Renewable Power System Research Articles

Techno-economic modeling and optimal sizing of autonomous hybrid microgrid renewable energy system for rural electrification sustainability using HOMER and grasshopper optimization algorithm

This research paper focuses on techno-economic modeling and optimal sizing of autonomous hybrid microgrid systems. The optimal configuration of the suggested stand-alone system was performed by developing a size optimization model based on the metaheuristic novel Grasshopper Optimization algorithm (GOA) method to minimize the Total Net Present Cost (TNPC), unmet load, and Cost of Energy (COE) in the Nsukka Community which comprises 88 villages. The GOA and HOMER Pro Software are employed to compare results across four possible configurations of hybrid renewable power systems (HRES). The comparative analysis between GOA and HOMER shows that configuration-4 (biogas/Diesel), emerges as the optimal solution, with a 0 % unmet load at the COE of $0.01783 per kWh. The findings indicated that the GOA-based HRES, with a higher saturation of Biogas and photovoltaics (PV), proves to be more affordable in comparison to the HOMER-based solutions. The reduction in the COE and NPC of renewable energy for peak demands highlights the growing importance of biogas generators as an affordable local power supply to meet energy demands. This underscores the potential of the GOA in optimizing hybrid renewable energy systems for remote communities, producing an economically viable and sustainable energy solution.

Impact of demand growth on the capacity of long-duration energy storage under deep decarbonization

Abstract The weather-dependent uncertainty of wind and solar power generation presents a challenge to the balancing of power generation and demand in highly renewable electricity systems. Battery energy storage can provide flexibility to firm up the variability of renewables and to respond to the increased load demand under decarbonization scenarios. This paper explores how the battery energy storage capacity requirement for compressed-air energy storage (CAES) will grow as the load demand increases. Here we used an idealized lowest-cost optimization model to study the response of highly renewable electricity systems to the increasing load demand of California under deep decarbonization. Results show that providing bulk CAES to the zero-emission power system offers substantial benefits, but it cannot fully compensate for the 100% variability of highly renewable power systems. The capacity requirement of CAES increases by ≤33.3% with a 1.5 times increase in the load demand and by ≤50% with a two-times increase in the load demand. In this analysis, a zero-emission electricity system operating at current costs becomes more cost-effective when there is firm power generation. The least competitive nuclear option plays this role and reduces system costs by 16.4%, curtails the annual main node by 36.8%, and decreases the CAES capacity requirements by ≤80.7% in the case of a double-load demand. While CAES has potential in addressing renewable variability, its widespread deployment is constrained by geographical, societal, and economic factors. Therefore, if California is aiming for an energy system that is reliant on wind and solar power, then an additional dispatchable power source other than CAES or similar load flexibility is necessary. To fully harness the benefits of bulk CAES, the development and implementation of cost-effective approaches are crucial in significantly reducing system costs.

Improved typical scenario-based distributionally robust co-dispatch of energy and reserve for renewable power systems considering the demand response of fused magnesium load

In recent years, the penetration of solar and wind power has rapidly increased to construct renewable energy-dominated power systems (RPSs). On this basis, the forecasting errors of renewable generation power have negative effects on the operation of the power system. However, traditional scheduling methods are overly dependent on the generation-side dispatchable resources and lack uncertainty modeling strategies, so they are inadequate to tackle this problem. In this case, it is necessary to enhance the flexibility of the RPS by both mining the load-side dispatchable resources and improving the decision-making model under uncertainty during the energy and reserve co-dispatch. In this paper, due to the great potential in facilitating the RPS regulation, the demand response (DR) model of fused magnesium load (FML) is first established to enable the deeper interaction between the load side and the whole RPS. Then, based on the principal component analysis and clustering algorithm, an improved typical scenario set generation method is proposed to obtain a much less conservative model of the spatiotemporally correlated uncertainty. On this basis, a two-stage distributionally robust optimization model of the energy and reserve co-dispatch is developed for the RPS considering the DR of FML. Finally, the proposed method is validated by numerical tests. The results show that the costs of day-ahead dispatch and re-dispatch are significantly decreased by using the improved typical scenario set and considering the DR of FML in regulation, which enhances the operation economy while maintaining the high reliability and safety of the RPS.

Benchmarking and contribution analysis of carbon emission reduction for renewable power systems considering multi-factor coupling

The renewable power system serves as a vital path towards achieving carbon peaking and neutrality goals. Accurately quantifying the carbon reduction emissions of renewable power systems and evaluating the contribution of various influencing factors to carbon reduction is quite important, which is related to the formulation of energy policies and regional power planning by the power grid or relevant departments. However, in the current research, there are few studies related to the quantification of carbon emission reduction, and in terms of optimal scheduling, the existing studies often only consider a single factor, ignoring the coupling effect between multiple factors, which makes the contribution of each optimization strategy to carbon emission reduction unclear. Therefore, based on the current power system scheduling mechanism and dual-carbon background, this paper optimizes the carbon emission reduction benchmark factor and its corresponding weight, proposes a new carbon emission reduction benchmark of renewable power system, and verifies the accuracy of this benchmark based on the classical scheduling model. On this basis, the mathematical models of thermal power deep peak shaving (DPS), electricity export (ELE) and green certificate trading (GCT) are established. Each factor is introduced into the classical scheduling model to simulate and verify the promoting effect of each influencing factor on carbon emission reduction. Finally, the above different influencing factors were constructed in different coupling scenarios to calculate the carbon emission reduction under each scenario, and the contribution degree of each influencing factor to carbon emission reduction was analyzed sharply according to the value of cooperation game instead of the traditional calculation method. The results show that the proposed benchmark can reduce the error by 2.6%–15.5 %. In the carbon emission reduction contribution simulation, the three factors have coupling effects, and deep peak shaving and electricity export are more sensitive to carbon emission reduction contribution. This study provides valuable insights for the development of carbon reduction strategies for renewable power systems.

A grid-connected eleven-level power conversion interface

ABSTRACT This paper proposes a grid-connected eleven-level power conversion interface (GCELPCI) for renewable power systems. The proposed GCELPCI is composed of a four-port DC-DC power converter (FPDDPC) and an eleven-level inverter (ELI). The FPDDPC combines a boost power converter and a transformer with a turn ratio of 2:1 to generate multiple combinations of the output voltages for the DC bus voltage of the ELI. The ELI integrates an asymmetric voltage technique on a T-type inverter to further convert the DC output voltages of FPDDPC into eleven-level AC voltage. A sinusoidal current is generated by the proposed GCELPCI through the filter inductor and injected into the power grid. Only nine power electronic switches are used in the ELI to generate an eleven-level AC voltage, the number of power electronic switches is reduced to simplify the power circuit. In addition, the capacity of the filter inductor is reduced. A digital signal processor (DSP) and a complex programmable logic device (CPLD) chip are used as the digital controller to complete a hardware prototype to verify the performance of the proposed GCELI.

Review on development prospect of operation scheduling strategies for flexible interconnected distribution network in renewable energy-penetrated power system

As the renewable energy integrates in the power system, the interactions between source, load, and storage equipment in the distribution network become frequent. The original distribution network could not meet the growing demand of the power grid. The use of controlled power electronic devices for flexible interconnection transformation of the distribution network could help improve the controllability, reliability, and safety of the power system and promote the consumption of distributed power sources. It is an important means of evolving towards future intelligent distribution networks. Typical devices for flexible interconnection are discussed of the operating characteristics. In addition, the core technologies of flexible interconnected distribution network system are introduced from the view of point of dispatching. Finally, to achieve a wider application of flexible interconnected distribution networks, the development trend is prospected.

Analysis of energy management in a hybrid renewable power system using MOA technique

Analysis of energy management in a hybrid renewable power system using MOA technique

A Hybrid Wild Horses Optimization (WHO) and Dwarf Mongoose Optimization (DMO) method for optimum energy management in SG system

AbstractA hybrid technique is proposed for the energy management (EM) of smart grid (SG) systems. The proposed method integrates the Wild Horse Optimization (WHO) and dwarf mongoose optimization (DMO) methods; hence, it is named the WHO–DMO approach. The Micro‐grid (MG)‐tied system is a combination of battery, micro turbine (MT), photovoltaic (PV), and Wind Turbine (WT). The key aim of the proposed approach is to manage the resources and power of the SG model and reduce the cost of electricity. The objective of the system is to improve load demand. The WHO method is enhanced by the DMO method, which minimizes the objective of the system. Access to power demand, state of charge (SOC), and renewable energy sources (RESs) for storing elements are considered constraints of the system. The unit of renewable power systems relies on batteries as energy sources to stabilize and sustain stable and consistent output power throughout the operation. The proposed technique is done in MATLAB platform and its implementation is calculated using the existing methods. From the simulation, the proposed method has less cost and higher power than the existing methods.

Hydrothermal–pumped-storage scheduling considering renewable energy uncertainty by improved cheetah optimization

The growth of renewable wind and solar energy in modern power systems affects the short-term scheduling of hydrothermal power generation. This article presents the optimum day-ahead scheduling of wind–solar–hydrothermal systems with pumped-storage plants (PSPs). To assess the contribution of PSPs in decreasing the generation costs of a renewable integrated power system, a scheduling model is formulated that considers different security constraints. An improved cheetah optimizer (ICO) is implemented on a renewable integrated test power system to solve the short-term optimum wind–solar–pumped-storage plant–hydrothermal scheduling (OWSPHTS) problem, taking into account all thermal, hydraulic and network constraints. Optimum solutions are obtained with different objectives considering renewable uncertainties. The proposed ICO solution method is compared with the similar algorithms in terms of optimal fuel costs, emissions, convergence success rate and computation time. The comparative solutions show that the introduced optimization technique is efficient for solving the real-world OWSPHTS problem.

Sub-synchronous oscillation suppression strategy for virtual synchronous generators based on dual-loop sliding mode control

The virtual synchronous generator (VSG) has synchronous voltage source characteristics that can effectively improve the stability of high-penetration renewable energy power systems. However, the output impedance of VSG using traditional voltage–current inner-loop control has lower damping in the sub-synchronous frequency range. Therefore, when the VSG is connected to a grid with series compensation for long-distance transmission of renewable energy, in the sub-synchronous frequency range, there is a potential interaction between the inductive output impedance of VSG and the capacitive impedance of the line, leading to the occurrence of sub-synchronous resonance (SSR). To address this challenge, the stability of a series-compensated grid-connected system employing voltage–current inner-loop control was analyzed using VSG sequence impedance modeling. A dual-loop sliding mode control strategy (SMC-DL) was proposed to suppress SSR occurrence. This control strategy can significantly enhance the damping of VSG’s output impedance in the sub-synchronous frequency range, and effectively suppress SSR. Compared to traditional control strategies, this control strategy does not require the addition of virtual impedance, avoids voltage offset issues, adapts to grids with different series compensation levels. Finally, the effectiveness of this approach was verified through simulations and experiments.

Using power system modelling outputs to identify weather-induced extreme events in highly renewable systems

In highly renewable power systems the increased weather dependence can result in new resilience challenges, such as renewable energy droughts, or a lack of sufficient renewable generation at times of high demand. The weather conditions responsible for these challenges have been well-studied in the literature. However, in reality multi-day resilience challenges are triggered by complex interactions between high demand, low renewable availability, electricity transmission constraints and storage dynamics. We show these challenges cannot be rigorously understood from an exclusively power systems, or meteorological, perspective. We propose a new method that uses electricity shadow prices—obtained by a European power system model based on 40 years of reanalysis data—to identify the most difficult periods driving system investments. Such difficult periods are driven by large-scale weather conditions such as low wind and cold temperature periods of various lengths associated with stationary high pressure over Europe. However, purely meteorological approaches fail to identify which events lead to the largest system stress over the multi-decadal study period due to the influence of subtle transmission bottlenecks and storage issues across multiple regions. These extreme events also do not relate strongly to traditional weather patterns (such as Euro-Atlantic weather regimes or the North Atlantic Oscillation index). We therefore compile a new set of weather patterns to define energy system stress events which include the impacts of electricity storage and large-scale interconnection. Without interdisciplinary studies combining state-of-the-art energy meteorology and modelling, further strive for adequate renewable power systems will be hampered.

Interruption characteristics of self-powered hybrid DC circuit breaker with synergistic effect of step-down and divided current

The DC voltage level of photovoltaic and PEDF applications have continuously advanced, reaching up to DC3000V. This presents new requirements for DC circuit breakers operating at higher voltages (>DC1500V) to ensure effective fault protection in the DC side. Based on this, a self-powered hybrid DC circuit breaker with synergistic effect of step-down and divided current is proposed. This innovative design utilizes the voltage of main branch to trigger IGBT conduction, eliminating the need for an additional trigger circuit. The interruption characteristics is analyzed by simulation. The reactor and shunt resistance effectively reduce the peak currents of both main and transfer branches. This approach significantly mitigates the voltage stress on the mechanical switch, reduces the risk of post-arc reignition, and shortens the interruption time. The circuit breaker topology proposed enables the DC arc interruption at larger current and higher voltage, serving as the valuable guide for the implementation of hybrid DC circuit breakers in renewable energy power systems.

Load frequency stabilization of distinct hybrid conventional and renewable power systems incorporated with electrical vehicles and capacitive energy storage

Maintaining a power balance between generation and demand is generally acknowledged as being essential to maintaining a system frequency within reasonable bounds. This is especially important for linked renewable-based hybrid power systems (HPS), where disruptions are more likely to occur. This paper suggests a prominent modified “Fractional order-proportional-integral with double derivative (FOPIDD2) controller” as an innovative HPS controller in order to navigate these obstacles. The recommended control approach has been validated in power systems including wind, reheat thermal, solar, and hydro generating, as well as capacitive energy storage and electric vehicle. The improved controller’s performance is evaluated by comparing it to regular FOPID, PID, and PIDD2 controllers. Furthermore, the gains of the newly structured FOPIDD2 controller are optimized using a newly intended algorithm terms as squid game optimizer (SGO). The controller’s performance is compared to benchmarks such as the grey wolf optimizer (GWO) and jellyfish search optimization. By comparing performance characteristics such as maximum frequency undershoot/overshoot, and steadying time, the SGO-FOPIDD2 controller outperforms the other techniques. The suggested SGO optimized FOPIDD2 controller was analyzed and validated for its ability to withstand the influence of power system parameter uncertainties under various loading scenarios and situations. Without any complicated design, the results show that the new controller can work steadily and regulate frequency with an appropriate controller coefficient.

Adaptive solar power generation forecasting using enhanced neural network with weather modulation

Solar power generation forecasting plays a vital role in optimizing grid management and stability, particularly in renewable energy-integrated power systems. This research paper presents a comprehensive study on solar power generation forecasting, evaluating traditional and advanced machine learning methods, including ARIMA, Exponential Smoothing, Support Vector Regression, Random Forest, Gradient Boosting, and Physics-based Models. Moreover, we propose an innovative Enhanced Artificial Neural Network (ANN) model, which incorporates Weather Modulation and Leveraging Prior Forecasts to enhance prediction accuracy. The proposed model is evaluated using real-world solar power generation data, and the results demonstrate its superior performance compared to traditional methods and other machine learning approaches. The Enhanced ANN model achieves an impressive Root Mean Square Error (RMSE) of 0.116 and a Mean Absolute Percentage Error (MAPE) of 36.26%. The integration of Weather Modulation allows the model to adapt to changing weather conditions, ensuring reliable forecasts even during adverse scenarios. Leveraging Prior Forecasts enables the model to capture short-term trends, reducing forecasting errors arising from abrupt weather changes. The proposed Enhanced ANN model showcases its potential as a promising tool for precise and reliable solar power generation forecasting, contributing to the efficient integration of solar energy into the power grid and advancing sustainable energy practices.

Quantifying the energy flexibility potential of a centralized air-conditioning system: A field test study of hub airports

Centralized air-conditioning systems are widely considered a major energy consumer with high energy flexibility, contributing to renewable penetration and power system regulation. Nevertheless, a lack of understanding of their system components' abilities limits the utilization of their full potential. Hereby, we took the centralized air-conditioning systems of two hub airports as typical examples and quantified their energy flexibility potential by cooling load reduction potential (qmax), cooling energy storage capacity (Q), based on field test. Cooling plants had the highest potential (qmax = 70–110 Wc/m2; Q = 500∼1500 Whc/m2), followed by terminal devices with building thermal mass (qmax = 2–11 Wc/m2; Q = 10–40 Whc/m2) and transmission & distribution networks (qmax = 50–200 Wc/m2; Q = 10–20 Whc/m2). Various air-conditioning terminal devices utilize building thermal mass to different degrees. During the investigation, the conventional all-air system used 7.8 %∼18.7 % of the building's thermal capacity, whereas that of the radiant floor system significantly increased to 38.9 %∼48.3 %. Consequently, the systems of the hub airports can participate in demand response programs with a cooling load reduction of 2∼15 MWe, for 6–20 h using different operating strategies. These findings shed light on demand-side flexibility characterization and exploitation to support a decarbonized energy system.

Multi-scenario flexibility assessment of power systems considering renewable energy output uncertainty

The widespread adoption of renewable energy sources presents a significant challenge to the flexibility of power system. To assess the flexibility of the power system in scenarios with uncertain renewable energy output, it is crucial to quantify it quantitatively. This evaluation plays a vital role in planning flexible regulatory resources and dispatching resources for both the energy source and load. This study introduces a novel flexibility assessment model tailored for power grids with high renewable energy penetration, specifically addressing uncertainty associated with wind and PV. By analyzing the impact of wind and PV uncertainty on system flexibility, the paper proposes an improved cohesive hierarchical cluster analysis method, incorporating reliability considerations based on the Davies-Bouldin classification reliability index. Additionally, the study develops models for flexibility resources and demands within high renewable energy power systems, along with quantitative assessment indicators across three key aspects. Through a structured flexibility assessment process accounting for wind and PV uncertainty, the effectiveness of the proposed approach is validated using real-world data from a renewable energy power grid in Shandong province. A set of typical renewable energy output scenarios with uncertainty is constructed using the improved hierarchical cluster analysis method. The study then analyses the impact of different wind and PV penetration rates and the proportion of energy storage units on system flexibility by the flexibility assessment model to validate the proposed method's effectiveness.

Can a new power system create more employment in China?

The transition from the coal-dominated power sector to the renewable energy-dominated new power system will inevitably result in significant social impacts. Taking China as the study case, we employed an integrated methodology combining the energy system optimization model, input‒output and structural path analysis, to explore the employment effects of the new power system, including the energy source, grid, load and storage. The results show that growth in solar power, wind power, energy storage and power transmission and distribution will have positive impacts on employment, while coal power shrinkage will result in negative impacts. Under new power system scenarios, renewable power can create additional 673–730 thousand jobs by 2030 and additional 2,431∼2,706 thousand jobs by 2060 compared with the business-as-usual scenario. However, jobs losses due to coal power shrinkage will reach 665–747 thousand jobs by 2030 and 3,127∼3,398 thousand jobs by 2060 compared with the business-as-usual scenario. In the meantime, power grid and energy storage can create additional 443–604 thousand jobs by 2030 and additional 2,138∼2,764 thousand jobs by 2060. Though the new power system will have positive employment impact in general, social justice should still be considered when there is an employment structure transition alongside the power structure shift.

A Novel Decentralized Frequency Regulation Method of Renewable Energy Stations Based on Minimum Reserve Capacity for Renewable Energy-Dominated Power Systems

In this paper, we propose a novel decentralized frequency regulation method for renewable energy-dominated power systems. First, the system is modularized into unified frequency regulation modules composed of synchronous generators(SGs) and renewable energy stations. Then, based on the aggregation model of the module, a renewable energy station frequency regulation method based on robust control barrier functions(RCBFs) is proposed to ensure that the frequency deviation is limited, and an additional switching control strategy is complemented to avoid the long-term suspension of over-high quasi-steady-state frequency. The proposed method is still applicable under the coordination of multiple renewable energy stations. Each renewable energy station deploys decentralized control based on local information, which can support fast frequency response. The robustness of frequency response time constants of renewable energy stations and frequency dead zone are considered, which makes the control method easy to implement. A module transient frequency stability margin index based on reserve capacity is proposed, which can be used to guide the system planning and operation. Finally, simulation results validate the applicability of the proposed method, demonstrating its efficacy in regulating frequency in renewable energy-dominated power systems.

Low-Frequency and Sub-Synchronous Power Oscillation Suppression and Analysis of Hybrid Line Commutated Converter for HVDC Grid

Building high-proportion renewable energy grid requires economical and reliable High Voltage Direct Current (HVDC) converters. Line Commutated Converter (LCC) is widely used because of its long transmission distance, low power loss, low manufacturing cost, and large transmission capacity, which modular multilevel converter (MMC) cannot achieve, but has commutation failure (CF) problems. Low-frequency and sub-synchronous power oscillation caused by CF will affect the safety of bulk power systems. This paper theoretically analyzes the power oscillation during CF and CF recovery, and then proposes a novel hybrid line commutated converter (H-LCC) based on integrated gate commutated thyristor (IGCT), which can effectively mitigate CF and suppress power oscillation. The principle of H-LCC suppressing power oscillation is analyzed. On this basis, the performance of LCC, MMC and H-LCC under the same fault conditions is compared by a simulation study, which proves that H-LCC can improve the security and stability of high-proportion renewable energy power systems.

Review of the marine energy environment-a combination of traditional, bibliometric and PESTEL analysis

Marine renewable energy is regarded as a nascent renewable energy resource that is less utilized due to a number of challenges in the sector. This paper focused on using both traditional and bibliometric analysis approaches to review the marine energy industry. It also assessed the various opportunities and challenges in the sector beyond technological challenges using PESTEL analysis. The results from the study identified the availability of renewable energy targets, international and national greenhouse gas (GHG) emissions reduction targets, job creation, skill transfer from offshore industries, renewable support, and low GHG emissions as the major opportunities for the sector. The challenges in the sector include the lack of commonality in device designs, high initial capital costs, lack of appropriate legal and regulatory frameworks, lack of funding, fragmentations in regulatory institutions, bad macro-economic indicators in some countries, environmental challenges, the survivability of the various technologies in the harsh oceanic environment, and strong competition from other renewable energy sources. The outcome of the bibliometric analysis spanning from 2013 to 2023 shows that tidal power is the focus of research in the field, and most studies are either focused on ways to improve its efficiency in terms of technology or on the identification of resource potentials for the siting of the various marine renewable power systems. Recommendations such as strong cooperation between the government and private sector, increased public education, collaboration with existing players in the marine sector, and increased research and development, among others, were proposed for the development of the sector.