Integration Of Wind Generation Research Articles

Optimal scheduling and management of grid‐connected distributed resources using improved decomposition‐based many‐objective evolutionary algorithm

AbstractThis paper emphasizes the integration of wind and photovoltaic (PV) generation with battery energy storage systems (BESS) in distribution networks (DNs) to enhance grid sustainability, reliability, and flexibility. A novel multi‐objective optimization framework is introduced in this study to minimize energy supply costs, emissions, and energy losses while improving voltage deviation (VD) and voltage stability index (VSI). The proposed framework comprising normal boundary intersection (NBI) and decomposition‐based evolutionary algorithms (DBEA) determines the optimal siting and sizing of renewable‐based distributed resources, considering load demand variations and the intermittency of wind and solar outputs. The comparative analysis establishes that the proposed strategy performs better than many contemporary algorithms, specifically when all the objective functions are optimized simultaneously. The validation of the proposed framework was carried out on the standard IEEE‐33 bus test network, which demonstrates significant percentage savings in energy supply costs (49.6%), emission rate (62.2%), and energy loss (92.3%), along with enormous improvements in VSI (91.9%) and VD (99.8953%). The obtained results categorically underline the efficiency, reliability, and robustness of the proposed approach when employed on any complex distribution network comprising multiple renewable energy sources and battery storage systems.

An Incorporated Power and Data Synchronous Transmission Method for SRG Integrated Wind Power Generation Systems

Reliable communication places important roles in renewable generation for operating status monitoring, fault alarming, cooperative control and so on. For existing power and data synchronous transmission strategies applied to switched reluctance generator (SRG) systems, the communication rate is limited by very low power switching frequencies. Hence, in this paper, a new PDST strategy is proposed and implemented in an SRG integrated wind generation system by incorporating power and data composite modulation method with signal injection method. For lower signal band, the data is modulated together with power by regulating the lower power switch of each phase in a fixed interval with given frequency, and for the higher signal band, the data in form of voltage ripple is injected into the bus by a coupling circuit. Therefore, specific voltage ripples with different given frequencies can be embedded into the power line for carrying signals, and the frequency band of signal bus can be well exploited. On signal receiver end, the voltage ripples are sampled and analyzed by fast Fourier transform method for demodulating the transmitted data. The feasibility of the proposed incorporated data transmission method is verified by experimental testing.

Enhanced Randomized Harris Hawk Optimization of PI controller for power flow control in the microgrid with the PV-wind-battery system

Microgrids, characterized by their ability to work individually or in combination with the main power system, play a pivotal role in addressing the growing demand for reliable and sustainable energy solutions. This work concentrates on the integration of sustainable energy sources, specifically photovoltaic (PV), and wind generation and a battery storage system within a microgrid framework. Additionally, a power flow control strategy is implemented to enhance the dynamic behaviour and stability of the microgrid. The proportional-integral (PI) controller is a fundamental component in regulating the microgrid’s power flow, ensuring optimal performance under varying operating conditions. However, tuning the PI controller parameters is a difficult task because of the dynamic and nonlinear nature of renewable energy sources. In this work, the application of the Enhanced Randomized Harris Hawk Optimization (ERHHO) to fine-tune the PI controller is proposed, using the algorithm’s ability to mimic the hunting behaviour of hawks in finding optimal solutions. The PV-Wind-Battery microgrid system is modelled, and the proposed algorithm is employed to optimize the PI controller parameters for efficient energy management. The ERHHO algorithm’s exploration-exploitation balance is harnessed to navigate the complex solution space and converge to optimal PI controller settings, thereby enhancing the microgrid’s stability and performance. The study evaluates the effectiveness of the proposed ERHHO-based PI controller tuning through comprehensive simulations. Performance metrics such as transient response, overshoot, settling time, and steady-state error are analysed to validate the robustness and efficiency of the proposed method. Compared to its nearest optimization algorithm, with the proposed algorithm rise time is reduced by 50%, overshoot is reduced by 75%, settling time is reduced by 66%, and finally, a percentage of reduction of steady-state error is 45%. The outcomes of this research contribute to the advancement of microgrid control strategies, offering a novel approach to PI controller tuning in the context of diverse renewable energy sources. The integration of the Harris Hawk Optimization algorithm provides a promising avenue for enhancing the operational efficiency and reliability of microgrids, paving the way for sustainable and resilient energy systems in the aspect of growing energy landscapes.

Optimal site and size of FACTS devices with the integration of uncertain wind generation on a solution of stochastic multi-objective optimal power flow problem

To reduce the Carbon footprint and reduce emissions from the globe, the world has kicked-off to leave reliance of fossil fuels and generate electrical energy from renewable energy sources. The MOOPF problem is becoming more complex, and the number of decision variables is increasing, with the introduction of power electronics-based Flexible AC Transmission Systems (FACTS) devices. These power system components can all be used to increase controllability, effectiveness, stability, and sustainability. The added uncertainty and variability that FACTS devices and wind generation provide to the power system makes it challenging to find the right solution to MOOPF issues. In order to determine the best combination of control and state variables for the MOOPF problem, this paper develops three cases of competing objective functions. These cases include minimizing the total cost of power produced as well as over- and underestimating the cost of wind generation, emission rate, and the cost of power loss caused by transmission lines. In the case studies, power system optimization is done while dealing with both fixed and variable load scenarios. The proposed algorithm was tested on three different cases with different objective functions. The algorithm achieved an expected cost of $833.014/h and an emission rate of conventional thermal generators of 0.665 t/h in the case 1. In Case 2, the algorithm obtained a minimum cost of $731.419/h for active power generation and a cost of power loss is 124.498 $/h for energy loss. In Case 3, three objective functions were minimized simultaneously, leading to costs of $806.6/h for emissions, 0.647 t/h, and $214.9/h for power loss.

Influence of bioenergy and transmission expansion on electrical energy storage requirements in a gradually decarbonized European power system

With the ongoing energy transition in Europe, the share of variable renewable energies (VRE) in the power system increases steadily and different flexibility options gain importance. This work investigates the deployment of supply side flexibility options in the future European power system by energy system modelling focusing on classic power storage technologies, hydrogen storage, generation by biomass and grid expansion. While investigating different flexibility availability scenarios, for the first time the competition of long-term flexibility options is investigated across a broad decarbonization spectrum. In all scenarios, an increasing need for additional flexibility options is found for moderate decarbonization levels with VRE shares higher than 50%. From there on, the deployment of power storages grows nonlinearly, and eventually features a remarkable leap when approaching a zero-emission system, which is mainly driven by hydrogen storage investments. This also comes along with a leap in the total system costs. When the expansion of biomass capacities is available, biomass partly takes over the function of hydrogen as a long-term flexibility option. When enabling grid expansion, additional transmission capacities lead to a stronger integration of wind generation, while (especially short-term) storage requirements for higher decarbonization levels decrease. Both the additional investments into biomass generation and transmission grid result in up to 4% lower total system costs, yet, without completely displacing any other flexibility option. We conclude that the interaction of multiply flexibility options will lead to a cost minimal power system.

Stochastic Multi-Objective Optimal Reactive Power Dispatch with the Integration of Wind and Solar Generation

The exponential growth of unpredictable renewable power production sources in the power grid results in difficult-to-regulate reactive power. The ultimate goal of optimal reactive power dispatch (ORPD) is to find the optimal voltage level of all the generators, the transformer tap ratio, and the MVAR injection of shunt VAR compensators (SVC). More realistically, the ORPD problem is a nonlinear multi-objective optimization problem. Therefore, in this paper, the multi-objective ORPD problem is formulated and solved considering the simultaneous minimization of the active power loss, voltage deviation, emission, and the operating cost of renewable and thermal generators. Usually, renewable power generators such as wind and solar are uncertain; therefore, Weibull and lognormal probability distribution functions are considered to model wind and solar power, respectively. Due to the unavailability and uncertainty of wind and solar power, appropriate PDFs have been used to generate 1000 scenarios with the help of Monte Carlo simulation techniques. Practically, it is not possible to solve the problem considering all the scenarios. Therefore, the scenario reduction technique based on the distance metric is applied to select the 24 representative scenarios to reduce the size of the problem. Moreover, the efficient non-dominated sorting genetic algorithm II-based bidirectional co-evolutionary algorithm (BiCo), along with the constraint domination principle, is adopted to solve the multi-objective ORPD problem. Furthermore, a modified IEEE standard 30-bus system is employed to show the performance and superiority of the proposed algorithm. Simulation results indicate that the proposed algorithm finds uniformly distributed and near-global final non-dominated solutions compared to the recently available state-of-the-art multi-objective algorithms in the literature.

Towards the Sustainable Decommissioning of Fixed Platforms by Aligning Ecosystem Services and Wind Generation: A Brazilian Case

Brazilian legislation, as well as others around the world, determines that oil platforms, mainly fixed, must be decommissioned at the end of the feasibility of exploring the oil field, however, it is not common to consider the loss of ecosystem services that sustain life, the biodiversity and make resources available to humans. Considering the importance of the ecosystems that developed in the substructures of platforms, this work proposes a solution to maintain ecosystem services together with the development of clean, economically viable wind energy generation. For this, a proposal is built based on climatological, geographic, and wind power generation market data that seeks to demonstrate the financial viability of the specific case of the Robalo 1 Platform, which is located about 50 kilometers from other platforms to be decommissioned, in the Sergipe-Alagoas Basin, Brazil. It was found that when minimally valuing ecosystem services such as artificial reefs and integrating wind generation for the non-decommissioning of the fixed platform substructure, from the point of view of net present value, the project proves to be viable.

Development of optimal placement and sizing of FACTS devices in power system integrated with wind power using modified krill herd algorithm

PurposeThis study aims to intend and implement the optimal power flow, where tuning the production cost is done with the inclusion of stochastic wind power and different kinds of flexible AC transmission systems (FACTS) devices. Here, the speed with fitness-based krill herd algorithm (SF-KHA) is adopted for deciding the FACTS devices’ optimal sizing and placement integrated with wind power. Here, the modified SF-KHA optimizes the sizing and location of FACTS devices for attaining the minimum average production cost and real power depletions of the system. Especially, the objective includes reserve cost for overestimation, cost of thermal generation of the wind power, direct cost of scheduled wind power and penalty cost for underestimation. The efficiency of the offered method over several popular optimization algorithms has been done, and the comparison over different algorithms establishes proposed KHA algorithm attains the accurate optimal efficiency for all other algorithms.Design/methodology/approachThe proposed FACTS devices-based power system with the integration of wind generators is based on the accurate placement and sizing of FACTS devices for decreasing the actual power loss and total production cost of the power system.FindingsThrough the cost function evaluation of the offered SF-KHA, it was noted that the proposed SF-KHA-based power system had secured 13.04% superior to success history-based adaptive differential evolution, 9.09% enhanced than differential evolution, 11.5% better than artificial bee colony algorithm, 15.2% superior to particle swarm optimization and 9.09% improved than flower pollination algorithm.Originality/valueThe proposed power system with the accurate placement and sizing of FACTS devices and wind generator using the suggested SF-KHA was effective when compared with the conventional algorithm-based power systems.

Improving dynamic stability of wind integrated system by incorporating magnetic energy storage

AbstractThe need to incorporate renewable energy sources in the present grid due to energy demand and environmental issues has led the power industry to face new challenges and unexpected transformations. This new epitome to build a flexible power system with coordination of increasing intermittent renewable energy resources and delivering power efficiently with reliability demand new technologies. This work investigates the coupling of static synchronous compensator (STATCOM) with the most exclusive superconducting magnetic energy storage (SMES) device designed for the improved functioning of a grid network that is incorporated with a wind farm. The widespread integration of intermittent wind generators in the grid is disturbing its stability and reliability. The SMES is interlinked with the grid system via a power electronic interface and chopper for the energy exchange. This work suggests STATCOM as a power electronic interface and a three‐level chopper configuration with novel control scheme for enhancing the functioning of the test system. This integrated controller (STAT‐SMES) has been analyzed for grid‐connected doubly fed induction generator wind farm under different fault conditions and time durations. The results have been compared without any controller, with STATCOM only, and with the proposed STAT‐SMES controller using MATLAB. The simulation outcomes prove that coupled control scheme is effective and better in handling wind farm integration issues.

Integration of solar and wind power generation in the BRICS countries: An analysis of investment parameters and challenges

Subject. This article explores the technological and economic barriers to the integration of solar and wind-driven electric power stations into the electric power system, and it analyzes the options for choosing the optimal investment project when comparing parameters in the format of the BRICS countries. Objectives. The article aims to define and systematize the parameters of investment efficiency of solar and wind power generation construction projects in the BRICS countries. Methods. To assess investment indicators, we used well-established measures in international practice: Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PbP), and Aggregate Capital Expenditures (CAPEX). Results. The article identifies and systematizes the fundamental barriers in the implementation of projects in the field of renewable energy sources (RES), among which there are political and institutional, informational, market, and cognitive ones. The article also considers options for overcoming these barriers through improving the institutional environment and State support. Conclusions and Relevance. Means to achieve economic efficiency and reliability of energy supply of the electric power system are getting transformed as the share of renewable energy sources increases and the transition to a new stage of the energy market's development gets actualized. The materials of the article can be used as a tool to determine the level of investment efficiency of renewable energy projects, taking into account the macroeconomic context.

Güneş Işınım Tahmini için Farklı Güneşlenme Durumlarından Faydalanan Hibrit Bir Yöntem Tasarımı

The constant expansion of solar energy has made the accurate forecasting of radiation an important issue. An accurate prediction of solar energy production is crucial for the effective integration of photovoltaic (PV) and wind generators in smart grids. The intermittent nature of solar energy poses many challenges to renewable energy system operators in terms of operational planning and scheduling. For this reason, forecasting solar radiation by means of the hybrid methods is becoming widespread. In this paper, a hybrid method for predicting solar radiation is proposed, wherein the prediction model is determined based on the clearness index. The study used two-year solar radiation data of the province of Mardin obtained from the Turkish State Meteorological Service (TSMS). As predictors, ANN, NARX networks, and Ridge regression methods were used, and the training data were modeled with all three approaches in the first stage of the study. The clearness index was determined into three ranges; slightly cloudy, cloudy, and mostly cloudy. The training data were modeled with three methods used as estimators, and the success of each method was examined in each defined clearness index range. As a result, in the hybrid prediction algorithm, the clearness index is first estimated using artificial neural networks, and then the future solar radiation value is predicted by using the most successful model within the predicted clearness index range. Experimental results show that more successful predictions are made with the proposed hybrid method than when models are used individually.

Flexibility-based improved energy hub model for multi-energy distribution systems

The increasing integration of variable wind generation has aggravated the imbalance between electricity supply and demand. Power-to-hydrogen (P2H) is a promising solution to balance supply and demand in a variable power grid, in which excess wind power is converted into hydrogen via electrolysis and stored for later use. In this study, an energy hub (EH) with both a P2H facility (electrolyzer) and a gas-to-power (G2P) facility (hydrogen gas turbine) is proposed to accommodate a high penetration of renewable energy. The EH is modeled and integrated into a demand response problem to mitigate voltage violations and overloads. The optimization problem is solved by a reinforcement learning method. A case study is presented to validate the proposed model and elaborate on the flexibility potential of integrating P2H into an MV CIGRE power distribution network with solar and wind sources.

Design of a smart microgrid with small-scale hydro generation: a practical case study

Microgrids are local electric grids integrating distributed generation and consumption, energy storage and management and power control. They can be an alternative for the energy supply of a house, a building, a small village or a wider region. Small-scale hydropower, in the range of 5 kW, consists of distributed generation systems gaining increasing interest. These so-called pico-hydro systems are becoming popular since they can take advantage of the integration of low-power wind generators and photovoltaic (PV) inverters, which are widely available at competitive prices. Thus, pico-hydro systems are not only relevant for energy generation in off-grid systems in remote areas but also for new contexts where the utility grid is available. This paper presents the design of a smart microgrid with small-scale hydro generation. It is a practical case study with the integration of two grid-connected pico-hydro turbines: a low-head propeller turbine and a water wheel. The microgrid was designed and implemented in a small museum: Casa da Seda (House of Silk). The energy generation is based on the complementarity between hydro and PV. The microgrid can operate in both grid-connected and islanded modes and feeds the House of Silk loads. The connection of both pico-hydro turbines into the microgrid is based on the integration of wind generators and PV inverters.

Research on power system transient stability with wind generation integration under fault condition to achieve economic benefits

For a few decades, electric power growth and demand have increased globally. To fulfil this demand, distribution generators (DGs) have been installed in the power system. To get numerous benefits such as comprising reduction of power losses, improvement of (voltage, current, rpm) transient stability, leads to economic benefits. DGs integration in the distribution power system (DPS) was reported as an optimal generator placement (OGP) problem to maximize these benefits. This paper performs wind turbine (WT) integration while considering the internal generator's (rpm) transient stability under fault conditions. A power loss index (PLI) method is implemented to select potential candidate buses for generator placement. A self-sorting analytical approach (SSA) is also used to determine the optimal bus and WT size. The IEEE 14-bus DPS is used as the OGP test system to demonstrate the proposed method's effectiveness in terms of power losses reduction, system transient stability, and economic benefits. The IEEE 14 Bus system was considered using MATLAB with a 3.2 GHz processor to perform the said approach. WT integration suggested in case-2 results in a 65.80% power losses reduction, which leads to a 60.88% enhancement of economic benefits of the DPS.

RETRACTED: Integration of hydrogen storage system and wind generation in power systems under demand response program: A novel p-robust stochastic programming

Penetration of renewable energy sources (RESs) in power systems increase all over the world to overcome current challenges, most importantly environmental issues. Beside advantages of RESs, their integration into the power systems have imposed various challenges considering uncertain and intermitted power output. To cope with these challenges, utilizing energy storage systems with renewable energy sources alongside the demand response (DR) programs are considered as reliable solutions. On the other hand, in an uncertain environment, minimizing worst-case cost or regret is counted as an important criterion to evaluate operation of any system under uncertain parameters. Therefore, in this paper, optimal operation of power systems is solved under penetration of wind turbines, hydrogen storage system, and DR programs in an uncertain environment. To guarantee robust operation of the system under the worst-case scenario, a novel stochastic p-robust optimization method (SPROM) is proposed which combines both stochastic programming and robust optimization approaches where minimizes the worst-case cost or regret level. The performance of the developed model is evaluated considering a six-bus test system under two cases as stochastic optimization (SO) and SPROM. Obtained results show that the maximum regret level is reduced considerably using the proposed SPROM comparing with pure stochastic method.

Identification of Extreme Wind Events Using a Weather Type Classification

The identification of extreme wind events and their driving forces are crucial to better integrating wind generation into the power system. Recent work related the occurrence of extreme wind events with some weather circulation patterns, enabling the identification of (i) wind power ramps and (ii) low-generation events as well as their intrinsic features, such as the intensity and time duration. Using Portugal as a case study, this work focuses on the application of a weather classification-type methodology to link the weather conditions with wind power generation, namely, the different types of extreme events. A long-term period is used to assess and characterize the changes in the occurrence of extreme weather events and corresponding intensity on wind power production. High variability is expected under cyclonic regimes, whereas low-generation events are most common in anticyclonic regimes. The results of the work provide significant insights regarding wind power production in Portugal, enabling an increase in its predictability.

The value of consumer acceptance of controlled electric vehicle charging in a decarbonizing grid: The case of California

Plug-in electric vehicles charged with zero-carbon electricity are important for decarbonizing regional energy systems. Flexible charging of these vehicles aids with grid integration of wind and solar generation but may require drivers to provide information about their travel patterns and allow grid operators to control the charging of their vehicles. Limited acceptance of flexible charging can potentially limit greenhouse gas emissions reductions from electric vehicle deployment. Therefore, here we assess how varying the extent of consumer acceptance of flexible charging affects electric vehicle greenhouse gas emissions reductions in a highly decarbonized California grid (>70% zero-carbon), a region with mandated zero-emission vehicle deployment and electricity decarbonization targets. We quantify the monetary value of flexible charging based on the reduction in stationary storage required to achieve a given zero-carbon penetration as flexible charging is adopted. We find that increased participation in smart charging and vehicle-to-grid increases zero-carbon generation uptake by up to 5.2% and 11.1%, respectively. The value of smart charging only reaches $87 per vehicle-year while that for vehicle-to-grid can reach $2,850 per vehicle-year. Non-monetary incentives may be needed to increase smart charging participation. These results can inform future analyses on the supply and demand for participation in flexible charging programs.

Second‐order sliding mode control of wind turbines to enhance the fault‐ride through capability under unbalanced grid faults

AbstractThe integration of wind generation to the grid is growing rapidly across the world. As a result, grid operators have introduced the so‐called grid codes (GC), which nowadays include a range of technical conditions and requirements, which wind generators must fulfill. Among these, the low‐voltage ride through (LVRT) is a requirement for wind turbines to stay connected to the grid and continue to operate during the disturbance. In this study, a control structure, combining inertial kinetic energy storage with a crowbar circuit, is proposed to enhance the ride through capability of a wind turbine generator (WTG) based on a wound‐field synchronous generator (WFSG) under unsymmetrical voltage dips. For the grid‐side converter (GSC), a decoupled double synchronous reference frame (DDSRF) d‐q current controller is used. Furthermore, a second‐order sliding mode controller (SOSMC) with super‐twisting (ST) algorithm is proposed for the GSC and the machine‐side converter (MSC) to improve the response speed and achieve an accurate regulation of the dq‐axis current components simultaneously. The main objectives of the GSC are to achieve a balanced, sinusoidal current and smooth the real and reactive powers to reduce the influence of the negative‐sequence voltage. A series of simulations are presented to demonstrate the effectiveness of the proposed control scheme in improving the LVRT capability of the WFSG‐driven wind turbine and the power quality of the system under unbalanced grid voltage conditions.

A novel sensitive based approach to ATC enhancement in wind power integrated transmission system

Available transfer capability (ATC) plays an important role for both buyers and suppliers in the deregulated power system. As the integration of renewable energy sources is taking place very quickly in the system. It is not suggested to place wind generation (WG) at any point of the system. In this paper, the optimal location of buses for WG using a novel sensitive based formula is proposed. The zones are formed using the transmission congestion distribution factor (TCDF) values. The average transmission congestion distribution factor (ATCDF) is used to locate the optimal point of the system. When the WG is integrated into the system its obvious ATC increases but at which point the ATC values will be more is considered as the main objective. The WG is placed at every bus in the zone created by the TCDF value and the ATC value is calculated. The test is performed on the IEEE-30 bus system in MATLAB coding environment. The comparison result shows the ATC value enhances more when the WG is placed according to the proposed method. The proposed method also helps to find multiple locations for WG installation according to ATCDF value.HighlightsAverage TCDF value is proposed to find the optimal bus location for getting more ATC value.Integration of WG according to highest ATCDF value in the system.Comparison of ATC obtain from the integration of WG at different buses in sensitive zones.Validation of a proposed method by comparing the ATC value obtain from integrating WG at different buses.The optimal bus location based on the ATCDF value gave enhance ATC value compare to the other bus location.ATCDF value helps in finding the multiple locations for integration of WG in the system.

Probabilistic estimation model of power curve to enhance power output forecasting of wind generating resources

Wind-generating resources are variable and uncertain compared to traditional power generation resources. The accurate short-term forecasting of power outputs is essential to the extensive integration of wind generation into power grids. The variability of wind speed leads to uncertainty in wind power outputs. Consequently, forecasting errors increase the uncertainty of wind power forecasts. In this paper, we propose the probabilistic power curve estimation to enhance power output forecasting of wind generating resources. In order to enhance the wind power output forecasting, the probabilistic approach such as theoretical Weibull distribution parameters and Monte-Carlo simulation method is applied, the new multiple segments of the existing power curve are used for practical probabilistic power curve and spatial interpolation modeling based on Ordinary Kriging techniques is proposed for generating wind speed forecasting outputs. In addition, the new power slope estimation of the forecasting power outputs is proposed. To validate the proposed probabilistic power curve model, empirical data from the Jeju Island’s wind farms are considered in South Korea. The proposed probabilistic power curve model will contribute to the accurate estimation of the relationships between measured wind speeds and electrical power outputs, thus quantifying the uncertainties in power energy conversion.