Renewable Energy Penetration Level Research Articles

Studying the Optimal Frequency Control Condition for Electric Vehicle Fast Charging Stations as a Dynamic Load Using Reinforcement Learning Algorithms in Different Photovoltaic Penetration Levels

This study investigates the impact of renewable energy penetration on system stability and validates the performance of the (Proportional-Integral-Derivative) PID-(reinforcement learning) RL control technique. Three scenarios were examined: no photovoltaic (PV), 25% PV, and 50% PV, to evaluate the impact of PV penetration on system stability. The results demonstrate that while the absence of renewable energy yields a more stable frequency response, a higher PV penetration (50%) enhances stability in tie-line active power flow between interconnected systems. This shows that an increased PV penetration improves frequency balance and active power flow stability. Additionally, the study evaluates three control scenarios: no control input, PID-(Particle Swarm Optimization) PSO, and PID-RL, to validate the performance of the PID-RL control technique. The findings show that the EV system with PID-RL outperforms the other scenarios in terms of frequency response, tie-line active power response, and frequency difference response. The PID-RL controller significantly enhances the damping of the dominant oscillation mode and restores the stability within the first 4 s—after the disturbance in first second. This leads to an improved stability compared to the EV system with PID-PSO (within 21 s) and without any control input (oscillating more than 30 s). Overall, this research provides the improvement in terms of frequency response, tie-line active power response, and frequency difference response with high renewable energy penetration levels and the research validates the effectiveness of the PID-RL control technique in stabilizing the EV system. These findings can contribute to the development of strategies for integrating renewable energy sources and optimizing control systems, ensuring a more stable and sustainable power grid.

Inertial Estimation in Power Grids Considering Load Contribution Based on FF-UKF

Abstract In modern power systems, the extensive integration of renewable energy sources leads to a reduction in system inertia, thereby affecting grid stability. This study proposes a novel method for estimating grid inertia, which, under quasi-steady state conditions, effectively incorporates load contribution by combining class noise signals collected by PMUs (Phasor Measurement Units) with the Forgetting Factor Unscented Kalman Filter algorithm (FF-UKF). The research initially analyzes the characteristics of various inertia sources in power systems dominated by renewable energy. Building on this, we propose a grid node equivalent inertia estimation model based on node equivalent frequency response, developing an FF-UKF-based algorithm that allows for the continuous dynamic estimation of inertia at various grid nodes. Simulation experiments conducted within the IEEE 39 bus system validate the method’s effectiveness and demonstrate the spatiotemporal distribution of grid inertia under varying levels of renewable energy penetration. This study is significant for understanding and enhancing frequency control and stability in power systems, providing key technical support for the modernization of the power system and the efficient integration of renewable energy.

Large-Scale BESS for Damping Frequency Oscillations of Power Systems with High Wind Power Penetration

With the high penetration of renewable energy into power grids, frequency stability and oscillation have become big concerns due to the reduced system inertia. The application of the Battery Energy Storage System (BESS) is considered one of the options to deal with frequency stability and oscillation. This paper presents a strategy to size, locate, and operate the BESS within the power grid and, therefore, investigate how sizing capacity is related to renewable energy penetration levels. This paper proposes an identification method to determine the best location of the BESS using the Prony method based on system oscillation analysis, which is easy to implement based on measurements while actual physical system models are not required. The proposed methods for BESS size and location are applied using MATLAB/Simulink simulation software (version: R2023a) on the Kundur 2-area 11-bus test system with different renewable energy penetration levels, and the effectiveness of the applied method in enhancing frequency stability is illustrated in the study cases. The case studies showed a significant improvement in steady-state frequency deviation, frequency nadir, and Rate of Change of Frequency (ROCOF) after implementing BESS at the selected bus. The integration of BESS can help to avoid Under-frequency Load Shedding (UFLS) by proper selections of size, location, and operating strategy of the BESS within the power grid.

Renewable energy incentives for project owners representing communities with heavily subsidised fossil fuel-based electricity

The significance of renewable energy (RE) as an essential component of sustainable cities is widely acknowledged. Nevertheless, the global transition towards RE faces numerous hurdles, including RE financing, particularly in regions where electricity generation from fossil fuels is heavily subsidized. This necessitates the formulation of effective incentives that can encourage project owners to invest in the transition towards RE. This manuscript proposes a comprehensive framework for incentivizing RE project owners and offers three types of incentives: capital-based, production-based, and grid support incentives. The effectiveness of the proposed set of incentives is evaluated through a detailed techno-economic analysis which aims to minimize Net Present Cost while considering power balance and grid constraints. It encompasses various aspects of RE systems, including optimal sizing of photovoltaics, batteries, inverters, and optimal power flow, at different levels of RE penetration. Additionally, the framework explores the possibility of achieving 100 % RE through off-grid systems, shedding light on potential challenges and opportunities. Further, economic indicators such as the Cost of Energy, Payback Period, Return on Investment, and Internal Rate of Return are also analyzed to evaluate the acceptability of the proposed framework. Results demonstrate that the presented incentivizing framework has the potential to facilitate RE adoption, positioning it as a valuable policy and investment opportunity for RE project owners. This yields substantial benefits including reduced financial burdens, improved return on investment, and access to diverse revenue streams.

Stability Analysis of Autonomous Microgrid Operated at Constant-Frequency with the Consideration of Reactive Power Balance

Large-scale autonomous microgrids have potential application values as they can increase renewable energy penetration level without compromising the stability of the existing large power systems. Before their widespread implementation, critical issues like stability analysis etc need to be solved. This paper analyses the stability in an autonomous microgrid operated at constant frequency with the consideration of reactive power balance. Difference equations of reactive power for the grid-forming generator are constructed separately from those for the grid-supporting and grid-feeding generators while the difference equations of the real power for all the generators are the same. For the voltage source inverter with its current controlled by proportional resonant controller, at the fundamental frequency, its output current is disentangled from its terminal voltage and is controlled to trace its reference accurately, namely . Therefore, each inverter can be modelled as an equivalent current source and the equivalent circuits for d-component and q-component can be separated from each other. Then, the nodal equations in matrix form for the microgrid system can be established readily. With these, the system level state-space equations are built to study the distribution of eigenvalues. By choosing proper coefficients for real power and reactive power reference generations and controller’s parameters, one can make all the eigenvalues falling in the left-hand-side of the complex plane. Therefore, the system is stable. Such a research paves the way for systematically searching good sets of coefficients and controller’s parameters which make system operate safely away from unstable region with necessary margin.

Method to Quantify Modal Interactions Between Converter Interfaced Generators and Synchronous Generators

The aim of this paper is to quantify the interactions between the oscillation modes of power systems within the realm of small-signal stability. This paper focuses on the interactions between Converter Interfaced Generator (CIG) oscillation modes and electromechanical oscillation modes of Synchronous Generators (SGs). It is difficult to determine the modal interactions using the existing analysis such as mode sensitivity, mode loci and participation factors. This paper proposes an extension to eigenvalue sensitivity analysis in order to determine the interaction between modes and impact of the interaction on system stability. Interaction coefficients are proposed to quantify the interaction between the modes. A modified IEEE 39-bus system with CIGs is considered to carry out the proposed analysis. The analysis is carried out to investigate the impact of PLL parameters on the interaction among the oscillation modes. The analysis is also carried out considering renewable energy penetration levels of 50-70%. It is observed that the interaction between CIG and electromechanical modes of SG results in increased participation of SGs' states in CIG modes. This increased participation of SG states in CIG modes results in reduced damping of oscillations in SG states.

A flexibility management system for behind-the-meter flexibility with distributed energy resources: A sensitivity analysis

The increasing uncertainty in power systems with high penetration levels of renewable energy fosters the research on the control, management, and operation of distributed energy resources and its coupling with the current grid operation and control scheme alongside future energy markets and flexibility services. This work proposes a flexibility management system for the behind-the-meter flexibility of the distributed energy resources, which employs an adaptive auto-regression forecasting module to predict the energy cost overruns of the system throughout the energy exchanges at the point of interconnection. In addition, a cost–benefit analysis is used to evaluate and optimize the DER set-point profiles to minimize the installation’s energy cost, which, together with power redispatching and unit recommitment modules, are executed to adjust the DER operating points according to real-time deviations and short-term flexibility requests. A reference case and sensitivity analysis are performed to demonstrate the efficiency of the management system and the influence of relevant parameters. According to the results, the proposed flexibility management system reduces the total energy cost by 45% and 27.8% using quadratic and linear cost function approaches, respectively, with an average calculation time of 236 s per 900 s of execution.

The Operation Strategy of the MIDC Systems for Optimizing Renewable Energy Integration of Jeju Power System

The power system on Jeju Island currently faces challenges due to an imbalance between power supply and demand owing to increasing renewable energy (RE) generation. Consequently, the multi-infeed high-voltage direct-current (MIDC) systems are expected to play an important role in reducing RE curtailment and increasing RE penetration. However, despite the balance between power supply and demand, transmission line (TL) overloads on Jeju Island, which has a low permissible rating, may lead to RE curtailment. We propose an operational strategy for the MIDC systems to mitigate TL overloading and RE curtailment. This strategy was aimed at managing the possible congestion of TLs, maintaining a power supply–demand balance, and increasing the penetration level of RE. In this paper, we present a scheme for calculating the operating points of the MIDC systems and allocating RE curtailment using optimization with security constraints to mitigate TL overloading. The proposed optimization model simplifies solving the mathematical equations involved by employing a sensitivity factor called the power transfer distribution factor (PTDF), using which linear programming (LP) optimization can be performed. To verify the feasibility of the operational strategy, case studies are conducted using an actual Jeju Island power system with a power system analysis tool—Power System Simulator for Engineering (PSS/E). The optimization problem was solved using MATLAB.

Analysis of overvoltage reactive power margin for future power systems with renewable energy generation

As modern power systems continue to adopt wide usage of Renewable Energy (RE) sources, the reliance on conventional generators is slowly reducing. Countries are adopting new energy policies to speed up the transition of future power systems to a fully renewable and clean system, and this is centered around the increase of RE penetration to reduce carbon emissions. However, it is expected that this transition will be met with stability issues due to the intermittent nature of REs and reduction of conventional generators. In recognition of these expected complications for the future systems, an analysis method is proposed in this paper to identify critical buses in terms of voltage stability for power systems with high RE penetration level. A new method that utilizes both modified continuation power flow and Q-V analysis to determine acceptable reactive power margins for each bus considering the intermittency of RE sources is presented in this study. This work is focused on the future Korea Power Systems with 16.05 GW of renewable energy. Considering different RE levels and loading conditions, several scenarios are studied to identify the voltage stability in the different Korea regions with a high penetration of RE. The resulting reactive power margins can be used to prevent overvoltage through the identification of weak and vulnerable buses for the future power systems.

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.

The overarching role of electric vehicles, power‑to‑hydrogen, and pumped hydro storage technologies in maximizing renewable energy integration and power generation in Sub-Saharan Africa

Globally, efforts are underway to keep global warming well below 1.5–2 °C as outlined in the Paris Agreement. Ghana plans to achieve 10 % renewable energy (RE) participation in the national energy mix by 2030. In the Renewable Energy Masterplan 2030, the target is a total installed RE utility scale of 1094.63 MW. In the current work, we propose a better pathway to gradual decarbonization of the Ghanaian energy sector (power, transport, industry, residential, and others) at higher technical levels of variable RE (VRE) penetration, 0 critical excess electricity production (CEEP), and lower cost than the 2030 Business-as-Usual (BAU) scenario. In our proposed scenario, High Renewable Energy Penetration (HREP) 2030, we assess the overarching role of electric vehicle integration, power-to-gas (hydrogen), and pumped hydro storage technologies in maximizing VRE penetration in Ghana. The results from the current study show that it is technically feasible to include solar and wind power penetrations of 32.66 % and 38.11 % in the 2030 HREP scenario compared to the 2.64 % and 1.99 % of the 2030 BAU case, respectively. The HREP scenario will ultimately lead to 26.45, 30.86, and 23.34 % contribution of solar, wind, and thermal in the total electricity production in 2030 compared to the 2.36, 1.78, and 74 % of the BAU scenario, respectively. The CO2 emissions and its cost in the HREP case are 22.5 and 22.81 % lower than in the BAU case, and the total annual cost of the former is 12 % lower than the latter. The total electricity production from wind and solar energy alone in the 2030 HREP will create an additional 723 jobs and 11.96 Mt avoided CO2 emissions. The proposal made in the current study could usher in a long-term transition pathway that leads from the current fossil-based system to an affordable, efficient, sustainable, and secure energy future for Ghana. The findings presented in the current study should be of significance to decision-makers in formulating and adopting zero and low-carbon strategies for long-term decarbonization targets and planning in Ghana beyond 2030.

Optimization Technique for Renewable Energy Storage Systems for Power Quality Analysis with Connected Grid

Power quality is the main problem with the power system network. Poor electricity quality may cause disruptions and financial challenges for consumers. Additionally, it could cause electronic gadgets to overheat, be damaged, or operate inadvertently. Transformers and other components of the power distribution system may also overheat and experience core saturation. This study investigates potential problems with the quality of the electricity in a photovoltaic linked power system. This paper suggests a new optimization method for day-ahead trading and control in DC microgrid power management (MG). The goal of the multiobjective optimization dispatch (MOOD) problem is to lower overall operational costs as well as the costs associated with power loss in efficient conservation systems and exhaust emission quantities such as nitrogen oxides, sulphur dioxide, and carbon dioxide. Using the weighted sum approach, the multiobjective optimization problem is reduced to a single optimization problem. The analytical hierarchy process (AHP) method is then used to calculate the weight coefficients while accounting for each objective function’s preferences. Power balancing, high levels of renewable energy penetration, the most effective scheduling of battery charging and discharging, control of load curtailment, and the technical limitations of the system are all taken into account when evaluating the system’s performance in both grid-connected and standalone operation modes. The ant lion optimizer (ALO) technique is taken into account to tackle MOOD, comparing the effectiveness of the proposed method to other well-known heuristic optimization techniques. The simulation’s results demonstrate how effectively the proposed strategy can address the coordinated control and optimization dispatch difficulties. They also found that operating the MG system economically in grid-connected mode can save overall costs by roughly 4.70% compared to doing it in independent mode.

Transmission impedance control impacts on carbon emissions and renewable energy curtailment

Increased levels of renewable energy penetration have created new congestion patterns in recent years. Since the grid is not designed for the new patterns, the operators may need to curtail renewable energy to maintain transmission flows within acceptable limits. Transmission line impedance control, using flexible ac transmission system (FACTS) devices, has been proposed as an approach to relieve congestion in transmission systems and enhance renewable energy utilization. In this paper, we conduct a comprehensive study to provide insights on FACTS implementation’s impact on renewable energy integration and carbon emission reduction. The study considers variations in renewable energy penetration level, system loading patterns, location of renewable generation, and location of FACTS devices. Furthermore, generation mix data from prominent regional transmission organizations (RTO) are used to achieve more realistic results. Simulations studies are carried out on a modified RTS-96 system with a two-stage stochastic unit commitment model. The results show that, even though impedance control is effective in cost reduction, it has limitations in facilitating renewable energy integration in systems with prominent cheap fossil fuel power plants.

Low carbon and efficiency oriented day-ahead joint electrical energy and ancillary services market clearing model for generation-side in China

China's market-oriented reform of electricity transaction and carbon neutrality target will bring new challenges to thermal power generators. Under the joint clearing mechanism of the energy and ancillary services markets, this paper proposed a bi-level model to characterize the individual-oriented behavior of the power generation company and the system-oriented collaborative decision-making behavior of generation companies. The bi-level optimization model is converted to a mathematical program with equilibrium constraints and further transformed into mixed-integer linear programming models which can be solved directly by using linearization techniques. We evaluated the impact of generation companies' behavior decisions in a regional power system under different levels of renewable energy penetration and deep-peaking compensation prices. Compared with the individual-oriented mechanism, a lower emission level, a lower average daily clearing price and higher unit efficiency would be achieved under system-oriented mechanism, even if there will be some loss of profit. The system-oriented decision-making behavior that will bring environmental benefits deserves more attempts. Therefore, for a power generation group with multiple generating units, the adoption of system-oriented decision is more conducive to realize the low-carbon transformation, which is an alternative way to promote the low-carbon transition of the power system in China.

Enhancing flexibility for climate change using seasonal energy storage (aquifer thermal energy storage) in distributed energy systems

Long-term energy storage is expected to play a vital role in the deep decarbonization of building energy sectors, while enhancing the flexibility of buildings to withstand future climate variations. However, it is challenging to design distributed multi-energy systems (DMES) while taking into account the uncertainties introduced by climate change, since stochastic optimization of such systems is difficult. The present study introduces a stochastic optimization model to address this bottleneck, taking into account DMES including aquifer thermal energy storage (ATES) as the long-term thermal storage. For the first time, a novel optimization algorithm links ATES with the DMES optimization model with the support of a simplified geotechnical model. Subsequently, a case study was conducted, focusing on a residential district in Chicago where the impact of future climate condition, energy demand, and solar and wind energy potentials were evaluated using Weather Research and Forecasting (WRF) data (up to 2080) and the EnergyPlus model. The study revealed that ATES is an attractive way to improve the renewable energy penetration level and minimize the dependence on fossil fuels with reasonable support from the grid to assist the fluctuations in both demand and generation. Furthermore, ATES notably reduces fuel consumption and dependence while greatly enhancing the flexibility of the energy system to withstand fluctuations in demand and renewable energy generation brought by future climate variations. These qualities will make ATES an important part of distributed energy systems, even though it is not currently the lowest cost alternative due to lack of technology maturity. The design platform introduced in the present study can be used to design DMES enhancing flexibility to accommodate future climate variations.

Characteristic Evaluation of Wind Power Distributed Generation Sizing in Distribution System

Energy consumption and environmental issues have become major drivers of increasing renewable energy penetration levels. The electricity generated from renewable energy sources is decentralized throughout distributed generation (DG), which is located at the distribution level. However, the presence of DG can change distribution system characteristics and affect protection systems. Thus, this study aims to investigate the impact of DG in term of its sizing and placement on distribution systems under both normal and fault conditions. In addition, the effects on voltage improvement under normal conditions and current under fault conditions are also considered. The case study system in this study was modelled after an actual section of a 22 kV distribution line from the Provincial Electricity Authority of Thailand using PSCAD software. For DG, wind turbine generation was selected as a renewable energy source. The simulation results demonstrated that the presence of DG has a significant impact on both voltage and current characteristics under both normal and fault conditions. These impacts on the distribution system caused by DG can affect the operation of conventional distribution systems, which require further analysis and preventive measures in order to ensure good system reliability.

Application of an Operating Reserve Demand Curve in ERCOT in the South Korean Electricity Market to Accommodate High Penetration Levels of Renewable Energy

Application of an Operating Reserve Demand Curve in ERCOT in the South Korean Electricity Market to Accommodate High Penetration Levels of Renewable Energy

A Review of Grid Connection Requirements for Photovoltaic Power Plants

The increasing rate of renewable energy penetration in modern power grids has prompted updates to the regulations, standards, and grid codes requiring ancillary services provided by photovoltaic-generating units similar to those applied to conventional generating units. In this work, a comprehensive survey presents a comparison of requirements related to voltage ride through reactive current injection/absorption; active power restoration; frequency stability regulation and active power control; voltage regulation and reactive power control; and the energy quality requisites included in the standards and grid codes of countries around the globe. The survey can be used to observe the differences between the requirements established in the grid codes depending on the power system operating characteristics, development of technology, and renewable energy penetration level. Many of these factors determine the parameters used to establish requisites for different grid codes, making a global standardization of the renewable energy interconnection requirements much harder.

Relaxation‐based bi‐lever reformulation and decomposition algorithm‐based collaborative optimization of multi‐microgrid for cloud energy storage

Energy storage devices become an indispensable part of modern power systems with high renewable energy penetration level. To reduce the operating costs, it is a promising way to allow the sharing and leasing of energy storage devices. In this paper, a bi-lever optimized dispatch scheme is proposed to improve the usage efficiency of cloud energy storage in multi microgrids (MMG) system. Minimizing the operating costs of shared cloud energy storage is the main task of the upper lever while maximizing the profits of MMG is the goal of the lower lever. Moreover, the transaction cost and benefit between the two levers play an important role in system level optimization. This leads to a hybrid optimization problem with both discrete decision variables and continuous decision variables. To solve the problem, a relaxation-based bi-lever reformulation and decomposition algorithm is developed. The effectiveness of the proposed bi-lever dispatch optimization model is verified by carrying out numerical experiments in three scenarios. It is shown that the proposed cloud energy storage service can effectively reduce the operating cost of MMG.

Optimal design of microgrids to improve wildfire resilience for vulnerable communities at the wildland-urban interface

Climate change leads to extreme climate events that result in frequent wildfires that cause numerous adverse societal impacts. Public Safety Power Shutoffs, adopted by utilities to minimize the risk of wildfires, pose many challenges to electricity consumers. Microgrids, have been proposed to improve the resilience of energy infrastructure during wildfire events for vulnerable communities. However, a comprehensive techno-economic and environmental assessment of the potential of such energy systems have not been performed. To address this research gap, the present study introduces a modeling framework, consisting of (1) clustering algorithms that identify the communities based on building footprint data, fire hazard severity, and renewable energy potential; (2) a building simulation model to quantify the energy demand; and (3) an energy system optimization model to assist the Microgrid design. A novel optimization tool was introduced to model Microgrids in wildland-urban interface, and subsequently, a comprehensive assessment was performed, focusing on seven localities from California, United States, with different climatic conditions. The study reveals that Microgrids can keep the average levelized energy cost and annual Public Safety Power Shutoffs below $0.3/kilowatt-hour (kWh) and 2%–3% (of the annual energy demand), respectively. Furthermore, renewable energy penetration levels can be maintained above 60% of the annual energy demand. Therefore, Microgrid may become an attractive solution to reduce the adverse impacts of wildfires and enhance the resilience of energy infrastructure. However, the study reveals that Microgrid cannot completely eliminate the Public Safety Power Shutoffs. The levelized cost and renewable energy generation curtailments (waste of renewable energy) become notably high when attempting to eliminate Public Safety Power Shutoffs completely. A notable reduction in energy storage cost is essential to achieve zero Public Safety Power Shutoffs, and this is expected with the evolution of energy storage technologies. The present study recommends Microgrids for communities affected by wildfires to enhance the resilience of energy infrastructure and protect the health and safety of residents. The modeling framework and optimization tool developed in this study can be used by stakeholders and their consultants to inform design and optimization of Microgrids for investment decision making.