Penetration Level Of Renewable Generation Research Articles

Analysis of the optimum allocation of BESS for contingency support

The development of energy storage technologies is considered a key issue to increase the flexibility of future power systems and to achieve high penetration levels of renewable generation. In recent years, battery-based storage has attracted great interest due to the cost reduction of lithium-ion batteries. In addition, the modularity of batteries makes possible to develop not only grid-scale energy storage facilities connected at transmission voltage levels but also smaller distributed energy storage systems connected at distribution voltage levels. However, it must be taken into account that the size and services provided by batteries depend on their location in the power system. In this paper, a methodology to determine the optimal location of battery based electrical energy storage systems for contingency support is presented. The methodology has been applied to a Spanish power system to evaluate the effect of batteries on contingency support when installed either in the transmission network or in the distribution network.

Voltage Regulation Planning Based on Optimal Grid-Connected Renewable Energy Allocation Using Nature-Inspired Algorithms to Reduce Switching Cycles of On-Load Tap Changing Transformer

Due to the non-deterministic and probabilistic qualities of renewable energy resources (RER), integrating these resources has posed a challenge for energy companies and government policy planners. Hybrid power systems require frequent voltage regulation to address the changes in renewable generation and facilitate voltage management. This leads to an increment in the operating time of the tap changer in the power transformer, thus resulting in accelerated degradation of the on-load tap changer (OLTC) device. The primary focus of this paper is to optimally manage the penetration level of renewable generation while maintaining the minimum time for proper operation of voltage regulation devices. Accordingly, the major contribution of this paper is to address the switching cycle issues of the on-load tap-changing transformer. Initially, a framework is proposed to optimize the location and size of renewable power generation using a multi-objective cuckoo search optimization algorithm. The main goal here is to reduce power losses and tap changer operations while improving the system voltage profile under different levels of renewable energy penetration. The optimization problem takes into consideration the fluctuating nature of wind speed and solar irradiance for sunny and cloudy days over 24 h. The framework has been applied to the IEEE 57-bus and 118-bus systems with different load levels. The performance of the proposed approach has been benchmarked by comparing it with the genetic optimization algorithm to identify the higher potential buses for renewable generation allocation. The cuckoo search algorithm (CSA) shows outstanding results in reducing the number of switching cycles to about (37 – 43) % whereas the genetic algorithm (GA) only (17 – 18). In this sense, the number of changes in tap position when using the CSA is 1956 for sunny days and 1763 for cloudy days compared to GA 2558 for sunny days and 2547 for cloudy days. The voltage profiles for both algorithms are maintained in the range of (1.06 and 0.94) per unit. The results affirm the effectiveness of the proposed approach in determining the optimal placement and size of RER with voltage profile improvement and reduction in the switching cycles of OLTC.

Grid forming inverter and its applications to support system strength–A case study

With the increasing penetration level of renewable generation, a shortage of system strength becomes a concern for the stable operation of the power system. Most commonly, Inverter Based Resources (IBR) plants are operated with grid following inverters (GFLI). However, a grid forming inverter (GFMI), which work as a voltage source and does not require direct reference and system strength from the grid, is now receiving increased attention. Here, Hardware-in-the-loop (HIL) testing of a GFMI and its capability to actively damp sub-synchronous oscillations and to provide synthetic inertia has been discussed. The improvement to system strength and transient stability have been presented using modelling of the transmission network in the Queensland (Australia). The study shows that with proper tuning and coordination with other generation in the area, the GFMI BESS will not only provide system strength to the grid, but also solve other stability issues.

Distributionally Robust Co-optimization of Transmission Network Expansion Planning and Penetration Level of Renewable Generation

Transmission network expansion can significantly improve the penetration level of renewable generation. However, existing studies have not explicitly revealed and quantified the trade-off between the investment cost and penetration level of renewable generation. This paper proposes a distributionally robust optimization model to minimize the cost of transmission network expansion under uncertainty and maximize the penetration level of renewable generation. The proposed model includes distributionally robust joint chance constraints, which maximize the minimum expectation of the renewable utilization probability among a set of certain probability distributions within an ambiguity set. The proposed formulation yields a two-stage robust optimization model with variable bounds of the uncertain sets, which is hard to solve. By applying the affine decision rule, second-order conic reformulation, and duality, we reformulate it into a single-stage standard robust optimization model and solve it efficiently via commercial solvers. Case studies are carried on the Garver 6-bus and IEEE 118-bus systems to illustrate the validity of the proposed method.

Machine learning-based frequency security early warning considering uncertainty of renewable generation

Loss of massive generation caused by HVDC blocking or tripping large power plants is a severe threat to receiving-end grids' frequency security, especially those with a high penetration level of renewable generation. Frequency security early warning is necessary to send warning messages in time. With the warning messages, appropriate measures can be taken in advance to minimize possible losses. In this paper, a machine learning-based frequency security early warning method considering the uncertainty of renewable generation is proposed. It includes three core parts: future scenario generation, assessment model establishment, and early warning indicator establishment. In the future scenario generation part, Markov Chain Monte Carlo (MCMC) is combined with Generative Adversarial Networks(GAN) for generating numerous future scenarios reflecting possible future operation modes of the system, considering the uncertainty of renewable generation and loads. In the assessment model establishment part, the assessment model with clustering based on metric learning is applied to establish the machine learning-based frequency security assessment model. The model is continuously retrained with Domain Adaptation Metric Learning (DAML) and a transitive closure-based constraint propagation clustering approach to improve assessment accuracy. Future frequency security risk indicators are established in the early warning indicator establishment part based on future scenarios' assessment results. According to the risk indicators, future frequency security is classified into different early warning levels. The future frequency security can be expressed clearly and intuitively with the early warning levels. A simplified provincial power system of China is adopted as an example to verify the validity of the proposed early warning method.

Redesigning capacity market to include flexibility via ramp constraints in high-renewable penetrated system

Capacity markets can co-exist alongside the energy and ancillary markets to ensure the medium-term and long-term supply adequacy by remunerating the generation resources for their availability. With the substantially increasing deployment of renewable generation, the system needs more flexibility to quickly mitigate the variability and uncertainty caused by the renewable generation. However, the present capacity market model does not differentiate the flexibility of generating units, so it might not give appropriate pricing signals to more flexible units that are usually more expensive. This is particularly important considering the increasing penetration levels of renewable generation on the electric grid, which increases the need for flexible units. This paper proposes a novel capacity market model considering flexibility requirement (FR) under the high penetration levels of renewable generation. Therefore, the proposed model can give the market-clearing price (MCP) for not only peak load generation capacity but also flexibility requirement. Thus, flexible units will tend to receive more economic incentives than non-flexible units. In the case studies, the profitability of different generation resources under the proposed capacity market model is analyzed and compared to the present capacity market model. The results show that the proposed method can maintain system reliability with regard to both peak load and flexibility requirement efficiently.

Model-Free State Estimation Using Low-Rank Canonical Polyadic Decomposition

As electric grids experience high penetration levels of renewable generation, fundamental changes are required to address real-time situational awareness. This letter utilizes unique traits of tensors to devise a model-free situational awareness and energy forecasting framework for distribution networks. This letter formulates the state of the network at multiple time instants as a three-way tensor; hence, recovering full state information of the network is tantamount to estimating all the values of the tensor. Given measurements received from μphasor measurement units and/or smart meters, the recovery of unobserved quantities is carried out using the low-rank canonical polyadic decomposition of the state tensor-that is, the state estimation task is posed as a tensor imputation problem utilizing observed patterns in measured (sampled) quantities. Two structured sampling schemes are considered, namely, asynchronous slab and fiber sampling. For both schemes, we present sufficient conditions on the number of sampled slabs and fibers that guarantee identifiability of the factors of the state tensor. Numerical results demonstrate the ability of the proposed framework to achieve high estimation accuracy in multiple sampling scenarios.

Maximum frequency deviation assessment with clustering based on metric learning

Loss of massive generation caused by HVDC blocking or tripping of large power plants is a severe threat to the frequency security of receiving-end grids, especially those with a high penetration level of renewable generation. Due to the uncertainty of renewable generation, a large number of scenarios need to be checked for hour-ahead frequency security assessment, which is time-consuming if they are assessed with full time-domain simulation. To improve assessment efficiency, a maximum frequency deviation assessment method with clustering based on Metric Learning (ML) is proposed in this paper. The distance measure of samples is firstly adjusted by ML with Kernel Regression to make samples with similar frequency dynamics close to each other. The training process of ML is optimized with Mini-Batch Gradient Descent (MBGD) method. Then fuzzy k-means clustering is used to group the samples into multi clusters by maximizing the membership degree of each training sample with the distance measure adjusted by ML. Finally, corresponding to each cluster, a Support Vector Regression (SVR) model is established to build the relationship between maximum frequency deviation and steady-state power flow features, and Core Vector Regression (CVR) is used to accelerate the training process of SVR. The membership degree of the new scenario to assess with respect to each cluster is calculated according to the distance measure learned by ML to classify it into a specific cluster in the on-line assessment process. The corresponding SVR model is used to assess the maximum frequency deviation of the new scenario. The New England 39-bus system and a simplified provincial power system of China are adopted to verify the validity of the proposed assessment method.

Modelling and simulation of ternary pumped storage hydropower for power system studies

As the deployment of wind and solar energy increases in the USA, energy storage (ES) will play an important role in future electric power grids to help manage the variability from high penetration levels of renewable generation, ES can provide promising power/energy demand to coordinate with renewables generation, as a virtual power plant (VPP). There is an industry need for the capability in power system studies to model ternary pumped storage hydropower (T-PSH), a pumped storage technology that offers increased system benefits. This study presents a comprehensive vendor-neutral dynamic model of T-PSH in GE's commercial software positive sequence load flow. A new governor model is developed with detailed gate valve modelling and a shared-penstock function. Specifically, this model is designed to simulate the seamless transition among three operation modes: generation mode, pumping mode, and hydraulic short-circuit mode. The developed T-PSH model has been tested and validated on a 10-bus test system. A comparison study of T-PSH to conventional pumped storage hydropower has also been conducted on the Western interconnection system.

Efficient identification of critical load model parameters affecting transient stability

In this paper, an efficient sensitivity analysis (SA) technique is implemented to identify critical load model parameters affecting the transient stability of a power system. Transient stability is becoming more crucial in modern power systems due to the adoption of a deregulated market structure and an increasing penetration level of renewable generation. Research has revealed that power system loads have a significant influence on transient stability. Ranking of critical load model parameters ensures accurate power system simulation results with less effort and resources have been invested in building load models. The ranking has been made for different loading conditions in order to make the ranking applicable most of the time. Different types of load model have been applied to investigate their influence on transient stability. All simulations have been illustrated using a modified version of the 68 bus NETS-NYPS test system. The results revealed the important load model parameters and important loads, and the level of accuracy required for these critical parameters.

Optimal operation of electricity, natural gas and heat systems considering integrated demand responses and diversified storage devices

In recent years, the increasing penetration level of renewable generation and combined heat and power (CHP) technology in power systems is leading to significant changes in energy production and consumption patterns. As a result, the integrated planning and optimal operation of a multi-carrier energy (MCE) system have aroused widespread concern for reasonable utilization of multiple energy resources and efficient accommodation of renewable energy sources. In this context, an integrated demand response (IDR) scheme is designed to coordinate the operation of power to gas (P2G) devices, heat pumps, diversified storage devices and flexible loads within an extended modeling framework of energy hubs. Subsequently, the optimal dispatch of interconnected electricity, natural gas and heat systems is implemented considering the interactions among multiple energy carriers by utilizing the bi-level optimization method. Finally, the proposed method is demonstrated with a 4-bus multi-energy system and a larger test case comprised of a revised IEEE 118-bus power system and a 20-bus Belgian natural gas system.

Resiliency oriented integration of DSRs in transmission networks

Secure and reliable operation of power system in normal and contingency conditions is of great importance for system operator. Natural disasters can seriously threaten power systems' normal operation with catastrophic consequences. While hardening approaches may be considered for resiliency improvement, an application of a new and cost-effective technology is proposed in this study. A planning procedure is proposed for integrating distributed series reactors (DSR)into transmission grids for improving the resiliency against these disasters. DSRs are able to control power flows through meshed transmission grids and thus improve the power transfer capability and the penetration level of renewable generation. The problem of integrating DSRs into transmission grids is formulated as a mixed integer linear programming problem. Different load and wind profiles and a predefined number of disaster scenarios are considered in evaluating the impacts of DSRs on system's operational costs, wind curtailment and load shedding during disasters and normal condition. The uncertainty of wind generation can affect economic viability of DSRs deployment which is handled using information gap decision theory. It is implemented on the IEEE-RTS 24-bus test system and results show the functionality of DSRs in converting the conventional transmission grid into a flexible and dispatchable asset.

A Flexible Ramping Capacity Model for Generation Scheduling with High Levels of Wind Energy Penetration

The penetration level of renewable generation has increased significantly in recent years, which has led to operational concerns associated with the system ramping capability. Here, we propose the flexible ramping capacity (FRC) model, which considers the practical ramping capability of generation resources as well as the uncertainty in net load. The FRC model also incorporates the demand curve of the ramping capacity, which represents the hourly economic value of the ramping capacity. The model is formulated mathematically using ramp constraints, which are incorporated into unit commitment (UC) and economic dispatch (ED) processes. Simulations are carried out using a 10-unit system to compare the FRC model with conventional methods. We show that the FRC method can improve reliability and reduce expected operating costs. The simulation results also show that, by using the FRC model, system reliability can be satisfied at high wind power generation levels while achieving economic efficiency.