Renewable Energy Output Research Articles

Interactive scheduling optimization of regional multi-agent integrated energy systems considering uncertainties based on game theory

With the increasing number of integrated energy system (IES) construction, a multi-agent system group composed of multiple IESs would be formed in a certain region. Moreover, complex uncertainties gradually existed in the IES which affect the system optimal situation and optimization results. In order to take full use of each agent's advantages and improve the performance of the whole region under uncertainty, this paper focuses on the multiple energy types' interaction of multi-agent IESs within the region to realize the stable operation, economic and environmental improvement of each agent and the whole region. To this aim, a multi-objective multiple energy types' multi-agent interactive optimization model considering energy demand and renewable energy output uncertainties was built. The model was based on Nash bargaining cooperative game theory and took economy, flexibility and carbon emission as objectives. Moreover, energy interaction scheduling and its corresponding price can be optimized through the model. The results of non-cooperative operation mode without interaction and cooperative operation mode with interaction were compared to analyze the superiority of interaction from the cost, carbon emission and flexibility aspects. The overall regional carbon emission and the economic cost has been reduced by 13.84% and 9.94%, for each IES, the carbon emission and economic cost could reduce by up to 41.5% and 44.5%. Moreover, the flexibility of the IES was correspondingly increased by nearly 47%. These results denoted that after multi-agent interaction, not only the overall region's energy, economic and environmental performance are improved, but also that of each agent are significantly improved. In addition, different energy types among multiple agents could be effectively used and regional joint energy storage is realized, improving the region flexibility level and resilience.

Research on Multi-Objective Energy Management of Renewable Energy Power Plant with Electrolytic Hydrogen Production

This study focuses on a renewable energy power plant equipped with electrolytic hydrogen production system, aiming to optimize energy management to smooth renewable energy generation fluctuations, participate in peak shaving auxiliary services, and increase the absorption space for renewable energy. A multi-objective energy management model and corresponding algorithms were developed, incorporating considerations of cost, pricing, and the operational constraints of a renewable energy generating unit and electrolytic hydrogen production system. By introducing uncertain programming, the uncertainty issues associated with renewable energy output were successfully addressed and an improved particle swarm optimization algorithm was employed for solving. A simulation system established on the Matlab platform verified the effectiveness of the model and algorithms, demonstrating that this approach can effectively meet the demands of the electricity market while enhancing the utilization rate of renewable energies.

Optimal allocation method for MIES-based shared energy storage using cooperative game theory and CSP

To further promote the efficient use of energy storage and the local consumption of renewable energy in a multi-integrated energy system (MIES), a MIES model is developed based on the operational characteristics and profitability mechanism of a shared energy storage station (SESS), considering concentrating solar power (CSP), integrated demand response, and renewable energy output uncertainty. We propose a corresponding MIES model based on co-operative game theory and the CSP and an optimal allocation method for MIES shared energy storage. The model considers the maximum operating benefit of the SESS as the upper objective function and the minimum operating cost of the MIES as the lower objective function. First, the Karush–Kuhn–Tucker conditions of the lower-layer model are transformed into constraints of the upper-layer model, and the Big-M method is used to linearize the nonlinear problem and convert the two-layer nonlinear model into a single-layer linear model. Second, based on the Nash negotiation theory, the benefits of each IES in the MIES are allocated. Finally, the fuzzy chance constraints are used to relax the power balance constraints, and the trapezoidal fuzzy numbers are transformed into a deterministic equivalence class to assess the impact of renewable energy output uncertainty on system operation. The validity and rationality of the proposed two-layer model are verified through simulation, and the results demonstrate that the proposed shared storage capacity leasing model can effectively reduce the total operation cost, increase the profitability of the shared storage operator, and increase the utilization rate of the SESS.

A comprehensive survey of low‐carbon planning and operation of electricity, hydrogen fuel, and transportation networks

AbstractThe trend of global energy systems towards carbon neutrality has led to an escalating interdependency between electricity, hydrogen fuel, and transportation networks. However, the means of surmounting the many challenges confronting the optimal coupling and coordination of electric power, hydrogen fuel, and transportation systems are not sufficiently understood to guide modern infrastructure planning operations. The present work addresses this issue by surveying the extant literature, relevant government policies, and future development trends to evaluate the present state of technology available for coordinating these systems and then determine the most pressing issues that remain to be addressed to facilitate future trends. On the one hand, the users of transportation networks represent flexible consumers of electric power and hydrogen fuel for those connected via devices such as electric vehicles and hydrogen fuel cell vehicles through charging stations and hydrogen refuelling stations. On the other hand, power grids can mitigate the negative effect of random charging behaviours on grid security through time‐of‐use electricity pricing, while excess renewable energy outputs can be applied to generate hydrogen fuel. The findings of this overview offer support for infrastructure planning and operations. Finally, the most urgent issues requiring further research are summarised.

Solar and Wind Data Recognition: Fourier Regression for Robust Recovery

Accurate prediction of renewable energy output is essential for integrating sustainable energy sources into the grid, facilitating a transition towards a more resilient energy infrastructure. Novel applications of machine learning and artificial intelligence are being leveraged to enhance forecasting methodologies, enabling more accurate predictions and optimized decision-making capabilities. Integrating these novel paradigms improves forecasting accuracy, fostering a more efficient and reliable energy grid. These advancements allow better demand management, optimize resource allocation, and improve robustness to potential disruptions. The data collected from solar intensity and wind speed is often recorded through sensor-equipped instruments, which may encounter intermittent or permanent faults. Hence, this paper proposes a novel Fourier network regression model to process solar irradiance and wind speed data. The proposed approach enables accurate prediction of the underlying smooth components, facilitating effective reconstruction of missing data and enhancing the overall forecasting performance. The present study focuses on Midland, Texas, as a case study to assess direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and wind speed. Remarkably, the model exhibits a correlation of 1 with a minimal RMSE (root mean square error) of 0.0007555. This study leverages Fourier analysis for renewable energy applications, with the aim of establishing a methodology that can be applied to a novel geographic context.

The Temporal and Spatial Characteristics of Wind–Photovoltaic–Hydro Hybrid Power Output Based on a Cloud Model and Copula Function

In a high proportion of wind–photovoltaic–hydro hybrid power systems, fluctuation and dispersion make it difficult to accurately quantify the output characteristics. Therefore, in this study, a cloud model and copula correlation coefficient matrix were constructed for a hybrid power generation system based on the output data. Multiple backward cloud transformation based on the sampling-with-replacement method was proposed to calculate the improved entropy and hyperentropy to analyze the fluctuation range and dispersion degree quantitatively. A similarity index was proposed to evaluate the similarity between wind power, PV power, and hydropower. A suitable copula function was selected, and the Kendall and Spearman coefficients show the correlation relationships of the hybrid systems. The temporal and spatial characteristics of the hybrid systems were analyzed based on the two models. A typical example in Qinghai proved the effectiveness and applicability of the method. The results show that the correlation between photovoltaic power and hydropower is better and that, in summer, hydropower can be used to adjust the output of renewable energy.

Two-stage low-carbon optimal dispatch of the power system considering demand response to defend large uncertainties and risks

Introduction: In order to promote the consumption of renewable energy, reduce carbon emissions, and take into account the uncertainty of renewable energy output and load fluctuations in the new power system that can affect the normal operation of market mechanism, a two-stage low-carbon optimization scheduling method for power system that considers demand response under multiple uncertainties is proposed in this paper.Methods: Uncertain scene sets are generated through Latin hypercube sampling and heuristic synchronous backpropagation method is used to reduce scenes to obtain typical scenes and their probabilities. Then, a one-stage optimization model is established with the goal of maximizing energy efficiency and corresponding demand response strategies are obtained. Green certificate carbon trading joint mechanism model consisting of tiered green certificate trading and time-sharing tiered carbon trading are established, and the output of two-stage units are optimized with the goal of minimizing comprehensive operating costs.Result: The simulation results show that the carbon emissions are decreased by 251.57 tons, the consumption rate of renewable energy is increased by 8.64%, and the total costs are decreased by 124.0612 million yuan.Discussion: From this, it can be seen that the dual layer low-carbon optimization scheduling strategy for power system considering demand response under multiple uncertainties can effectively reduce the operating costs and carbon emissions of the system, while balancing the economic and environmental aspects of power system operation.

Optimal Coordination of Energy Coupling System Considering Uncertainty of Renewable Energy Sources

To advance carbon neutrality policies, many countries are increasingly integrating Renewable Energy Sources (RESs) into their energy mix. However, for harnessing natural energy in power generation, RESs present challenges in output control, potentially leading to power imbalances. Such imbalances, when coupled with an excessive reliance on unpredictable RES, may necessitate the curtailment of grid-integrated renewable generation, halting production despite available capacity. This paper proposes strategies to mitigate the economic and operational efficiency impacts of increased RESs on the power grid. It focuses on the optimal operation of Battery Energy Storage Systems (BESSs) and Power to Gas (P2G) technology, which flexibly converts surplus electricity into hydrogen. By employing Mixed Integer Linear Programming (MILP) for optimization and Stochastic Programming (SP) for the management of the uncertainty of renewable energy output, this paper evaluates the anticipated benefits of BESS and P2G applications.

Optimal economic dispatching strategy for power systems considering distributed controllable load clusters

The day-ahead dispatching of power system depends on the predicted renewable energy output and load power, which brings great challenges to the real-time power balance. This paper focuses on voltage sensitive loads (VSLs) that are widely distributed in space, thus exploring the real-time dispatching potential for VSLs. Firstly, based on the P-V relationship of VSLs clusters, a ZIP model is adopted to establish VSLs’ power controllable model. Meanwhile, considering the potential voltage instability risk during the process of voltage regulation, a linear relationship between voltage safety margin (VSM) index and node power variation was derived. Then, a two-stage linear dispatching model for controllable VSLs clusters was established to meet the needs of online application. In detail, the first stage mainly focuses on evaluating the scheduling potential of VSLs clusters and the second stage determines the optimal real-time response strategy for VSLs clusters. Finally, the effectiveness and feasibility of the proposed method are verified through an improved IEEE-30 node system.

Why germanys energiewende may fail to meet its goals: insights from power system simulation with natural generation

In order to achieve the Paris climate targets, the German government has developed phased plans and anchored these in an amendment to the Renewable Energy Sources Act (EEG). This not only defines expansion targets for renewable energy producers, but also the additional amounts of energy that must be available in the electricity grid in future if it is to supply the mobility and building heating sectors, which are currently still powered by fossil fuels. While the lack of grid transport capacities is recognized and discussed in the public debate, another known and critical hurdle is rarely presented here. This is the temporal distribution of power from renewable energies and its effects on the processes involved in controlling the grid balance. By analyzing a target scenario specified in the EEG for 2030 and another for 2040+, which a German economic research institute recommends to policymakers, the aim is to investigate whether their targets can be achieved under the conditions of the natural fluctuations occurring in Germany and the quantities of renewable energy that can be harvested with a reasonable use of resources. The focus is on simulating future generation curves using historical data in such a way that they show the typical weather-driven behaviour to be expected in Germany. Unavoidable Surplus generation is used as efficiently as possible with electricity and hydrogen storage systems. Both scenarios fall considerably short of their targets. CO2-free coverage of the demand specified for the long-term future is only possible with a quantity of generators that is at the limit of what is feasible. The reason for this is that the current plans are obviously based on an unrealistic picture of the temporal distribution of renewable energy output. Achieving the transformation goals in Germany could be much more complex than politicians currently believe and may require a change in strategy sooner or later.

Economic performance and carbon emissions: revisiting the role of tourism and energy efficiency for BRICS economies

AbstractIncreased demands for infrastructure, water, electricity, and different natural assets have triggered land erosion, climate change, and pollution increase and deterioration in biodiversity. The purpose of this research is to look at how economic performance, tourism, renewable energy, and energy efficiency affect carbon emissions in the emerging economies of BRICS during 1990–2021. Using panel estimation approaches, the empirical outcomes validate the longer-run equilibrium connection between the components of the model. Using a nonparametric estimator, the study found that economic performance is the significant driver of higher emissions levels in the sample countries. In contrast, tourism, energy efficiency, and renewable energy substantially reduce emissions levels and improve environmental sustainability. The estimated results have been found robust, and the feedback effect is found valid between repressors and carbon emissions. This study further suggests that investment in research and development, improvement in energy-efficient tools and equipment utilization, and enhanced renewable energy output are the key policy efforts for ensuring environmental sustainability.

Optimal configuration of energy storage for alleviating transmission congestion in renewable energy enrichment region

High-penetration renewable energy development causes transmission congestion in power system operation. Such transmission congestion in short period can be alleviated by energy storage configuration, instead of investing and expanding new transmission lines. This paper presents an optimal configuration method of energy storage for alleviating transmission congestion in renewable energy enrichment region. In order to obtain the characteristics of renewable energy outputs, a scenario generation technique for renewable energy outputs considering spatio-temporal correlation is first proposed. A Monte Carlo-based approach for evaluating the transmission congestion is proposed for identifying the potential locations of energy storage installation. Finally, an optimal configuration method of energy storage based on stochastic programming is proposed for alleviating transmission congestion. Numerical experiments are carried out on a modified IEEE-RTS 24-bus system and a practical 129-bus system. Numerical results show that energy storage can improve the flexibility of power system operation and the utilization of renewable energy generation. Especially, in the practical 129-bus system, the proposed method reduces renewable energy curtailment by 625.26 MWh and 444.43 MWh under fall and winter typical scenarios, respectively. And, investing some energy storage devices is more economical when the duration of transmission congestion is relatively short.

Research on peak load shifting for hybrid energy system with wind power and energy storage based on situation awareness

The uncertainty of wind power and load fluctuations can elevate the peaking pressure on the power grid and influence the optimization strategy for peak load shifting. Additionally, there is a need to explore the trade-off and dynamic adjustment between economic considerations and the effectiveness of peak load shifting strategies. In this paper, based on the situation awareness theory, an optimization model on peak load shifting is proposed for a hybrid energy system with wind power and energy storage unit. First, in the situation perception stage, simulations that incorporate stochastic volatility are executed for renewable energy outputs and electric loads, leading to the establishment of uncertainty models for both wind power and electric load. During the situation comprehension stage, an optimization model with multi-objective is proposed to simulate the hybrid system situation, in which the mutual constraints and games are considered between peak load shifting effects and system economy. Then, in the stage of situation presentation and situation orientation, by setting quantitative indicators to analyze and judge the system situation, the problem of changing uncertainty from the weights of various attributes in multi-objective decision-making is transformed into the prediction problem of the adjustable factor, and a hybrid network model is proposed to realize the interval prediction of the adjustable factor, further balancing the goals of load leveling and system economy. Example simulations demonstrate that the proposed optimization model for peak load shifting can effectively reduce the peak-valley difference ratio of the net load by over 39.08 %, thereby smoothing the overall net load fluctuation. In the face of uncertainty, the model shows minimal impact on the peak-valley difference ratio of the net load, experiencing only 3.91 % and 7.13 % changes in response to wind power and load fluctuations, respectively. These findings underscore the model's sensitivity to different levels of uncertainty in both wind power and load scenarios. The MAML-LSTM-FC model can clarify the selection range of the adjustable factor according to the requirements of system quantitative indexes, thereby achieving dynamic coordination between the effects of peak load shifting and economics.

Low-Carbon Economic Dispatch of Integrated Energy Systems in Multi-Form Energy-intensive Parks Based on the ICT-GRU Prediction Model

This paper presents a solution to the issues of redundancy and ambiguity in predicting variables associated with renewable energy output while aligning with the objectives of the “dual-carbon” energy strategy. A low-carbon economic dispatch method for multi-form energy-intensive parks is proposed, employing the ICT-GRU prediction model. Leveraging historical generation data, the ICT-GRU model enables accurate forecasting of renewable energy output. Subsequently, a comprehensive energy system model is developed considering the carbon emission characteristics and control features of park entities. The model aims to minimize operational costs and facilitate low-carbon economic dispatch. The effectiveness of the proposed method is demonstrated through a case study conducted in a multi-form energy-intensive load park integrated into a power grid. The results validate its capability to achieve low-carbon economic operation and provide valuable insights for grid dispatch optimization.

An Empirical Study on the Relationship Between Energy Transformation and Economic Growth – Based on Ramsey Model

The study analyzed the interactive relationship between energy transformation and economic growth through the Ramsey model. The energy factor was introduced as an independent factor into the Ramsey model and an application improvement design was carried out. From a micro perspective, the impact of energy and economic factors on net output was analyzed. The research results indicate that the estimated coefficients of fossil energy output elasticity, labor output elasticity, capital output elasticity, and renewable energy output elasticity are 0.740602, 0.708407, 0.454672, and 0.304065, respectively. In addition, the significance tests were all below 0.05, indicating significance. Meanwhile, the elasticity of capital output and labor output complement each other; The output elasticity of fossil fuels and renewable energy are also complementary to each other. In a complementary relationship, the downward trend of one element is often accompanied by the upward trend of another element. This study sorts out the interactive relationship between energy transformation and economic growth, pointing out the direction for the future development of China’s energy economy.

A novel demand response-based distributed multi-energy system optimal operation framework for data centers

To address the increasing energy demands in data centers, distributed multi-energy system (DMES) can provide an efficient solution. This study proposes a DMES coupled by photovoltaics, wind turbines, ground source heat pump, internal combustion engines, electric chillers, absorption chillers, and energy storage device. The optimal scheduling of DMES with source-load interaction plays a crucial role in improving the performance of data centers. To achieve this, an integrated demand response (IDR) strategy is established, considering time-of-use pricing and renewable energy peak subsidies. Then, a clean and economical optimal operation model for DMES based on proposed IDR strategy and carbon trading mechanisms is established. Furthermore, to enhance the robustness of scheduling, this study analyzes the impacts of uncertainties in renewable energy output and demand response on DMES operation using the Monte Carlo method and interval method. Case study results demonstrate that the proposed IDR strategy increases electricity and cooling loads while reducing heat loads during the peak period of renewable energy generation, compared to the traditional IDR strategy. The proposed scheduling model, which incorporates the proposed IDR strategy and carbon trading mechanism, improves data center performance by reducing total costs by 1.85%, carbon emissions by 0.56%, electricity purchases by 2.93%, while increasing renewable energy utilization by 0.77%. The results also highlight the critical role of energy storage devices, with an optimal initial charge state is 0.5. Additionally, optimization considering multiple uncertainties, results in higher total costs and broader interval widths compared to single-factor uncertainty optimization, but reduces system operational risk.

Neural network-assisted integration of renewable sources in microgrids: A case study

This study examines the incorporation of renewable energy sources into microgrids using neural network-assisted optimization methods. The objective is to tackle the difficulties related to the fluctuation and uncertainty of renewable energy production. An examination of the collected data over various time periods indicates encouraging patterns in the production of renewable energy. The solar energy use shows a steady rise from 120 kWh to 140 kWh, representing a 16.67% increase. Similarly, wind energy usage also demonstrates an upward trend, increasing from 80 kWh to 95 kWh, marking an 18.75% expansion. The biomass energy production has seen a substantial increase from 50 kWh to 65 kWh, representing a significant 30% rise. The examination of microgrid load consumption demonstrates the increasing energy needs in residential, commercial, and industrial sectors. The household load consumption has increased from 150 kWh to 165 kWh, representing a 10% spike. Additionally, the commercial load and industrial load have also seen a surge of 15%. The predictions made by the neural network demonstrate a high level of accuracy, closely matching the actual output of renewable energy. The accuracy rates for solar, wind, and biomass projections are 98.4%, 95.5%, and 97.3% correspondingly. The assessment of improved energy distribution emphasizes the effective usage of renewable sources, guaranteeing grid stability and optimal resource utilization. The results highlight the capacity of neural network-assisted methods to precisely predict renewable energy outputs and efficiently incorporate them into microgrids, hence promoting sustainable and resilient energy solutions. This report provides valuable insights on improving microgrid operations, decreasing reliance on traditional energy sources, and accelerating the shift towards sustainable energy systems.

Optimizing Smart Grids with Advanced AI Algorithms for Real-time Energy Management

Using optimization techniques based on neural networks, this study explores how microgrids might integrate renewable energy sources. Dealing with problems caused by the uncertainty and unpredictability of renewable energy generation is the primary goal. Renewable energy generation has been showing encouraging trends, according to data analysis spanning many time periods. From 120 kWh to 140 kWh, there was a steady rise of 16.67% in solar energy utilization. Also, there was an 18.75% rise, from 80 kWh to 95 kWh, in the use of wind power. There was a 30% rise, from 50 kWh to 65 kWh, in the output of biomass energy. Microgrid load utilization analysis shows rising energy demands in commercial, industrial, and residential areas. Commercial and industrial loads climbed by 15% and 10%, respectively, while residential energy use increased by 10%, from 150 kWh to 165 kWh. With solar predictions at 98.4%, wind predictions at 95.5%, and biomass predictions at 97.3%, predictions made using neural networks were highly congruent with actual output of renewable energy.

Voltage control method for multi-energy system based on the coupling of renewable energy hydrogen production and storage

Renewable energy power generation combined with hydrogen storage, is an important way to realize the deep integration of hydrogen energy and renewable energy in multi-energy systems. In the multi-energy system of renewable energy power generation and hydrogen production in AC/DC hybrid. Due to the fluctuation of heat load and electric hydrogen production load in the system and the multi-time scale characteristics of energy flow transmission of heat storage and hydrogen storage. It will lead to the difficulty of power coordination in the whole Multi Energy System (MES). In order to solve the problem of power imbalance in MES, this paper first establishes the hybrid power flow equation of MES cluster composed of AC/DC and hydrogen energy transmission channels. The reactive voltage fluctuation characteristics of the AC/DC system under the fluctuation of energy exchange, system energy conversion, and renewable energy output of the MES cluster interconnection channel are analyzed. Secondly, a multi-energy flow model of the multi-energy system is established. The main factors affecting the voltage-reactive power of the AC system are analyzed. A reactive power model based on electro-thermal conversion, hydrogen storage, and multi-energy transmission channels is established. Then, a generation algorithm of the safe and stable boundary of pipeline pressure is proposed to maintain the pressure of hydrogen production and storage system within a safe range. The robust optimization control method is used to control the coupling of multi-energy sources. Finally, based on the actual operation data of the multi-energy power system. The simulation model of reactive power dynamic optimization control for multi-energy interconnected systems is established. The simulation results show that the control method proposed in this paper is adopted. Through the coordinated distribution of energy among multi-energy systems, the voltage stability level of the system can be effectively improved.

An AI-Based System for Predicting Renewable Energy Power Output Using Advanced Optimization Algorithms

Accurate generation forecasting of Renewable Energy Sources (RES) is becoming more and more crucial for effective grid operation and energy management as RES are incorporated into the electrical grid. Because Machine Learning (ML) and Deep Learning (DL) algorithms can learn complicated relationships from data and provide accurate forecasts, they have become more popular than traditional forecasting approaches, which have limits. This article examines the state of the art and future directions in the field of ML and DL-based forecasting of renewable energy generation. This paper reviews the several approaches and models that have been used to project renewable energy. It also highlights the challenges, such as managing the uncertainty and unpredictability of renewable energy output, data accessibility, and model interpret ability. To sum up, this study emphasizes how important it is to develop accurate and dependable renewable energy forecasting models to facilitate the future transition to sustainable energy sources and enable the integration of RES into the electrical grid.