Renewable Energy Power System Research Articles

Employing battery energy storage systems for flexible ramping products in a fully renewable energy power grid: A market mechanism and strategy analysis through multi-Agent Markov games

In high-proportion renewable energy power systems, flexible ramping products (FRPs) are critical for mitigating the volatility of renewable energy outputs and enhancing the adaptability of power systems. This paper aims to explore a fully renewable energy power system, with a battery energy storage system (BESS) as the sole provider of FRPs. An innovative market mechanism and new approaches to market equilibrium analysis are introduced. Initially, a common day-ahead (DA) market is established, covering both electricity and ancillary services with FRPs. The setup allows FRP providers to make bidding decisions and get rewarded based on actual ramping mileages. A bi-level multi-agent Markov game (MG) model is formulated. The upper level integrates the proposed strategies of wind power plants (WPP), photovoltaic power plants (PVP), and BESS, while the lower level encompasses the joint market clearing mechanism. An iterative solution algorithm, using reinforcement learning, is also introduced to address the model, effectively circumventing the privacy concerns associated with traditional mathematical programming methods. The proposed solution models and strategies are validated and demonstrated to be effective through two illustrative examples, highlighting their application and relevance in fully renewable energy power systems.

Active Power Dispatch of Renewable Energy Power Systems Considering Multiple Renewable Energy Station Short-Circuit Ratio Constraints

Active Power Dispatch of Renewable Energy Power Systems Considering Multiple Renewable Energy Station Short-Circuit Ratio Constraints

Comprehensive assessment and energetic-exergetic performance optimization of a new solar thermal electricity system based on Scheffler-type receivers combined with screw-type volumetric machines

This study explores the potential of a novel solar-powered cascade Rankine cycle system based on Scheffler-type receivers combined with screw expanders. Specifically, in this solar power system, steam is generated in the Scheffler-type receiver, which proves to be well performing compared with other technological solutions to exploit solar energy, due to satisfactory efficiency of the focal receiver that is able to curtail heat losses, even at high evaporation temperatures. Subsequently, steam expands in the screw machines which, unlike conventional steam turbines, are specially fitting in energy conversion with vapor-liquid mixes in the field from tens to hundreds of kW. In the present study, comprehensive assessment of this renewable energy power system is thoroughly conducted in a large range of operating states. For this purpose, specific numerical models and basic criteria fixed for the screw expanders and Scheffler receivers part-load behavior are combined with thermodynamic formulations established for energetic-exergetic performance optimization of the entire solar thermal electricity plant. Hence, parametric optimization of the major thermodynamic factors involved at part-load operating situations is conducted to enhance the energetic and exergetic efficiencies of the designed solar thermal power system.

Study on the Evolutionary Process and Balancing Mechanism of Net Load in Renewable Energy Power Systems

With the rapid development of renewable energy sources such as wind and solar, the net load characteristics of power systems have undergone fundamental changes. This paper defines quantitative analysis indicators for net load characteristics and examines how these characteristics evolve as the proportion of wind and solar energy increases. By identifying inflection points in the system’s adjustment capabilities, we categorize power systems into low, medium, and high renewable energy penetration. We then establish adjustment models that incorporate traditional coal power, hydropower, natural gas generation, adjustable loads, system interconnections, pumped-storage hydroelectricity, and new energy storage technologies. A genetic algorithm is employed to optimize and balance the net load curves under varying renewable energy proportions, analyzing the mechanism behind net load balance. A case study, based on real operational data from 2023 for a provincial power grid in western China, which is rich in renewable resources, conducts a quantitative analysis of the system’s adjustment capability inflection point and net load balancing strategies. The results demonstrate that the proposed method effectively captures the evolution of the system’s net load and reveals the mechanisms of net load balancing under different renewable energy penetration levels.

A voltage-decoupled Zn-Br2 flow battery for large-scale energy storage

The flow battery represents a highly promising energy storage technology for the large-scale utilization of environmentally friendly renewable energy sources. However, the increasing discharge power of rechargeable battery results in a higher charge voltage due to its coupling relationship in charge-discharge processes, intensifying the burden of renewable energy power systems. Herein, we proposed a voltage-decoupled Na+-conducting Zn-Br2 flow battery (Ud-Na-ZBFB). Within a pH-regulation strategy, both neutral Zn/Zn2+ and alkaline Zn/Zn(OH)42− negative redox couples are integrated into one device, so as to increase discharge voltage a 0.5 V theoretical increase while remains a low charge voltage. The proof-of-concept Ud-Na-ZBFB demonstrates an unprecedented voltage characteristic, achieving a discharge voltage of up to 2.18 V while maintaining a remarkably low charge voltage of 1.78 V at a current density of 20 mA cm−2. Benefiting from the high discharge voltage, the peak power density of Ud-Na-ZBFB reaches 580 mW cm−2, nearly twice higher than the traditional ZBFB. Besides, the Ud-Na-ZBFB cycling operates for 45 h with excellent stability and resilience. With the “cycle-to-stage” operational mode, battery state of Ud-Na-ZBFB can be completely recovered after a long-term operation stage with several cycles, which electrolyte utilization efficiency of one stage is up to 41 %. This work offers a brand-new utilization pattern for high-power rechargeable battery that possesses a great potential in energy storage application scenes.

Challenges of New Energy Power Systems

At present, in the context of global energy transition, China is actively promoting the construction of a high-proportion renewable energy power system, but it faces multiple challenges. First of all, the increasing demand for new energy installed capacity and the challenge of power balancing ability make the system operation face complexity and uncertainty. Secondly, the reshaping of market mechanisms and business models, as well as complex challenges at the technical level, are also constraints. The key technologies involved include innovation and application in simulation analysis and mechanism cognition, system construction, energy frame and stability control.

Comparative analysis and optimal allocation of virtual inertia from grid‐forming and grid‐following controlled ESSs

AbstractA broad consensus of neutralizing the carbon dioxide emissions facilitates the transition to the renewable energy power system. Meanwhile, the concerns about the volatility of renewable energies are growing as the rotational inertia of power system becomes inadequate. To maintain the frequency stability of power system, some studies for configuring inertia energy storage systems (ESSs) are carried out, mainly focusing on the allocation of virtual inertia from grid‐forming ESS. In contrast, the allocation of virtual inertia from grid‐following ESS has not been well elaborated and the differences in virtual inertia provided by these two modes are yet to be revealed. Based on ‐norm and Kron reduction, firstly, the state‐space model of post‐disturbance system is established, together with the transient performance evaluation. Then the inertia characteristics of both grid‐forming and grid‐following devices are formulated, followed by the unified gradient descent optimization method for allocating virtual inertia. A modified IEEE 39‐bus system and its time‐domain simulations help in the verification of the contribution of this paper. Through the comparative analysis of corresponding optimal results, the conclusions from two aspects are drawn: in terms of transient frequency support, the grid‐forming devices can provide no less than better inertia support; with the higher power capacity and similar energy capacity, the grid‐forming devices can relieve the response pressure of other generators by approximately .

Performance analysis of IoT-enabled hydro-photovoltaic power systems considering electrical power mission chains

Synergistically and complementarily managing the operation of hydropower plants and other weather-dependent renewable energy generation plants (e.g., solar energy plants) provides an effective measure to improve the performance efficiency (PE) of renewable energy. However, environmental uncertainty and physical system unavailability pose challenges in assessing the PE of the power generation. This paper proposes an approach to synergistically and complementarily managing the operation of hydro-photovoltaic (HPV) power systems in IoT under uncertain environments and develops a mission chain-based PE model for quantifying the capacity of the fulfilment of a power supply mission to satisfy users’ demand for power. An importance measure-based restoration strategy is then proposed to enhance the PE of an HPV power system (PEPS). The approach is examined by taking a large-scale HPV in China in the case study. The results show that, in winters and in summers, the PEPS is higher than PE of a photovoltaic power system and a hydropower system, respectively, and the PEPS with the importance measure-based restoration strategy is higher than that under the random restoration strategy, respectively. The findings not only lay the foundation for the complementary operation of hybrid renewable energy power systems but also provide a reference for the restoration in case of power cuts.

Stability analysis of grid-connected inverter under full operating conditions based on small-signal stability region

Impedance analysis is a practical approach for assessing the small-signal stability of renewable energy power systems. However, existing research predominantly focuses on specific operating conditions, neglecting the fundamental principles governing stability evolution under time-varying operating conditions. This paper presents a methodology to develop the small-signal stability region (SSSR) for grid-connected inverters using the impedance method. A comprehensive stability analysis for grid-connected inverter systems is performed based on the stability region. Firstly, the multi-parameter SSSR of the grid-connected inverter is defined according to both the aggregated impedance criterion and the generalized Nyquist criterion. Furthermore, a polynomial approximation expression for the SSSR boundary is derived. Secondly, the sensitivity analysis of operating points and control parameters is performed under full operating conditions to investigate their impact on stability based on the quantified boundary. The analyses reveal that the stability of the grid-connected inverter system near the SSSR boundary decreases with increasing active power and decreasing reactive power but exhibits an initial increase followed by a decrease with a larger PLL bandwidth. Finally, the accuracy of the stability region and the influence of key parameters are verified through case studies and experiments. The study in this paper can be used for quantitative analysis of stability margins and decision guidance of control optimization for grid-connected inverters.

Scenario-Driven Optimization Strategy for Energy Storage Configuration in High-Proportion Renewable Energy Power Systems

Scenario-Driven Optimization Strategy for Energy Storage Configuration in High-Proportion Renewable Energy Power Systems

Improving Distributed Renewable Energy Power Planning Through Particle Swarm Algorithm

The ongoing energy structure reform in our country has led to the emergence of distributed renewable energy as a primary source of energy development and utilization, primarily due to its utilization of local resources. However, challenges such as undefined objectives and ineffective planning have impeded its progress. This study specifically investigates distributed renewable energy power planning by enhancing a particle swarm algorithm with a strategy for updating local optimal solutions. The refined algorithm tackles issues related to renewable energy variability and economic efficiency, thereby optimizing the planning of distributed renewable energy power systems. The outcomes illustrate improvements in system operation, economic viability, and environmental sustainability. This research contributes to the progression of particle swarm algorithms for the planning of distributed renewable energy power systems.

Review and prospect on the construction method of security region for integrated natural gas and power systems

Natural gas and power systems are both important components of modern energy systems. As an efficient and clean peak-shaving power source, natural gas power generation is crucial for ensuring the safe operation of a high-penetration renewable energy power system —supporting China's strategic goal of "carbon peaking and carbon neutrality." However, in recent years, there have been frequent incidents of cascading faults between the gas and power grids. Studying the security region of the integrated electricity-gas system (IEGS) and identifying the system's security margin online is the key to actively preventing such accidents. In view of this, this paper first introduced the typical coupling forms of electricity-gas networks and summarizes the security analysis and evaluation methods from the perspectives of power systems and natural gas systems, respectively. Then, the basic model of the IEGS security region considering the interconnection of gas and electricity networks was extracted, and various methods for solving IEGS security boundaries and dealing with uncertain disturbance factors were compared. Finally, the possible development directions of the construction method of the security region for IEGS were proposed.

Research on the application of renewable energy in power system based on adaptive hierarchical fuzzy logic maintenance

Condition-based maintenance is very desirable for minimizing the maintenance and failure costs of power systems without sacrificing reliability. A systematic approach including an adaptive maintenance advisor and a system maintenance optimizer is proposed here for effectively handling the operational variations and uncertainties for condition-based maintenance. First, the maintenance advisor receives and implements the maintenance plans for its key components from the system maintenance optimizer, which optimizes the maintenance schedules with multi-objective evolutionary algorithm, considering only major system variables and the overall system performance. During operation, the offshore substation will experience continuing ageing and shifts in control, weather and load factors, measurement and human judgment detected from the connected grid and all other equipments with uncertainties. Then, the advisor estimates the changes of reliability indices due to operational variations and uncertainties of its key components by hierarchical fuzzy logic and sends the changes back to the maintenance optimizer. The maintenance optimizer will upgrade the load-point reliability and report any drastic deterioration of reliability within each substation, which may lead to re-optimization of the substation's maintenance activities for meeting its desired reliability. The offshore substation connected to a medium-sized onshore grid will be studied here to demonstrate the ability of this proposed approach in dealing with uncertainties in the implementation of maintenance with significant reduction of computational complexity and rule base.

Collaborative optimization of renewable energy power systems integrating electrolytic aluminum load regulation and thermal power deep peak shaving

Addressing renewable energy (RE) curtailment in power systems necessitates a comprehensive strategy leveraging peak regulation resources from both the power and load sides. On the power side, deep peak shaving of thermal power plants can mitigate surplus electricity during periods of high RE production. On the load side, energy-intensive industrial loads, characterized by large capacity and rapid adjustability, can respond effectively to energy deficits when RE is low. To enhance the peak regulation capacity for optimal RE accommodation, this paper proposes a collaborative optimization method combining electrolytic aluminum load (EAL) regulation with thermal power deep peak shaving (DPS). The study is initiated by developing a sophisticated peak regulation model for the electrolytic aluminum load (EAL), considering production characteristics, cost features, and safety constraints. Subsequently, a collaborative optimization model is formulated, integrating EAL regulation with thermal power deep peak shaving (DPS), aiming to minimize societal peak regulation costs (PRC). Applying this model to a practical regional grid in Yunnan Province reveals substantial reductions in RE curtailment rates, effective minimization of total social PRC, and enhanced operational economics of thermal power DPR under varying wind power scenarios. Notably, the proposed approach requires only minor adjustments to the EAL, ensuring production safety is maintained. This research provides valuable insights for the seamless integration of EAL into grid peak regulation services.

Universal SSO Stabilization of Renewable Energy Power Systems

Universal SSO Stabilization of Renewable Energy Power Systems

A hybrid approach based economic assessment EV charging station powered by integrating solar PV and biomass

In today's world, electric vehicles (EVs) are becoming increasingly popular. A significant power outage occurs in the system when EVs are charged from grid-connected charging stations. This manuscript proposes a hybrid approach for charging station electricity generation by integrating solar PV and biomass. The proposed hybrid technique is a combination of both the Dandelion Optimizer (DO) and Deep Attention Dilated Residual Convolutional Neural Network (DADRCNN). It is hence called DO-DADRCNN. The proposed approach DO is used to optimize the control parameter of the converter. And DADRCNN model used to predict the control parameter of the converter. This paper's main goal is the minimization of levelized cost of energy (LCOE), initial cost and net present cost (NPC). The most ecologically friendly system is the Photo Voltaic (PV)/battery hybrid since it emits no pollutants. The new uses for hybrid renewable energy power systems that combine solar photovoltaics and biogas, as well as an examination of the system's economic and environmental performance. The DAO-DADRCNN approach and the environmental economic evaluation of hybrid sustainable energy systems are intended to be investigated. Biogas and solar PV-based EV charging solutions provide techno economic and environmental feasibility. The proposed strategy is implemented in the MATLAB platform and the emission of carbon-di-oxide (CO2) emission is compared with various existing techniques like Wild horse optimizer, Salp Swarm Algorithm, and Particle Swarm Optimization. The proposed approach DO-DADRCNN obtains lower carbon dioxide (CO2) emission than the existing techniques. The CO2 emissions of the proposed technique is 5 kg/h which is lower and the cost for energy is 0.8$ which is lower than the existing methods.

Research on defense strategies for power system frequency stability under false data injection attacks

The rapid development of cyber-physical power system has highlighted the pressing issue of cyber attacks (CAs) faced by large-scale renewable energy power systems (LREPS). The injection of false data attacks on critical electrical equipment by attackers can lead to abnormal system frequencies, triggering cascade failures and causing widespread blackouts. This paper introduces a tri-level defense model specifically crafted for LREPS, comprising an upper, middle, and lower level (defender-attacker-operator). By promoting collaborative decision-making among these agents, the model aims to mitigate the rate of change of frequency (RoCoF) and effectively withstand CAs. The upper-level employs an economic-inertia optimization strategy, scheduling generator units to maximize system inertia while minimizing generation costs. By leveraging the strong duality theorem, the tri-level model is decomposed into a bi-level model consisting of a master problem and a subproblem, solved using the Benders decomposition method. Furthermore, this paper also considers augmenting the virtual inertia of renewable energy units when defense resources are insufficient, ensuring RoCoF remains within limits after CAs. The effectiveness and superiority of the proposed model and strategy are validated through case studies based on the improved IEEE 118-bus system. The results demonstrate that the model can reduce load shedding caused by the worst-case CAs, enhance system inertia, lower RoCoF, and improve the stability of LREPS.

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.

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.

Research on Grid-connected Voltage Improvement Technologies for High-penetration Photovoltaic Power Generation

The degradation in power quality due to the high penetration of grid-connected photovoltaic (PV) systems is a critical issue that urgently needs to be addressed in the field of renewable energy generation. Solar PV systems typically lack mechanical inertia, which means that they are unable to provide additional mechanical inertia to maintain voltage and frequency stability when the power system is subjected to external disturbances. Therefore, to ensure stable operation of the power system, alternative measures need to be relied on to maintain voltage stability. This article first analyzes the mechanism of voltage fluctuations when photovoltaic power generation is connected to the grid. It then delves into the challenges of power quality brought about by the grid integration of renewable distributed generation systems, as well as the current research status of mainstream voltage improvement technologies, customized power devices, and energy storage systems. Finally, the paper presents recommendations for improving grid voltage quality, enhancing the stability and reliability of the power system, and promoting the widespread application of renewable energy in power systems.