Penetration Of Renewable Power Generation Research Articles

Decentralised Hybridised Energy Management Systems (DHEMS) in power grids

The integration of electric batteries along the power supply chain is crucial for the transformation of the energy sector towards a new flexible grid that allows the penetration of renewable power generation while ensuring stability and supply security. Batteries penetration in the grid can be boosted through an efficient management of heterogeneous generation sources, controllable loads and batteries, according to different criteria of stability, efficiency, cost, maintenance and power flow requirements. The Distributed Hybrid Energy Management System (DHEMS) is a management software tool able to solve an optimization problem maximizing renewable energy sources exploitation. The DHEMS has been designed with two control layers. First, the Cloud DHEMS layer accepts external setpoints (from a VPP, DSO or TSO) and dispatchs the total active and reactive power to be exchanged with the grid by a set of distributed plants. Second, the Local DHEMSs are in charge of distributing received set points and commands among the local sets that form each power plant. Different real control and communication tests have been done, in La Plana facility (owned by Siemens Gamesa Renewable Energy).

Comparison of Voltage Regulation of a Smart Load for 3 Bus System and 15 Bus System under High Penetration of Wind Energy System

Abstract: Modern study about utilizing energy from renewable energy sources was stimulus due to emerging oil crisis in older days due to uncontrolled use of conventional energy sources. Renewable Power Generation from wind and solar energy has become a significant proportion for the overall power generation in the grid. High penetration of Renewable Power Generation (RPG’s) effectreliable operation of bulk power system due to fluctuation of frequency and voltage of the network. The main objectives of high penetration of Renewable Power Generations in distribution system are Regulation of voltage, Mitigating voltage fluctuations due to flickers and Frequency control. The design and control of voltage regulation system using smart loads (SL’s) under large penetration of renewable energy system in distribution level is to be studied with the help of FACT devices like Static Compensator (STATCOM) and It is one of the fast active devices with accurate voltage regulation capability and most importantly for the sensitive/critical loads. Electric spring (ES) is proposed as compelling technique for guideline of framework voltage under fluctuating RPG's with next to no guide of correspondence framework [1]. It is a converter-based framework with self-commutated switches in span design, which is associated with non-basic burdens in series to go about as savvy load. These Smart Loads are controlled to direct voltage across basic burdens and hence partaking popular side administration. Expanded entrance of RPG’s, basically factor speed wind energy transformation framework is having impact on voltage and power quality [1][2]. In this paper, A contextual analysis of impact of variable speed wind energy framework on voltage is completed and which is demonstrated with fluctuating breeze speed. Execution examination of keen burdens are to be contrasted and existing receptive power compensator burdens and Improvement in voltage profile on test feeder is directed on a 3 Bus system and 15 Bus system. Keywords: Renewable energy system (RES), Electric spring (ES), STATCOM, Voltage Flicker, Smart load

Impact of optimal power generation scheduling for operating cleaner hybrid power systems with energy storage

De-carbonization of the electric power system, reduction in greenhouse gas emissions and the enhancement of environmental security have emphasized the need for the optimal utilization of renewable/cleaner energy in power system operation. However, unpredictability and intermittency are the major barriers that limit the penetration of renewable power generation (RPG). In this context, large-scale energy storage (LSES) facilities may prove to be an effective means for mitigating the long-term volatility and vulnerability of RPG and subsequently reduce the power dependence on fossil fuel-based generation systems. Thus, the possibility of increasing the penetration of cleaner power may be achieved by simultaneously implementing LSES and an intelligent scheduling strategy so that the hybrid power system may be operated with lower power loss, more cost-effective operation, and an enhanced voltage security. Previous researchers in multiple works have demonstrated the operational analysis and optimization of hybrid configurations with and without storage in stand-alone, islanded and remote operations. In contrast, attempts have been made in this work to incorporate, analyse and validate the impact of LSES facilities (ie, pumped hydroelectric storage (PHS) and compressed air energy storage (CAES)) with benchmark power grid configuration (ie, New-England-39 bus).In this work, a comparative analysis among different hybrid configurations, particularly wind-solar-thermal, wind-solar-hydro-thermal, wind-solar-hydro-thermal-PHS and wind-solar-hydro-thermal-CAES is conducted within a multi-objective optimization environment. The proposed design problem is analysed with the inclusion of real-time constrictions in the form of stochastic variation in renewable power output and random disruptions. The modified bacteria foraging algorithm is used to evaluate the optimum generation schedule for which the operational objectives will be achieved for the design problem. Further, using fuzzy membership function, the trade-off between conflicting objectives is portrayed in Pareto optimal domain. Among different hybrid configurations considered, the HPS which incorporates the CAES system exhibits the most superior performance.

Robust Assessment of Short-Term Wind Power Forecasting Models on Multiple Time Horizons

The massive penetration of renewable power generation in modern power grids is an effective way to reduce the impact of energy production on global warming. Unfortunately, the wind power generation may affect the regular operation of electrical systems, due to the stochastic and intermittent nature of the wind. For this reason, reducing the uncertainty about the wind evolution, e.g. by using short-term wind power forecasting methodologies, is a priority for system operators and wind producers to implement low-carbon power grids. Unfortunately, though the complexity of this task implies the comparison of several alternative forecasting methodologies and dimensionality reduction techniques, a general and robust procedure of model assessment still lacks in literature. In this paper the authors propose a robust methodology, based on extensive statistical analysis and resampling routines, to supply the most effective wind power forecasting method by testing a vast ensemble of methodologies over multiple time-scales and on a real case study. Experimental results on real data collected in an Italian wind farm show the potential of ensemble approaches integrating both statistical and machine learning methods.

Feasibility Studies of a Converter-Free Grid-Connected Offshore Hydrostatic Wind Turbine

Owing to the increasing penetration of renewable power generation, the modern power system faces great challenges in frequency regulations and reduced system inertia. Hence, renewable energy is expected to take over part of the frequency regulation responsibilities from the gas or hydro plants and contribute to the system inertia. In this article, we investigate the feasibility of frequency regulation by the offshore hydrostatic wind turbine (HWT). The simulation model is transformed from NREL (National Renewable Energy Laboratory) 5-MW gearbox-equipped wind turbine model within FAST (fatigue, aerodynamics, structures, and turbulence) code. With proposed coordinated control scheme and the hydrostatic transmission configuration of the HWT, the ‘continuously variable gearbox ratio’ in turbulent wind conditions can be realised to maintain the constant generator speed, so that the HWT can be connected to the grid without power converters in-between. To test the performances of the control scheme, the HWT is connected to a 5-bus grid model and operates with different frequency events. The simulation results indicate that the proposed control scheme is a promising solution for offshore HWT to participated in frequency response in the modern power system.

Day-ahead spatiotemporal wind speed forecasting using robust design-based deep learning neural network

The operations of power systems are becoming increasingly challenging due to the high penetration of renewable power generation, which is uncertain and stochastic. Highly accurate day-ahead renewable power forecasting helps operators in a dispatch center schedule power generations for conventional thermal generators. Efficient day-ahead renewable power forecasting requires information from widely separated locations. This paper proposes a novel day-ahead spatiotemporal wind speed forecasting method based on a convolutional neural network (CNN), which is an image-based deep learning method. This paper exploits a robust design based on Taguchi’s orthogonal array to determine the numbers of cascade/parallel layers, convolutions in each layer, kernels and hidden layers, as well as the kernel size, zero-padding and dropout ratio in the proposed CNN to ensure that the proposed CNN is insensitive to the seasonal characteristics of wind speeds while retaining high accuracy. In this paper, the day-ahead forecasting of wind speed at an offshore wind farm (Fuhai) near Taiwan is performed using historical wind speeds at seven sites in Taiwan, China, South Korea and the Philippines. Simulation results reveal that the proposed robust design-based CNN outperforms existing methods.

Impact of Generation Flexibility on the Operating Costs of the Taiwan Power System Under a High Penetration of Renewable Power

The penetration of renewable energy is gradually increasing. Therefore, power system flexibility, which is required to maintain system security and to cope with renewable generation uncertainty, becomes more important. Various technologies, such as flexible generators, demand management, energy storage, network reconfiguration, and even efficient system operations, are capable of improving power system flexibility. Nevertheless, among them, flexible generation resources can provide a direct way to enhance the power system flexibility. This article first applies the fuzzy analytic hierarchy process to calculate the flexibility index of generation resources. Then, the process of unit scheduling is implemented under various operation scenarios to investigate the relationship between the generation flexibility and the cost of unit scheduling. Finally, the required flexibility and the corresponding capacity of flexible generators under a specified penetration of renewable power generation are investigated under different operation scenarios.

Spatio-Temporal Flexibility Management in Low-Carbon Power Systems

The deepening penetration of renewable power generation is challenging how the minute balancing of supply and demand is carried out by power system operators. Several proposals to short-term operational planning rely on robust optimization to offer guarantees on the ability of the operator to meet a wide array of possible scenarios. The main downside of these approaches is their conservative results whose operating costs and/or carbon footprint may be sub-economical. Such results come by because these approaches immunize their solutions for the required level of security against realizations of potential events within their uncertainty set. Moreover, these approaches also often ignore the inherent time and spatial couplings of wind and solar generation variability. In this article, we seek to reduce the conservativeness of the robust solution by proposing the concept of spatio-temporal flexibility requirement envelopes. We show how it is able to efficiently capture and model the temporal trends and spatial correlation of multisite renewable generation and load. A mathematical program for energy scheduling is also developed using the projections of this envelope. We showcase the use and advantages of spatio-temporal flexibility requirement envelopes and their associated scheduling approach in a microgrid and on a modified IEEE Reliability Test System.

Day-Ahead Solar Irradiation Forecasting Utilizing Gramian Angular Field and Convolutional Long Short-Term Memory

The operations of power systems are becoming more challenging on account of the high penetration of renewable power generation, including photovoltaic systems. One method for improving the power system operation involves making accurate forecasts of day-ahead solar irradiation, enabling operators to minimize uncertainty in managing the balance between generation and load. To overcome the limitations of Long Short-term Memory (LSTM) in a one-dimensional forecasting problem, this work proposes a novel method in forecasting solar irradiation by encoding time-series data into images using the Gramian Angular Field and the Convolutional LSTM (ConvLSTM) network. The pre-processed data become a five-dimensional input tensor that is perfectly suitable for ConvLSTM. The ConvLSTM network uses convolution operations in its input-to-state transition and state-to-state transition. The network thus enables time-series forecasting by a feature-rich approach, which ultimately provides competitive forecasting performance despite the use of a small dataset. The proposed method was evaluated in day-ahead solar irradiation forecasting using a univariate dataset of Global Horizontal Irradiation (GHI) data from Fuhai in Taiwan. The proposed method was resampled using 5×2-fold cross-validation, and assessed using the Wilcoxon signed-rank test to determine the statistical significance of the result. It outperformed benchmark methods such as Autoregressive Integrated Moving Average (ARIMA), Convolutional Neural Network cascaded with Long Short-term Memory (CNN-LSTM), and LSTM cascaded with a fully-connected (FC) network.

Markov Model-Based Energy Storage System Planning in Power Systems

Battery energy storage systems (BESSs) can regulate the supply of energy from an electric utility, and improve the reliability and stability of the power system, which has high penetration of renewable power generation. This paper determines the optimal sizes (MWh) and capacities (MW) of BESSs in a distribution system that is composed of photovoltaic (PV) arrays. The objective is to minimize the investment cost, replacement cost, operation/maintenance cost, and the net present values of both the energy loss cost and to maximize the annual arbitrage benefit (AAB). The energy loss cost and AAB incorporate time-of-use (TOU) tariffs. The objective is subject to equality constraints (power flow equations and chronological ESS balance constraints) and inequality constraints (voltage, line flow, and BESS operational limits). Because the studied problem involves many constraints and variables in the chronological domain, traditional optimization methods cannot be applied directly to this large-scale problem. Assume that the chronological PV powers and loads are stochastic process, where the description of present state fully contains the information for the future evolution. Thus, Markov models are implemented by considering probability/duration/frequency of PV powers and loads to reduce the numbers of constraints and variables. The Markov states are attained using a clustering algorithm that is based on fuzzy-c-means. Simulation results obtained from a 33-bus system consisting of a 1 MW PV farm and six BESSs reveal the applicability of the proposed method. Simulation results indicate that the TOU tariff markedly affects the allocation of BESSs and the AAB benefit is generally smaller than the costs of the BESSs and energy losses.

Risk-Based Uncertainty Set Optimization Method for Energy Management of Hybrid AC/DC Microgrids With Uncertain Renewable Generation

Uncertainties associated with the increasing penetration of renewable power generation (RPG) in microgrids have introduced numerous challenges to their effective energy management. This paper proposes a novel risk-based uncertainty set optimization method for the energy management of typical hybrid AC/DC microgrids, where RPG outputs are considered as the major uncertainties. The underlying risks of RPG curtailment and load shedding are formulated in detail based on the probabilistic distribution of forecasted RPG values, and are further considered in the objective functions. The proposed model is solved using a piecewise linearization method combined with the quadratic Newton-Gregory interpolating polynomial technique to linearize the variable integration limit terms of the underlying risks, while the Chebyshev consistent linear approximation method is proposed to approximate the non-linear terms of the bi-directional converter conversion efficiency. Finally, the proposed model is reformulated as a mixed integer linear programming problem, and effectively solved using a high-performance solver. The proposed method is applied in simulations of an actual hybrid AC/DC microgrid system in China to demonstrate its effectiveness, good applicability, and robustness in comparison to standard robust optimization methods. The impact of RPG prediction accuracy and electric vehicle battery loss cost on the obtained solutions are further analyzed and discussed.

Economic and environmental benefits of increasing the renewable energy sources in the power system

Increasing Renewable Energy Sources (RES), impact the power system. Moreover, the system equilibrium and stability are difficult because constant generation cannot be generated from RES concerning conventional energy sources. This paper studies the effect of RES on the power system through the actual application of the Egyptian power system. The strategy of the Egyptian power system aims at increasing the proportion of RES, especially, Wind Power (WP) and photovoltaic (PV). Therefore, Different operation scenarios are presented to illustrate the impact of RES on the Egyptian network. Also, the economic and environmental benefits of increasing the Penetration of Renewable Power Generation are presented by calculating the expected annual reductions of CO2 emission (ER), certified emission reduction (CER) and the fuel-saving amount. Therefore, how to decrease the cost of electricity (DCOE) model when calculating the cost-benefit analyses from RES. The results show the economic benefits of the point of view of the amount of savings achieved when bringing renewable energy to each scenario. Finally, the economic study shows the benefits renewable energy sources have provided, where the RES by the end of 2022 will reduce the emission of carbon dioxide to 46405×103 tCO2 which yield a return of 433427.6×103 $ according to CER price and the savings of the fuel will reach 19066 ktoe. The model was created using DIgSILENT power factory software.

Wide-area monitoring and early-warning of subsynchronous oscillation in power systems with high-penetration of renewables

With the increasing penetration of renewable power generation (RPG) in modern power systems, subsynchronous oscillation (SSO) induced by the interaction between RPG and the grid is posing a great threat to the system stability and equipment safety. To address this issue, this paper proposes a wide-area monitoring and early-warning system of SSO. It incorporates the locally deployed subsynchronous phasor measurement units (S-PMUs) and a centralized data center & subsynchronous dynamics analyzer (DC&SDA). Several key functions have been developed, including: (1) the subsynchronous state estimation (SSE) to form global view or observability of SSO dynamics, (2) the establishment of subsynchronous power & current flow to locate oscillation source/path, and (3) the real-time evaluation of subsynchronous stability using two complementary criteria, one based on the identification of oscillation damping and the other based on the frequency characteristics of the impedance models. The practicality and performance of the proposed system have been demonstrated with electromagnetic transient (EMT) simulations on a practical system that suffered SSO incidents.

Enhanced Frequency Regulation Using Multilevel Energy Storage in Remote Area Power Supply Systems

Frequency support from renewable power generators is critical requirement to ensure the frequency stability of remote area power supply (RAPS) systems with high penetration of renewable power generation. However, traditional control strategies and the stochastic nature of wind resource constrain wind energy conversion system (WECS) such as permanent magnet synchronous generator (PMSG) from participating in frequency regulation. This work proposes to integrate hybrid energy storage including ultracapacitors (UCs) and lead-acid batteries (LABs) into a PMSG to provide frequency support. The UCs deal with fast changing frequency by emulating conventional inertial response, whereas the LABs mimic automatic governor response (i.e., primary frequency response). The mechanical power reserved in wind turbine using suboptimal maximum power point tracking strategy is utilized to restore system frequency (i.e., secondary frequency response). Moreover, supplementary control strategies are proposed to enable the UCs and LABs to assist primary frequency response and secondary frequency response, respectively. Simulation study and experimental test are carried out to validate the effectiveness of frequency response provided by the multilevel energy storage. The multilevel energy storage solution can effectively regulate RAPS system frequency while avoiding abrupt and frequent charging/discharging of the LABs and significant mechanical/electromagnetic stress on the WECS.

Demand Response From the Control of Aggregated Inverter Air Conditioners

Inverter air conditioners (ACs) account for a large proportion of air conditioning loads in many countries and, thus, contribute significantly to the peak loads in these areas, especially in summer. On the other hand, as an important category of thermostatically controlled load with thermal energy storage capability, inverter ACs also have the potential to provide considerable flexibility for electric power systems that are faced with increasing challenges posed by high penetration of renewable power generation. This paper focuses on the demand response from the control of the aggregated inverter ACs for load reduction. A virtual energy storage system (VESS) model that encapsulates the room with an inverter AC was established based on the electric model of an inverter AC and the thermodynamic model of a room. Based on the VESS model, a virtual state of charge (VSOC) priority-based load reduction control method with temperature holding and linear recovery strategies was proposed. The VSOC priority based control was designed to decrease the negative impact of load reduction on customers’ thermal comfort from the perspective of the whole AC population. The temperature holding strategy was designed to reduce the electric power of an AC while ensuring that the indoor temperature is always below the allowable limit. The linear recover strategy was proposed to reduce the load rebound after load reduction. Four cases were studied regarding the operation and load reduction of the 100 inverter ACs, and the simulation results verified the models established and the effectiveness and advantages of the proposed load reduction control method.

A General Model for Estimating Emissions from Integrated Power Generation and Energy Storage. Case Study: Integration of Solar Photovoltaic Power and Wind Power with Batteries

The penetration of renewable power generation is increasing at an unprecedented pace. While the operating greenhouse gas (GHG) emissions of photovoltaic (PV) and wind power are negligible, their upstream emissions are not. The great challenge with the deployment of renewable power generators is their intermittent and variable nature. Current electric power systems balance these fluctuations primarily using natural gas fired power plants. Alternatively, these dynamics could be handled by the integration of energy storage technologies to store energy during renewable energy availability and discharge when needed. In this paper, we present a model for estimating emissions from integrated power generation and energy storage. The model applies to emissions of all pollutants, including greenhouse gases (GHGs), and to all storage technologies, including pumped hydroelectric and electrochemical storage. As a case study, the model is used to estimate the GHG emissions of electricity from systems that couple photovoltaic and wind generation with lithium-ion batteries (LBs) and vanadium redox flow batteries (VFBs). To facilitate the case study, we conducted a life cycle assessment (LCA) of photovoltaic (PV) power, as well as a synthesis of existing wind power LCAs. The PV LCA is also used to estimate the emissions impact of a common PV practice that has not been comprehensively analyzed by LCA—solar tracking. The case study of renewables and battery storage indicates that PV and wind power remain much less carbon intensive than fossil-based generation, even when coupled with large amounts of LBs or VFBs. Even the most carbon intensive renewable power analyzed still emits only ~25% of the GHGs of the least carbon intensive mainstream fossil power. Lastly, we find that the pathway to minimize the GHG emissions of power from a coupled system depends upon the generator. Given low-emission generation (<50 gCO2e/kWh), the minimizing pathway is the storage technology with lowest production emissions (VFBs over LBs for our case study). Given high-emission generation (>200 gCO2e/kWh), the minimizing pathway is the storage technology with highest round-trip efficiency (LBs over VFBs).

Power‐oscillation evaluation in power systems with high penetration of renewable power generation based on network virtual inertia

With regards to the problems in which the power-oscillation characteristics of power systems with high penetration of renewable power generation are complex and difficult to effectively analyse, a novel network virtual-inertia method is proposed to evaluate the power oscillation in such systems. Firstly, the network virtual inertia is defined and developed. Secondly, the network virtual inertia is calculated and obtained based on the measurement information. Thirdly, the relationship between the network virtual inertia and generator inertia is constructed. Finally, the network virtual inertia of power systems with high penetration of renewable power generation is analysed. The evaluation method is effective and easy to implement. Simulations involving four-generator two-area containing renewable power generation were conducted under various disturbances to prove the effectiveness of the proposed method.

Numerical method for probabilistic load flow computation with multiple correlated random variables

Grid complexity is increasing progressively as the deepening penetration of renewable power generation and unpredictable demand, which necessitates an exhaustive assessment of system parameters in a probabilistic manner. In this study, the authors employ a dimensional reduction integral method to tackle the above problems challenged by dimensionality. Their approach transforms the multivariate raw moments into a linear sum of several one-dimensional integrals, which could be solved by Gauss quadrature. To handle the correlation between non-Gaussian input variables, Nataf transformation is used to map the inputs into the independent normal domain. Instead of commonly used series expansion such as A-type Gram-Charlier, Edgeworth or Cornish-Fisher, the probability distributions of output variables can be better approximated by C-type Gram-Charlier series with the calculated moments. The salient feature of the proposed method is demonstrated in a modified IEEE 118-bus test system with respect to both accuracy and run times.

Synthetic Inertia Control Strategy for Doubly Fed Induction Generator Wind Turbine Generators Using Lithium-Ion Supercapacitors

To address the system operability challenges due to the continuous reduction of system inertia by increasing penetration of renewable power generation, this paper proposes a new synthetic inertia control (SIC) strategy for doubly fed induction generator (DFIG)-based wind turbine generators. The proposed SIC scheme enables individual DFIG generators to contribute an effective inertial response which helps to stabilise the rate of change of frequency and minimize the maximum frequency deviations in particular under severe disturbance conditions. To achieve the proposed SIC, lithium-ion supercapacitors are required in the dc link of the DFIG power electronic conversion system to increase extra energy storage capability. The DFIG's dc voltage operates at various levels following the derived SIC algorithms for energy exchange with the connected ac grid, and the conventional control system for DFIG is modified to implement the algorithms. The functions and system benefits of SIC scheme are verified using a DFIG model connected to a power system and compared with the simulations of conventional DFIG control scheme. Practical issues such as size, cost, weight, connection configuration, and charging/discharging rate of lithium-ion capacitors for the SIC are discussed and relevant recommendations are presented.

Design and Performance of an Advanced Communication Network for Future Active Distribution Systems

AbstractThe design of a future active distribution network is crucial for the deployment of a smarter grid. The increasing penetration of renewable power generation and of dynamic loads increases t...