High Proportion Of Renewable Energy 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.

Hybrid modeling with data enhanced driven learning algorithm for smart generation control in multi-area integrated energy systems with high proportion renewable energy

Multi-area integrated energy systems (MA-IES) with a high proportion of renewable energy have become a trend in social development. The access to a large number of distributed energy sources makes the safe, stable, and economic operation of MA-IES suffer serious challenges. To reduce the frequency deviation and area control error (ACE) of MA-IES and to improve the economy of operation, this study proposed a hybrid modeling with data enhanced driven learning algorithm, which combines model-driven and data-driven algorithms, named combined proportion-integral-derivative and deep reinforcement learning (CPIDDRL). Firstly, the frequency deviation signals are collected and judged, and the smaller frequency deviation signals, which are sent to the model-driven part, are regulated by proportion-integral-derivative (PID) controllers. Then, the ACE and large frequency deviation signals are collected and transmitted to the data-driven part, and the Transformer deep learning network is applied to predict the multi-feature timeseries, which are applied to strategy selection for state-action-reward-state-action reinforcement learning. Finally, the model-driven part and data-driven part work together to generate adjustment commands every 4 s. The control effects of CPIDDRL, PID, Q-learning, and sliding model control algorithms are compared in two and four areas integrated energy systems. The results show that, compared to the comparison algorithms, the CPIDDRL reduces the mean values of frequency deviation and ACEs by at least 46.78% and 6.83%, respectively, and the total generation cost by at least 8.20%. CPIDDRL has practical significance in improving system stability, enhancing renewable energy integration capabilities, and promoting the development of smart grids.

Research on the Role of Bac Ai Pumped Storage Hydropower in the Operation of Vietnam's Power System in 2030 with a High Proportion of Renewable Energy

Research on the Role of Bac Ai Pumped Storage Hydropower in the Operation of Vietnam's Power System in 2030 with a High Proportion of Renewable Energy

Active power balance control of wind-photovoltaic-storage power system based on transfer learning double deep Q-network approach

IntroductionThis study addresses the challenge of active power (AP) balance control in wind-photovoltaic-storage (WPS) power systems, particularly in regions with a high proportion of renewable energy (RE) units. The goal is to effectively manage the AP balance to reduce the output of thermal power generators, thereby improving the overall efficiency and sustainability of WPS systems.MethodsTo achieve this objective, we propose the transfer learning double deep Q-network (TLDDQN) method for controlling the energy storage device within WPS power systems. The TLDDQN method leverages the benefits of transfer learning to quickly adapt to new environments, thereby enhancing the training speed of the double deep Q-network (DDQN) algorithm. Additionally, we introduce an adaptive entropy mechanism integrated with the DDQN algorithm, which is further improved to enhance the training capability of agents.ResultsThe proposed TLDDQN algorithm was applied to a regional WPS power system for experimental simulation of AP balance control. The results indicate that the TLDDQN algorithm trains agents more rapidly compared to the standard DDQN algorithm. Furthermore, the AP balance control method based on TLDDQN can more accurately manage the storage device, thereby reducing the output of thermal power generators more effectively than the particle swarm optimization-based method.DiscussionOverall, the TLDDQN algorithm proposed in this study can provide some insights and theoretical references for research in related fields, especially those requiring decision making.

A wind farm control strategy for frequency regulation reserve: Optimize wake loss and frequency support capability

—With a high proportion of renewable energy, the issue of grid frequency fluctuations is becoming increasingly prominent. To tackle this challenge, wind farms can enhance frequency regulation response by increasing rotational kinetic energy through overspeed control. However, this approach may result in energy losses due to the wake effect. This paper proposes an optimized configuration strategy for wind farm frequency regulation reserve (FRR), utilizing pitch angle cooperative control to reduce wake losses. The objective is to explore the optimal configuration for maximizing wind energy utilization under FRR conditions. A genetic algorithm is used to find the optimal control parameters in the established FLORIS-based wind farm power reserve wake model, which involves rotor speed and pitch angle control, to maximize output power and rotational kinetic energy. Based on this, a lookup table is constructed to map wind conditions to the optimal control parameters for different FRR level. According to the reserve power schedule, optimization control is applied to the wind farm. Simulation results show that, compared to the traditional equal distribution strategy for FRR power, the proposed control strategy can enhance the total kinetic energy reserve of the wind farm by optimizing wake loss, thereby improving frequency support capability.

Rolling planning model for high proportion renewable energy generation power system considering frequency security constraints

In this paper, a rolling planning model for high proportion renewable energy generation power systems is proposed, considering frequency security constraints, to address the frequency stability challenges posed by increased integration of wind and solar energy into the power grid under the “double carbon” goal. First, this study establishes a frequency response model for a thermal generation unit (TGU) and analyzes the impact of the high proportion of renewable energy on system frequency stability. Subsequently, a dynamic frequency security constraint is formulated, which can be used in generation planning to address the frequency issues. Second, a bi-level rolling planning model for high proportion renewable energy generation power systems considering dynamic frequency security constraints is established. The upper level focuses on investment decisions. The lower level incorporates frequency security constraints into the operational simulation to guarantee the frequency stability of the power system when making decisions on the size of newly built TGUs annually. Finally, case studies are conducted to demonstrate the validity and effectiveness of the proposed rolling planning model.

Assembly and operation optimization of proton exchange membrane water electrolyzer for performance enhancement

Abstract Proton exchange membrane water electrolysis (PEMWE) for hydrogen production possesses wide-ranging and rapid dynamic response capabilities, offering promising applications in the consumption of new energy sources and the dynamic balancing of power grids with a high proportion of renewable energy. The assembly and operating conditions of PEMWE significantly affect its performance. Therefore, thoroughly studying and understanding the influence of PEMWE’s assembly and operating conditions on water electrolysis performance are crucial for enhancing PEMWE’s performance and promoting its applications. In this research, the influence of installation preload torque, feedwater flow rate, and operating temperature on the performance of the electrolyzer, including the electrochemical active specific surface area (ECSA), high-frequency resistance (HFR) and various types of polarizations (including activation polarization, ohmic polarization, and mass transfer polarization) during the operation of the electrolyzer, were detailedly investigated. The results showed that the optimal preload torque and operating temperature for the electrolyzer were 3 Nm and 80°C. Under these conditions, an optimized PEMWE performance would be achieved by ensuring that the feedwater flow rate exceeded the water consumption during electrolysis.

Fault Diagnosis of Pumped Storage Units—A Novel Data-Model Hybrid-Driven Strategy

Pumped storage units serve as a crucial support for power systems to adapt to large-scale and high-proportion renewable energy sources by providing a stable and flexible energy supply. However, due to the coupling effects of electric power load demands and the complex multi-source factors within the water–mechanical–electrical system, the interrelationship between unit parameters becomes more intricate, posing significant threats to the operational reliability and health status of the units. The complexity of fault diagnosis is further aggravated by the intricate and varied nature of fault characteristics, as well as the challenges in signal extraction under conditions of strong electromagnetic interference and high noise levels. To address these issues, this paper proposes a novel data-model hybrid-driven strategy that analyzes vibration signals to achieve rapid and accurate fault diagnosis of the units. Firstly, the spectral kurtosis theory is employed to enhance the traditional empirical mode decomposition, achieving optimal decomposition and noise reduction effects for vibration signals. Secondly, the intrinsic mode functions (IMFs) obtained from the decomposition are reconstructed, and the entropy values of effective IMFs are calculated as fault feature vectors. Subsequently, the CNN-LSTM model is utilized for fault diagnosis. The effectiveness and feasibility of the proposed method are verified through actual operational data from pumped storage units in a specific region. Through analysis, the fault diagnosis accuracy of the method proposed in this paper can be maintained above 95%, demonstrating robustness in complex engineering environments and effectively ensuring the safe and stable operation of pumped storage units.

Optimal economic configuration by sharing hydrogen storage while considering distributed demand response in hydrogen-based renewable microgrid

With the issues of the greenhouse effect and environmental pollution globally, a high proportion of renewable energy has been integrated into microgrids. Its interconnection brings many challenges to the electric power industry. This paper proposes a new distributed response strategy through sharing hydrogen storage resources, aiming to solve the supply-demand imbalance in microgrids. First, the uneven power distribution from shared energy storage stations necessitates ensuring a fair power dispatch among users. Next, a microgrid demand response model with multi-constraint conditions and high penetration of wind and solar power is established, incorporating constraints like user power consumption, energy storage capacity, and charging/discharging power while considering differences in user electricity behaviors. Finally, the optimal economic configuration uses Lagrange duality theory to process the integrated models, including the demand response, hydrogen sharing, and power generation models, to make the optimization goals fairer. Results demonstrate that our strategy can adjust user electricity usage according to real-time supply-demand conditions, reducing users' total costs by 39.4 %. Compared with the existing configurations of sharing hydrogen storage configurations, our model uses more accurate utility functions, demonstrating better economic advantages, with users' total costs reduced by 4.89 % and revenue of the shared storage station increased by 4.02 %.

Optimal Battery Storage Configuration for High-Proportion Renewable Power Systems Considering Minimum Inertia Requirements

With the continuous development of renewable energy worldwide, the issue of frequency stability in power systems has become increasingly serious. Enhancing the inertia level of power systems by configuring battery storage to provide virtual inertia has garnered significant research attention in academia. However, addressing the non-linear characteristics of frequency stability constraints, which complicate model solving, and managing the uncertainties associated with renewable energy and load, are the main challenges in planning energy storage for high-proportion renewable power systems. In this context, this paper proposes a battery storage configuration model for high-proportion renewable power systems that considers minimum inertia requirements and the uncertainties of wind and solar power. First, frequency stability constraints are transformed into minimum inertia constraints, primarily considering the rate of change of frequency (ROCOF) and nadir frequency (NF) indicators during the transformation process. Second, using historical wind and solar data, a time-series probability scenario set is constructed through clustering methods to model the uncertainties of wind and solar power. A stochastic optimization method is then adopted to establish a mixed-integer linear programming (MILP) model for the battery storage configuration of high-proportion renewable power systems, considering minimum inertia requirements and wind-solar uncertainties. Finally, through a modified IEEE-39 bus system, it was verified that the proposed method is more economical in addressing frequency stability issues in power systems with a high proportion of renewable energy compared to traditional scheduling methods.

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.

Participation of wind-storage combined system in the secondary frequency regulation control strategy of power grid

Abstract The secondary frequency modulation of the unit, that is, the automatic generation control (AGC), is the process of increasing or decreasing the power of the power generation unit by the power plant unit according to the dispatching command issued by the power grid dispatching center, and then using the frequency modulator to restore the frequency. Based on the principle of AGC, this paper establishes a frequency dynamic response model of the secondary frequency modulation of the traditional unit and the wind storage participation system including load frequency control (LFC) and traditional proportional-integral-derivative (PID) controllers, determines the frequency modulation control strategy of wind storage, and uses the distribution mode based on Area Control Error (ACE) signal to assist in the secondary frequency modulation. In order to solve the parameter optimization problem of the classical PID controller when the wind-storage combined system participates in the secondary frequency modulation under the large load disturbance, a parameter optimization model of the controller with PID was established, and the ITAE evaluation index was finally selected as the fitness function by comparing different fitness evaluation indexes. To overcome the problem that the conventional Particle Swarm Optimization (PSO) is prone to local extremum, the inertia weights and learning factors are adjusted nonlinearly to balance and optimize the global and local optimization capabilities and improve the search efficiency, and the parameters of the PID controller are optimized by the improved PSO algorithm. The simulation results show that the proposed algorithm improves the secondary frequency modulation effect under the background of a high proportion of renewable energy connected to the power grid.

How to choose mobile energy storage or fixed energy storage in high proportion renewable energy scenarios: Evidence in China

With the large-scale integration of renewable energy and changes in load characteristics, the power system is facing challenges of volatility and instability. Therefore, enhancing the safe and stable operation capability of the power system is an urgent problem that needs to be solved. Mobile energy storage can improve system flexibility, stability, and regional connectivity, and has the potential to serve as a supplement or even substitute for fixed energy storage in the future. However, there are few studies that comprehensively evaluate the operational performance and economy of fixed and mobile energy storage systems. To comprehensively evaluate the economic benefits of large-scale mobile energy storage systems, this paper constructs an overall horizontal cost model for energy storage systems that considers the power system and train transportation system. This model not only introduces the traditional concept of lifecycle cost of energy storage systems, but also considers the costs of traditional transmission lines and battery transportation. Secondly, to achieve simulation of large-scale mobile energy storage system planning and operation, this paper establishes a multi-region power planning and operation simulation (MPO) model and a battery transportation and logistics (BTL) model to accurately reflect the operation mode of fixed energy storage and mobile energy storage in the power system. Finally, taking the actual power grids and railway networks in Northeast and North China as case studies, this article provides an in-depth analysis of the technical, economic, and environmental performance of large fixed and mobile energy storage systems, and conducts a detailed comparison. The research results indicate that under high grid connection ratios (using 75% and 66% as examples), the overall cost of mobile energy storage systems continues to decrease to 1.42 CNY/kWh and 0.98 CNY/kWh. Compared to fixed energy storage at 5.45 CNY/kWh and 4.79 CNY/kWh, it has an absolute economic advantage and shows significant carbon reduction capabilities, reaching 241,800 to 1,394,600 tons. This discovery fully confirms the enormous potential and application value of mobile energy storage in high proportion renewable energy scenarios, providing strong technical support and economic analysis basis for the sustainable development of the power system.

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

On the Internationalization of English Teaching System in the Planning of Sustainable Development of Future Renewable Energy

Energy is an essential element of human production and business activities. At present, China's energy development and utilization is facing the contradiction between increasing environmental pressure and high emission energy consumption structure. Based on the optimal power flow algorithm, this paper optimizes the scheduling and international teaching of the power system with a high proportion of renewable energy. The probability density function of fan output and photovoltaic output is obtained by wind speed and light intensity model, and the probability density function of load is also proposed. Then, interruptible load, microgrid and energy storage are used as flexible resources to improve the system’s flexibility to meet the random fluctuations caused by a high proportion of wind and light, so as to maintain the stable operation of the power grid. The mathematical model of process optimization power flow satisfying process constraints is a nonlinear time-varying system. The key to process optimization power flow algorithm is to find a simple and effective linearization and discretization method that is in line with the actual power network. On the basis of ensuring the accuracy of the model, the theory, model and algorithm of the process optimization power flow are studied on the basis of the proposed linear time period, the approximate linear time variability of the node voltage, and the goal of greatly simplifying the problem. Finally, through simulation analysis, comparing the results of model 1 and model 2, we can see that from the point of view of the power abandonment cost, the power abandonment cost of model 1 is 2.29 million yuan, which is less than the 8 million yuan of model 2. From the perspective of depth peaking and start-stop times, the total depth peaking of model 1 is 331 times, which is greater than that of model 2, 258 times. The total start-stop of model 1 is 15 times, which is more than 9 times of model 2, so the total cost of optimized electricity of wind and light in model 2 is less than that in model 1. According to the optimal scheduling model of wind and light renewable energy, the effective utilization rate of renewable energy in China is low, and the teaching system should pay attention to this problem. Therefore, this paper puts forward an international English teaching strategy for the utilization rate of renewable energy.

Research on the Evolution Process of New Distribution Network Forms Considering Low Carbon and Sustainable Development

To obtain the planning and development status of new distribution networks under the low-carbon and sustainable development goals, research the evolution process of new distribution network forms under the “carbon peak” and “carbon neutrality” goals (dual carbon goals). Firstly, combining the characteristics of high proportion renewable energy, construct a new distribution network morphology evolution system under the “dual carbon target”. Explore the energy pattern and structural form of the current and medium/long-term new distribution network, and obtain the corresponding evolution process. Then, considering the transmission characteristics of the new distribution network under the “dual carbon target”, an evaluation index system is established in three aspects: power supply, distribution, and transmission. And construct an evaluation model for the development form of distribution networks based on SWOT analysis, to evaluate the new development form of distribution networks. Finally, numerical examples were verified and analyzed using MATLAB simulation software. The method proposed in this article not only intuitively describes the evolution process of the new distribution network morphology, but also obtains the evolution of the new distribution network at different time stages under the “dual carbon target”, as well as the evolution results of the distribution network morphology in terms of carbon emissions and cost investment. The final research results of this article can provide theoretical basis for distribution network planning under the “dual carbon goal”.

Promoting Decarbonization in China: Revealing the Impact of Various Energy Policies on the Power Sector Based on a Coupled Model

The carbon emissions of the power industry account for over 50% of China’s total carbon emissions, so achieving carbon peak and carbon neutrality in the power sector is crucial. This study aims to simulate the impacts of three energy policies—carbon constraints, the development of a high proportion of renewable energy, and carbon trading—on China’s energy transition, economic development, and the power sector’s energy mix. Through the construction of a dynamic computable general equilibrium (CGE) model for China and its integration with the SWITCH-China electricity model, the impact of diverse energy policies on China’s energy transition, economic progress, and the power mix within the electricity industry has been simulated. The integration of the SWITCH-China model can address the limitations of the CGE model in providing a detailed understanding of the specific intricacies of the electricity sector. The results indicate that increasing the stringency of carbon restrictions compels a reduction in fossil energy use, controlling the output of coal-fired power units, and thereby reducing carbon emissions. The development of a high proportion of renewable energy enhances the cleanliness of the power sector’s generation structure, further promoting the national energy transition. Implementing a carbon trading policy, where the entire industry shares the burden of carbon reduction costs, can effectively mitigate the economic losses of the power sector. Finally, the policies to further enhance the implementation of carbon trading policies, strengthen effective governmental regulation, and escalate the deployment of renewable energy sources are recommended.

Research on simulation accuracy of equivalent aggregate electromagnetic model optimization for large renewable energy stations

Abstract The traditional power grid is rapidly transforming into a new power system with high penetration of new energy. Simulation modeling plays an important role in supporting the cognition and research of the new power system. A high proportion of renewable energy is connected to the grid through power electronic components, which puts forward a higher demand for research on the accuracy and stability of power grid simulation modeling. Renewable energy plants usually have a large number of single units, and equivalent aggregation modeling is the usual way to simplify the modeling of renewable energy plants. However, the transmission line power multiplier equivalent module inevitably introduces series impedance into the network, resulting in errors between simulation and actual engineering. Therefore, the study of lossless multiplier elements based on Modified Nodal Analysis (MNA) can improve the simulation accuracy, and it is verified with the simulation results of detailed models in actual scenarios, proving that MNA lossless multiplier elements can optimize the accuracy and stability of equivalent aggregation modeling of large-scale multi-unit renewable energy stations.

A multi-timescale and chance-constrained energy dispatching strategy of integrated heat-power community with shared hybrid energy storage

The community in the future may develop into an integrated heat-power system, which includes a high proportion of renewable energy, power generator units, heat generator units, and shared hybrid energy storage. In the integrated heat-power system with coupling heat-power generators and demands, the key challenges lie in the interaction between heat and power, the inherent uncertainty of renewable energy and consumers’ demands, and the multi-timescale scheduling of heat and power. In this paper, we propose a game theoretic model of the integrated heat-power system. For the welfare-maximizing community operator, its energy dispatch strategy is under chance constraints, where the day-ahead scheduling determines the scheduled energy dispatching strategies, and the real-time dispatch considers the adjustment of generators. For utility-maximizing consumers, their demands are sensitive to the preference parameters. Taking into account the uncertainty in both renewable energy and consumer demand, we prove the existence and uniqueness of the Stackelberg game equilibrium and develop a fixed-point algorithm to find the market equilibrium between the community operator and community consumers. Numerical simulations on integrated heat-power community show that the proposed solution outperforms the constant curtailment strategy with the daily cost reduced by 14.38%.

Design of a Stochastic Electricity Market Mechanism with a High Proportion of Renewable Energy

Design of a Stochastic Electricity Market Mechanism with a High Proportion of Renewable Energy