Renewable Energy Output Research Articles

Multi-scenario flexibility requirement analysis of high proportion of new energy access to power system

Abstract To address climate change, the proportion of renewable energy integration into the grid system is gradually increasing, leading to higher demands for flexibility. Current research typically employs methods such as dynamic system modeling, the construction of flexibility indicators, and scenario analysis to measure the flexibility requirements of the power system across different time scales. The use of frequency decomposition algorithms to explore flexibility requirements from the perspective of net load curves is relatively rare. This study utilizes net load data from the Western Inner Mongolia Power Grid to explore the distribution patterns of net load across different time scales under various seasonal and penetration scenarios using frequency decomposition algorithms. The results reveal that renewable energy output exhibits significant fluctuations and distinct diurnal variations, while net load also shows notable patterns and considerable volatility and seasonality. The analysis of flexibility requirements on typical seasonal days highlights the differences in demand distribution at various frequencies, with long-time-scale components primarily reflecting the overall trend of net load, while the medium-to-long time scale components characterize smaller, more frequent fluctuations. Additionally, the uncertainty associated with wind and solar output significantly affects the diurnal fluctuations of net load, with seasonal changes mainly represented in short-time-scale components. The study also emphasizes the impact of different penetration levels on flexibility requirements, indicating that as penetration decreases, the midnight requirement peak diminishes, suggesting differences in flexibility requirements based on renewable energy integration levels. Furthermore, the paper proposes corresponding solution technologies for “generation–grid–load–storage” across different time scales of flexibility requirements, ensuring the stable operation of the grid amidst climate change and rising electricity demand.

Multi‐timescale optimal scheduling of microgrids for generating new energy output scenarios based on correction error sampling intervals

AbstractMicrogrid can realize energy saving and emission reduction and multi‐energy complementation, but the fluctuation of renewable energy output and the error of day‐ahead scheduling will threaten the stability of microgrid operation. For this reason, this article proposes a microgrid multi‐timescale optimal scheduling method based on new energy output scenario generation. First, the microgrid framework of this article is introduced, and an energy cycle emission reduction model taking into account electricity‐to‐gas conversion is designed; second, a new energy prediction error model is established based on the prediction box and Gaussian hybrid model, and the scenario of wind and photovoltaic power generation is generated by correcting the sampling intervals for error sampling; and then, based on the day‐ahead scheduling plan, an intraday cooling, heating and electricity two‐layer rolling optimization model is established to correct the day‐ahead scenario. Finally, the example analysis shows that the cyclic emission reduction model can realize the recycling of resources, reduce the fuel cost and carbon emission, and the generation of scenarios for wind power can smooth out the fluctuation of new energy power, which, together with the intra‐day two‐layer rolling optimization, can reduce the day‐ahead scheduling error and improve the stability of microgrid operation.

Optimization operation strategy of wind–pumped storage integrated system participation in spot market considering dual uncertainties

Abstract With the gradual increase in the penetration rate of renewable energy, the multifunctional role of pumped storage is becoming increasingly prominent, and the joint operation of “renewable energy + pumped storage” is a current research hotspot. However, during the joint system operation, there are dual risks from internal (renewable energy output) and external (market prices) factors, which significantly impact the system’s overall revenue. Therefore, an analysis is conducted around the operational mechanism of the “wind power–pumped storage” joint operation, and the uncertain factors faced during the system’s operation are identified. Second, an optimization model for wind power–pumped storage under deterministic scenarios is constructed, employing robust optimization theory and information gap decision theory to describe the uncertainty of electricity prices and wind power, thus forming a hybrid of the information gap decision theory and the robust optimization model for wind power–pumped storage. Finally, the results show that: (1) The total revenue of the model proposed in the paper has increased by 2.36% compared to the robust optimization model and by 9.04% compared to the deterministic model, significantly enhancing the model’s robustness and risk resistance capabilities. (2) From the perspective of the economic feasibility of different energy storage system configurations, the wind plant equipped with pumped storage has the highest economic feasibility, with an internal rate of return of 9.8% and net present value of 872 million Chinese Yuan, which is higher than that of compressed air energy storage and electrochemical energy storage systems. (3) Decision makers can set the risk deviation coefficient and the uncertainty budget according to their risk preferences, thereby changing the robustness of the model for differentiated decision making. However, an increase in the uncertainty budget coefficient will cause the total revenue of the joint operation system first to increase and then decrease, with the maximum revenue achievable within the range of 500–625; the total revenue reaches its maximum when the risk deviation coefficient is between 0.1 and 0.125.

Cooperative game robust optimization control for wind-solar-shared energy storage integrated system based on dual-settlement mode and multiple uncertainties

Aiming at the problems of renewable energy output uncertainties and single scenario operation mode of energy storage systems, a cooperative game robust optimization control method for wind-solar-shared energy storage system based on dual-settlement mode of power market is proposed in this paper. A cooperative game-based energy management framework under dual settlement mode of electricity market is constructed, the profit relationship between shared energy storage under multiple application scenarios and renewable energy are extracted and the corresponding profit models are established. Considering the multiple uncertainties of renewable energy and electricity prices, combined with robust optimization theory, a multi-level two-stage robust optimization model is established to make optimal electricity trading decisions for renewable energy and shared energy storage. Additionally, the cooperative game robust optimization model is solved by i-C&CG algorithm. The effectiveness of proposed control method is verified through actual operating data of a certain power grid in China. The simulation results show that the cooperative game robust optimization model achieves the optimal operation of the wind-solar-shared energy storage system considering multiple uncertainties, which can improve the ability of the system to cope with the uncertainty risk and the reliability of the system.

Muti‐units day‐ahead scheduling involving the pumped storages and considering deep‐load regulation

AbstractThis paper presents a day‐ahead scheduling for multi‐energy entities. The deep load regulation involving pumped storages, which refers to deep peak regulation, is adopted to address the impact of wind power and photovoltaic (PV) uncertainties, thereby improving the economic efficiencies of day‐ahead dispatching. And the impact of different peak valley electricity price differences on the peak shaving effectiveness of pumped storage energy was studied. Firstly, the multi‐scenario random programming method is applied to solve the prediction uncertainties of wind power and PV output in the day‐ahead. Subsequently, the multi‐scenario set of day‐ahead wind power and PV output and the load forecasting curve are considered. A pumped storage scheduling model is then established integrating the hydropower, thermal power and pumped storage. The optimal generation scheduling of pumped storage and thermal units is determined by minimizing load fluctuations and peak shaving costs. Finally, a local power grid in the Hunan province of China is selected for verification. It is shown that the proposed model can effectively accommodate the fluctuation of renewable energy output, reduce the peak regulating pressure of thermal units, and improve the operational economy of the power system.

Reinforcement Learning for EV Fleet Smart Charging with On-Site Renewable Energy Sources

In 2020, the transportation sector was the second largest source of carbon emissions in the UK and in Newcastle upon Tyne, responsible for about 33% of total emissions. To support the UK’s target of reaching net zero emissions by 2050, electric vehicles (EVs) are pivotal in advancing carbon-neutral road transportation. Optimal EV charging requires a better understanding of the unpredictable output from on-site renewable energy sources (ORES). This paper proposes an integrated EV fleet charging schedule with a proximal policy optimization method based on a framework for deep reinforcement learning. For the design of the reinforcement learning environment, mathematical models of wind and solar power generation are created. In addition, the multivariate Gaussian distributions derived from historical weather and EV fleet charging data are utilized to simulate weather and charging demand uncertainty in order to create large datasets for training the model. The optimization problem is expressed as a Markov decision process (MDP) with operational constraints. For training artificial neural networks (ANNs) through successive transition simulations, a proximal policy optimization (PPO) approach is devised. The optimization approach is deployed and evaluated on a real-world scenario comprised of council EV fleet charging data from Leicester, UK. The results show that due to the design of the rewards function and system limitations, the charging action is biased towards the time of day when renewable energy output is maximum (midday). The charging decision by reinforcement learning improves the utilization of renewable energy by 2–4% compared to the random charging policy and the priority charging policy. This study contributes to the reduction in battery charging and discharging, electricity sold to the grid to create benefits and the reduction in carbon emissions.

Management of Hybrid Hydropower and Floating Solar Systems at Num Ngum 1 in Lao PDR

The integration of a hybrid hydro-floating solar power (HPP-FPV) system is covered in this study with the goal of improving energy management and producing more electricity. The production capacity of the hybrid system is almost twice as high as that of the original HPP system, demonstrating its potential for higher energy generation. Because it forecasts weather and modifies electricity generation accordingly. The analysis highlights how crucial it is to make sure that the hybrid system's electricity generation doesn’t go beyond the current power grid's maximum transmission capacity. This is essential to preserving the consistency and caliber of the power that is provided. The research's overall goal of increasing the output of renewable energy through creative system integration and practical management techniques is captured in the abstract.

High-resolution assessment of wind energy potential in the Hami region of Northwestern China

Abstract Wind energy plays a pivotal role in the global effort to mitigate climate change, with China emerging as a leader in renewable energy adoption. The Hami region in northwestern China stands out as a crucial area for wind power development, given its substantial wind resources and strategic importance in China’s energy landscape. However, existing studies on wind energy potential vary widely and involve large uncertainties due to sparse measurements and coarse resolution, highlighting the need for more precise assessments to guide policy decisions and optimize energy utilization. This study leverages high-resolution ERA5 reanalysis data and advanced wind turbine technology to assess wind energy potential in the Hami region, taking into account factors including wind speed patterns, turbine heights, and geographical constraints. The comparison with in-situ data demonstrated that high-resolution ERA5 reanalyzed wind speeds enable to capture multi-year wind speed variations in this region. We find substantial potential for wind energy in Hami, with energy densities exceeding 200 W/m² in the high-potential wind zones. Importantly, this study identifies a new high-potential area in eastern Hami, termed the East Wind Zone. Our high-resolution assessment of wind energy potential at different heights over the past two decades reveals long-term trends and seasonal variations. Increasing the hub height from 95 m to 140 m raises the average wind power generation potential across Hami by 31.3 GWh/year. Our findings highlight the importance of strategic wind farm placement to maximize renewable energy output and provide insights for policy and industry, supporting China's renewable energy goals.

Strategic behaviors of renewable energy generation companies participating in the electricity and carbon coupled markets based on non-cooperative game theory

Renewable energy (RE) can participate in electricity and carbon market trading in China, which can help stimulate the investment of RE. Thus, we propose a bi-level model for non-cooperative game interactions among power generation companies (GenCos), considering the electricity and carbon coupled markets. The upper-level model considers strategic behaviors driven by maximizing profits for GenCos. The lower-level model includes a two-stage electricity market: the day-ahead market and the real-time market; it implements joint clearing of energy and reserves, and continuous trading with competitive auctions in the carbon market based on the clearing results of the electricity market. Simulations are performed based on the actual data of the power system in a certain region of Guangdong Province in China. The results show that electricity and carbon coupled markets can effectively reduce market costs and carbon emissions, improve the society welfare and RE benefits. The uncertain increase in RE output and higher installed capacity reduce the total revenue of the RE in the coupled markets. The initial carbon quota decomposition optimization decision of the coal-fired GenCos and gradual reduction in the initial carbon quota will increase the revenue of the RE GenCos.

Exploring the impact of economic growth and sources of electricity production in India: New insights from sectoral analysis using NARDL approach

This study aims to explore the asymmetric impact of renewable energy on the sectoral output of the Indian economy by analyzing the time series data from 1971 to 2019. The nonlinear autoregressive distributed lag approach (NARDL) is employed to examine the short- and long-run relationships between the variables. Most studies focus on economic growth, ignoring sectoral dynamics. The result shows that the sectoral output shows a differential dynamism with respect to the type of energy source. For instance, agricultural output responds positively to the positive shock in renewable energy, whereas industry and service output behave otherwise. Since the latter sectors depend heavily on non-renewable energy sources, they behave positively towards them. Especially, electricity produced from non-renewable energy sources significantly influences service sector output. However, growing evidence across the world is portraying the strong relationship between the growth of renewable energy sources and economic growth. However sectoral dynamism is crucial to frame specific policies. In this regard, the present paper’s result indicates that policies related to promoting renewable energy sources will significantly influence sectoral output in the long run in India.

Dynamic Pricing and Energy Management of Virtual Power Plant Based on Source-Charge Uncertainty

With the intermittent, random and volatile distributed renewable energy mass influx into the grid and power market, the power system will face greater challenges. As a small energy autonomous system that integrates distributed generation, energy storage and load, virtual power plant connects the power market and end users, and it is also an important subject of power market reform. Its operating efficiency directly determines the effectiveness of market reform. However, virtual power plant faces the market risk of double uncertainty of internal renewable energy output and load demand during operation, so it needs to optimize the market behavior to protect its own interests. Therefore, the paper takes virtual power plant as the research object, and discusses the dynamic pricing strategy of virtual power plant considering the double uncertainty of source and charge and real-time market linkage from the aspects of theoretical analysis, modeling construction and simulation analysis, to provide reference for decision pricing of virtual power plant and formulation of electricity market reform policy.

Hydrogen storage planning robust to year‐round net load fluctuation

AbstractLong‐term hydrogen storage systems are considered a solution to the long‐term supply imbalance caused by different seasonal characteristics in renewable energy output and load. However, most existing planning models assume the whole year‐round net load series is known in advance and the optimal operation sequence of long‐term storage is derived accordingly. This assumption underestimates the necessary long‐term storage capacity because of the difficulty of accurate fine‐grained long‐term forecasting. This work proposes a long‐term hydrogen storage planning framework that is robust to year‐round net load fluctuation. The daily average component from the historical net load series is first derived to formulate the long‐term operation scenario set. On this basis, a robust planning model is developed where the trend of long‐term storage operation is forced to be identical for all long‐term operation scenarios and not to respond to one specific scenario with full information of the net load sequence. The results show that the planning results given by the proposed framework are more robust in real‐time operation with less load shedding.

Optimization of renewable energy hybrid power plant in the state of Karnataka, India

This paper proposes a methodology on reliability and cost minimization of the electricity produced by Renewable energy sources. The approach is to use Hybrid Renewable Energy Systems, which involves integrating various renewable sources. The objective of this study is to address these challenges by HRES method in the state of Karnataka. In Karnataka, there has been large scale curtailment of renewable energy generation due to the various structural issues. This study would give a solution to reliability and cost related issues faced by this state. For the study a Mathematical Model has been developed using Feed Forward Neural Network (FNN) and Chemical Reaction Optimization algorithms. Chemical reaction Optimization model is chosen to remove biases of the Artificial Neural Network Model of FNN. This model will be used to simulate interaction between Hybrid Renewable Energy Systems. The result of the study will provide a power system with high & optimized renewable energy output in the state of Karnataka, India which will be reliable and cost effective.

The Robust Optimization of Low-Carbon Economic Dispatching for Regional Integrated Energy Systems Considering Wind and Solar Uncertainty

In this paper, a two-stage robust optimization approach is employed to address the variability in renewable energy output by accounting for the uncertainties associated with wind and solar energy. The model aims to achieve a balanced system that is both low-carbon and economically efficient while also being resilient to uncertainties. Initially, a regional integrated energy system model is developed, integrating electricity, gas, and heat. The variability of wind and photovoltaic power outputs is represented using a modifiable uncertainty set. A resilient optimal scheduling model is formulated in two stages, with the objective of minimizing costs under worst-case scenarios. This model is solved iteratively through a column and constraint generation approach. Additionally, the scheduling model incorporates horizontal time shifts and vertical complementary substitutions for carbon trading costs and demand-side loads to avoid excessive conservatism and to manage carbon emissions and energy trading in the regional integrated energy system (RIES). Results show that the two-stage robust optimization approach significantly enhances the system’s resilience to risks and minimizes economic losses. The inclusion of carbon trading mechanisms and the demand response prevents the system from becoming overly robust, which could impede economic growth, while also reducing carbon emissions. The proposed method effectively achieves balanced optimal scheduling for a robust, economical, and low-carbon system.

A two‐stage reactive power optimization method for distribution networks based on a hybrid model and data‐driven approach

AbstractThe uncertainty of distributed energy resources (DERs) and loads in distribution networks poses challenges for reactive power optimization and control timeliness. The computational limitations of the traditional algorithms and the development of artificial intelligence (AI) based technologies have promoted the advancement of hybrid model‐data‐driven algorithms. This article proposes a two‐stage reactive power optimization method for distributed networks (DNs) based on a hybrid model‐data‐driven approach. In the first stage, based on the topology and line parameters of the DN, as well as forecasts of loads and renewable energy outputs, a mixed‐integer second‐order cone programming (MISOCP) algorithm is used to control the on‐load tap changer (OLTC) positions on an hourly day‐ahead basis. In the second stage, leveraging deep learning technology, the real‐time reactive power output of photovoltaics (PV) and wind power units is controlled at a 5‐min time scale throughout the day. Specifically, using traditional solvers, the global optimal reactive power output for PV and wind power units is determined first, corresponding to various load and renewable energy output scenarios. Then, neural networks are trained to map node power to the optimal reactive power outputs of renewable energy units, capturing the complex physical relationships. For the second stage, a transformer network framework with a self‐attention mechanism and multi‐head attention for deep learning training is applied to uncover the intrinsic and physical spatial relationships among high‐dimensional features. The proposed method is tested on a modified IEEE 33‐bus system with multiple distributed renewable energy sources. The case study results demonstrate that the proposed hybrid model‐data‐driven algorithm effectively coordinates day‐ahead and real‐time controls of various devices, achieving real‐time model‐free optimization throughout the day. Compared to traditional deep neural networks (DNNs) and convolutional neural networks (CNNs), the transformer network provides superior reactive power optimization results.

A two-step optimization model for virtual power plant participating in spot market based on energy storage power distribution considering comprehensive forecasting error of renewable energy output

As a complement to the medium and long-term market, the spot market plays an important role in maintaining the security and stability of the power grid. However, as spot trading is more proximate to the actual operation of the power system, the virtual power plant (VPP) is exposed to greater volatility in renewable energy output as well as market prices. To enhance its market competitiveness, this paper constructs a two-step optimization model for VPP participation in the spot market. Based on long-term transaction, the energy storage power is distributed based on the comprehensive forecasting error of renewable energy output. This makes the VPP obtain the maximum profit in the day-ahead market. The trading deviation penalty is reduced by adjusting the energy storage operation plan in the real-time market. The conditional value at risk is used to measure the risk of a trading strategy. The profit of VPP participation in the spot market is analyzed under different risk levels, thus providing a basis for decision makers with different risk appetites. The results show that: (1) The sensitivity of the growth rates of EtVaRαand EtCVaRα to risk level shows an opposite trend. And there is an excess of arbitrage power at low risk level. (2) Although the total cost decreases as the risk level increases, the actual profit in the spot market tends to increase and then decrease. The risk level of the VPP should be set at about 0.4 to make full use of energy storage and obtain the maximum market benefit. (3) The energy storage power can be distributed more accurately according to the forecasting error of renewable energy. This not only enhances its arbitrage ability, but also ensures its regulation ability, thus improving the overall benefit of VPP at different risk levels. (4) The sensitivity of the trend in the unit profit of traded quantity in the spot market to the trading proportion in the medium and long-term market is not monotonic.

Security-constrained unit commitment model considering frequency and voltage stabilities with multiresource participation

The rapidly increasing proportion of renewable energy sources has led to a reduction in the relative share of synchronous units, which has resulted in a decline in the inertia of the power system and a decrease in its voltage support capacity. This has led to several issues related to the frequency and voltage stabilities of the power system. To ensure these frequency and voltage stabilities, it is necessary to maintain a number of synchronous units online in the day-ahead generation schedule. First, the dynamic frequency change process after a power system fault is discussed, and a linear expression is derived for the frequency stability constraints involving energy storage systems and wind turbines. Second, the short-circuit capacity of the bus is characterized to describe the strength of voltage support, and the minimum short-circuit capacity requirement of the bus is solved based on the transient voltage recovery problem after clearance of the short-circuit faults. The total short-circuit capacity provided by the unit to the bus is then calculated through the network reactance matrix to establish the voltage stability constraint. Subsequently, the security-constrained unit commitment model considering frequency and voltage stabilities (FVS-SCUC) is established. Finally, the effectiveness of the proposed model is demonstrated through a numerical simulation of the IEEE-39 system comprising storage and wind turbines. The model ensures the frequency and voltage securities of the system, and the renewable energy output is improved upon considering energy storage. Thus, the overall system cost was reduced by nearly 30% by considering the frequency regulation effects of the energy storage system and wind turbine as well as the voltage regulation effects of the energy storage system.

Research on the Configuration of a 100% Green Electricity Supplied Zero-Carbon Integrated Energy Station

In the context of rapid growth in renewable energy installations and increasingly severe consumption issues, this paper designs a 100% green electricity supplied zero-carbon integrated energy station. It aims to analyze its configuration focusing on the following three core features: zero carbon emissions, 100% green electricity supply, and a centralized–distributed system structure. It discusses equipment selection and provides models for configuring upstream green electricity resources, power generation, energy storage, transformer, and energy conversion. The study examines the synergy between lithium-ion battery storage and modular molten salt thermal storage, along with the virtual energy storage characteristics formed by thermal load inertia, supported by mathematical models. Based on the data from a green electricity system in an Eastern Chinese city and typical load profiles, the paper validates a specific configuration for a 100% green electricity supplied zero-carbon integrated energy station, confirming model accuracy and calculating the required scale of upstream green electricity resources. It proves that establishing an electro-thermal storage synergy system is crucial for addressing the significant fluctuations in renewable energy output. It also argues that leveraging thermal load inertia to create virtual storage can reduce the investment in energy storage system construction.

Optimal reactive power dispatch with renewable energy sources using hybrid whale and sine cosine optimization algorithm

The optimization of reactive power dispatch entails the complex challenge of controlling and managing the flow of reactive power in power networks to maintain desired voltage levels across many buses. Nowadays, there is a rising preference for employing renewable energy sources rather than traditional thermal generators. This change presents both challenges and possibilities for power system operators and managers. This paper addresses the Optimal Reactive Power Dispatch (ORPD) problem by presenting a novel approach that incorporates solar and wind power plants into existing power networks using the Hybrid Whale and Sine Cosine Optimisation Algorithm (HWSCOA). Solar and wind power plants are established at bus 5 and bus 8 respectively to replace traditional thermal generators in a specific case study using the IEEE 30-bus system. To handle uncertainties associated with load demand changes and the intermittent nature of renewable energy generation, the study employs probability density functions and a variety of scenarios. The primary goal is to minimize power losses in transmission cables while also lowering voltage changes throughout the network. To address uncertainty in load demands and renewable energy output, a scenario-based methodology is used, generating 30 different scenarios to cover all conceivable outcomes. By presenting the ORPD challenge as an optimization problem, the study hopes to achieve considerable reductions in power losses and voltage variations from nominal levels. The findings of this study reveal encouraging results, including significant reductions in power losses and optimized voltage stability even under shifting conditions.

Enhancing reliability assessment in distributed generation networks: Incorporating dynamic correlation of wind-solar power output uncertainty

Amidst escalating environmental concerns and energy scarcity, the integration of distributed generation (DG) within distribution networks (DN) has emerged as a pivotal developmental trend. The uncertainty inherent in renewable energy output often disrupts DG networks. Notably, the dynamic correlation between key renewable sources, such as wind and solar energy, significantly influences the reliability analysis of these networks.To comprehensively assess the impact of wind-solar power output uncertainty and its dynamic correlation on DN reliability, this study leverages copula theory to express the dynamic correlation coefficient between wind and solar power. This coefficient is formulated as the dynamic correlation of wind-solar power through copula dynamic correlation coefficient. Employing an auto-regressive moving average (ARMA) model with constraints solved using maximum likelihood kernel (MLK), we construct the wind-solar joint output (WSJO) model. Subsequently, utilizing sequential Monte Carlo simulation (MCS) with the WSJO model, we analyze DN reliability. In case of DN failure, the WSJO model generates random samples of the wind-solar joint output sequence. Subsequent power restoration to governed islands enables the calculation of DN reliability indices. The WSJO model constructed in this study accounts for wind resource output uncertainty and dynamic correlation, aligning more closely with actual distributed generation output and enhancing the accuracy of reliability assessment. Finally, we simulate the improved IEEE-RBTS-BUS6-F4 system to underscore the crucial role of considering wind-solar energy's dynamic correlation in DN reliability assessment.