less constraint technique is applied to solve OPF problem. Proposed technique combines single and multi-objective EAs to find better convergence and evenly distributed PF. For the validation and effectiveness of proposed algorithm, various conflicting objective functions are formulated and implemented on IEEE 30 and 300-bus network. Each case is independently run twenty times. Hyper volume indicator technique is employed to find the best PF, and the best-compromised solution is obtained by using fuzzy decision-making technique. Recently, maximum integration of wind and solar power is highly encouraged. Complexity of OPF is increased with the integration of uncertain renewable energy resources. Hence, 30-bus test system is modified by replacing some conventional generators with the wind and solar generation. Uncertainties in wind, solar and load demand are modelled by appropriate probability distribution functions. Simulation results show that the proposed method can find the near global PF of highly complex problems subject to satisfying all the operational constraints.

Short term wind speed forecasting using time series techniques

ABSTRACT Wind power is a renewable energy source that can be used in place of conventional fossil-fuel-based power. Although the integration of wind power has many benefits, the conventional electrical system requires a constant supply, i.e. the power supply ought to be equivalent to the power demand consistently. It’s tough to keep this equilibrium because of the variation of the wind power output. Improving wind speed predictions is one of the solutions to the balance problem. This paper centers around short-term wind speed forecasting using time series methods. A time series is a logically ordered succession of numerical data points that can be used to study any variable that changes over time. This paper applies a variety of time series forecasting techniques – Exponential Smoothing, ARIMA, LSTM, and a novel hybrid LSTM-ARIMA model – to three different time periods of hourly measured wind speed data. The performance of the models is compared using metrics such as MSE (Mean Squared Error), RMSE (Root Mean Squared Error), NSE (Nash – Sutcliffe model efficiency coefficient), MAE (Mean Absolute Error), and MAPE (Mean Absolute Percentage Error). The proposed LSTM-ARIMA model has the highest prediction accuracy and achieves the least error metrics at all time scales. It outperforms other architectures, achieving a MAPE of 24.78% for the 12-day scale, 9.30% for the 2-day scale, and 12.80% for the 1-day scale.

A demand side response scheme for enhancing power system security in the presence of wind power

One of the major concerns associated with the wind power integration is its anti-peaking characteristic which increases the required ramping of the conventional generators and potentially threatens power system security. Recently, demand side response has become one of the likely successful solutions that can be employed in overcoming the negative impact of the anti-peaking characteristic of the daily net load curve. This paper presents a demand side response framework in which the load shifting strategy is effectively applied to improve the daily load profile of the system in the presence of wind power. The size of the load to be shifted is determined such that the system security in terms of voltage magnitudes and voltage stability margins is preserved for the normal state and prospective emergency states. To stimulate the demand side response candidates to shift their load and support the utilities to realize a particular daily load shape, an incentive reimbursement pattern based on the participants’ submitted sizes has been proposed. The mathematical formulation of the problem is stated as a nonlinear mixed integer programming problem. The problem is solved using Benders decomposition technique where the intact problem is split into a master problem and several smaller nonlinear problems solved individually by the conventional methods. The performance of proposed approach has been evaluated by its application on the IEEE 57- and 118-bus systems.

Wind Turbines Control Trends and Challenges: An Overview

Wind turbines (WTs) create electricity by utilizing the energy of the wind. As a result, wind turbine control and cost-effective operation were studied. The control system offers a long service life, maximum energy output, and safety. On control methods and control strategies, various ways for limiting and optimizing energy consumption were discussed. Integration of wind power may compromise the stability of the transient system. Asynchronous induction generators cannot handle the quantity of reactive power generated in wind energy applications. WTs are usually constructed to withstand inclement weather but not for high speeds or torque. Strong aerodynamic torques or rotational speeds are capable of destroying WT blades. To prevent this, WTs always have a cutout speed over which the turbine will be stopped by its brakes. When excessive wind speeds endanger the safety of the turbines, the WT employs a range of control techniques. As a result, all WTs are constructed using a power control method. This can regulate pitch and stall. Passive or active stall control can be applied to the WT. Therefore, this study analyzed the associated technologies, the maintenance of wind turbines, the cost, the many types of wind turbine controllers, and the negative effects and roadblocks unique to the wind energy industry.

Optimal Dispatch of Wind Power, Photovoltaic Power, Concentrating Solar Power, and Thermal Power in Case of Uncertain Output

The integration of large-scale wind and photovoltaic power into modern power grids leads to an imbalance between the supply and demand for resources of the system, where this threatens the safety and stable operation of the grid. The traditional mode of grid dispatch and the capability of regulation of conventional thermal power units cannot satisfy the demands of grid connection for large-scale renewable energy, where the system requires the compensation and coordinated dispatch of flexible power sources. In light of this problem, this paper establishes a model to quantify the uncertainty in the forecasted outputs of wind and photovoltaic power. This is used to develop forecasts of the output of wind and photovoltaic power for several groups of scenarios, and predictions with the best complementarity are selected as a typical set of scenarios by means of their generation, reduction, and combination. By taking full advantage of the complementarity in the rates of regulation of conventional thermal power and concentrating solar power (CSP), a coordinated model of dispatch for wind power, photovoltaic power, CSP, and thermal power is established for a number of typical combinations of scenarios. The influence of uncertainty in the outputs of wind and photovoltaic power on the dispatch of the power grid is examined, and different modes of dispatch are formulated through simulations to analyze the superiority of the dispatch strategy proposed in this paper in terms of abandoned wind quantity, abandoned solar quantity, and the cost of dispatch.

A double-deck deep reinforcement learning-based energy dispatch strategy for an integrated electricity and district heating system embedded with thermal inertial and operational flexibility

With the high penetration of wind power connected to the integrated electricity and district heating systems (IEDHSs), wind power curtailment still inevitably occurs in the traditional IEDHS dispatch. Focusing on the flexibilities of the IEDHS is considered to be a beneficial solution to further promote the integration of wind power. In the district heating network, the thermal inertia is utilized to improve such flexibility. Therefore, an IEDHS dispatch model considering the thermal inertia of district heating network and operational flexibility of generators is proposed in this paper. In addition, to avoid the tendency of traditional reinforcement learning (RL) to fall into local optimality when solving high-dimensional problems, a double-deck deep RL (D3RL) framework is proposed in this study. D3RL combines with a deep deterministic policy gradient (DDPG) agent in the upper level and a conventional optimization solver in the lower level to simplify the action and reward design. In the simulation, the proposed model considering the transmission time delay characteristics of the district heating network and the operational flexibility of generators is verified in four scheduling scenarios. Besides, the superiority of the proposed D3RL method is validated in a larger IEDHS. Numerical results show that the considered scheduling model can use the heat storage characteristics of heating pipelines, reduce operating costs, improve the operational flexibility and encourage wind power utilization. Compared with traditional RL, the proposed optimization method can improve its training speed and convergence performance.

Optimal scheduling of flexible resources on the source and load sides considering comprehensive user satisfaction

With the large-scale integration of wind power, photovoltaic power, and other new energy sources, their volatility and intermittency tend to cause the problem of flexibility imbalance in power systems. To make full use of the advantages of flexible resources on the new energy and load sides in power dispatching, an optimal scheduling method of flexible resources considering comprehensive user satisfaction is proposed in this paper. First, a demand response model considering comprehensive user satisfaction is constructed. Then, by taking into account the constraints of demand response and comprehensive user satisfaction, an optimal dispatching model of flexible resources on the source and load sides is established to achieve economic optimality. Finally, the case study based on the improved IEEE 30-bus system is carried out, and the simulation results under various scenarios verify the effectiveness of the proposed method.

Distributed robust optimization for low-carbon dispatch of wind-thermal power under uncertainties.

Faced with the requirement of carbon emission reduction in power industry, low-carbon power dispatch involving various low-carbon approaches has been recognized as one of effective ways. Concentrate on several important approaches: wind power integration and carbon reduction cooperation, it is necessary to deal with the uncertainties of wind power and carbon reduction modes for thermal power encountered in low-carbon power dispatch. For this purpose, this paper firstly presents a distributed robust optimization model synthetically considering robustness, economy, and environment. Next, wind power characterizations, scenario division and compression methods, and allocation algorithms of initial carbon emission rights are fully discussed for the convenience of model solution. Finally, empirical analysis shows that (1) the proposed model proves to be effective not only in coping with wind power uncertainties and reducing operating costs, (2) but also in dealing with the uncertainties of carbon reduction modes and reducing carbon emissions, and (3) low-carbon power dispatching strategies combining robustness, economy, and environment could be achieved through the proposed model and method, which are especially helpful to minimize interference from these two types of uncertainty more scientifically and reasonably.

A Robust Second-Order Conic Programming Model with Effective Budget of Uncertainty in the Optimal Power Flow Problem

Integrating large-scale wind energy in modern power systems necessitates high-efficiency mathematical models to address classical assumptions in power systems. In particular, two main assumptions for wind energy integration in power systems have not been adequately studied. First, nonlinear AC power flow equations have been linearized in most of the literature. Such simplifications can lead to inaccurate power flow calculations and result in technical issues. Second, wind power uncertainties are inevitable and have been mostly modeled using traditional uncertainty modeling techniques, which may not be suitable for large-scale wind power integration. In this study, we addressed both challenges: we developed a tight second-order conic relaxation model for the optimal power flow problem and implemented the novel effective budget of uncertainty approach for uncertainty modeling to determine the maximum wind power admissibility and address the uncertainty in the model. To the best of our knowledge, this is the first study that proposes an effective, robust second-order conic programming model that simultaneously addresses the issues of power flow linearization and wind power uncertainty with the new paradigm on the budget of uncertainty approach. The numerical results revealed the advantages of the proposed model over traditional linearized power flow equations and traditional uncertainty modeling techniques.

Load frequency control of power system considering electric Vehicles’ aggregator with communication delay

The power from wind generation is intermittent in nature; which may cause a significant frequency deviation whenever, load frequency control (LFC) capacity is unable to recover the balance between generation and the required demand. Therefore, electric vehicle (EV) is another option owing to its fast dynamics response to compensate for the real power imbalance in each area, wherein LFC capacity is insufficient. This paper presents a control scheme based on event-triggered control (ETC) to minimize frequency deviation in each area.. The performance of proposed control scheme is demonstrated in single-area, multi-area power system, and with integrated wind power, according to real wind speed dynamics. Additionally, different amount of EVs integration and communication properties are simulated in study. Also, from the attacker’s perspective changing the EVs participation and sensitivity analysis with changes in parameters are analyzed. The effectiveness of the proposed study is compared with a recently reported techniques for LFC problem. The comparison suggests reduced overshoot/under shoot and settling time of frequency deviation. Additionally, the controller action of the proposed method uses different amount of thresholds of communication transmission which not only reduces the computation cost and communication burden, but also maintains minimum control effort.

Network governance and effectiveness on renewable energy integration: A comparative case study on power transmission networks in the United States

This paper examines how network structure and coordinating mechanisms in the US power transmission system affect the integration of wind energy through comparative case studies. The two transmission network cases represent two major transmission network governance models in the US power sector. Using archival data from all network participants and interviews with key stakeholders, we find that a centralized transmission network coordinated by an independent network administrative organization (NAO) is more effective in integrating wind power than a less centralized structure. Particularly, the concentration of decision-making authorities is a more substantial determinant than the structural centrality of the core agency. The two cases also highlight that hierarchy, collaboration, and market mechanisms coexist in a transmission network to manage the tension between stability and flexibility. When the network operates in a turbulent environment with great uncertainties, different coordinating mechanisms complement each other to improve system resilience.

Prediction of Wind power rolling interval based on VMD-PSR-LSTM-QR

The integration of wind power into the power grid brings great challenges to the safe operation of the power system. Predicting the wind power interval can avoid effectively the randomness and volatility of the integration, and protect the security and stability of the power grid. Considering the non-stationary characteristics of wind power, this paper proposes a new wind power rolling interval prediction method based on VMD-PSR-LSTM-QR. First, wind power is decomposed into several subsequences by variational mode decomposition (VMD). Second, phase space reconstruction (PSR) maps the subsequences to a high-dimensional space and constructs them. Then, long short-term memory (LSTM) networks are used for training different subsequences, and quantile regression (QR) parameters are determined. Finally, the predicted results of each subsequence are reconstructed. This paper uses actual wind power data in China as examples to verify the proposed method, and it is found that the proposed method achieves significantly higher prediction accuracy than traditional methods.