Large Amount Of Renewable Energy Research Articles

The refinery of the future.

Fossil fuels-coal, oil and gas-supply most of the world's energy and also form the basis of many products essential for everyday life. Their use is the largest contributor to the carbon dioxide emissions that drive global climate change, prompting joint efforts to find renewable alternatives that might enable a carbon-neutral society by as early as 2050. There are clear paths for renewable electricity to replace fossil-fuel-based energy, but the transport fuels and chemicals produced in oil refineries will still be needed. We can attempt to close the carbon cycle associated with their use by electrifying refinery processes and by changing the raw materials that go into a refinery from fossils fuels to carbon dioxidefor making hydrocarbon fuels and to agricultural and municipal waste for making chemicals and polymers. We argue that, with sufficient long-term commitment and support, the science and technology for such a completely fossil-free refinery, delivering the products required after 2050 (less fuels, more chemicals), could be developed. This future refinery will require substantially larger areas and greater mineral resources than is the case at present and critically depends on the capacity to generate large amounts of renewable energy for hydrogen production and carbon dioxide capture.

Distributed Dynamic State Estimation of Power Systems Considering Dynamic State Constraints based on Phasor Measurements Units

Background: Dynamic state estimation can provide detailed information about power systems. However, there is no clear, dynamic equation for the state transition of bus voltages in power systems. Currently, smoothing methods based on historical data are commonly used, but they cannot ensure accurate state prediction in power systems with a large amount of renewable energy. Moreover, the fast sampling rate of phasor measurement units generates a vast amount of real-time data for the dispatch center, making it challenging for centralized state estimation to meet real-time demands. Objective: This paper proposes a distributed power system state estimation considering dynamic state constraints to address the above issues. Methods: By incorporating the constraints between the dynamic states of the system dynamic components and the bus voltage phasors, the state transition equations for bus voltage phasors are constructed based on the predicted dynamic states and the nodal injection power equations. This allows taking the dynamic model constraints into account when predicting bus voltage phasors. Then, based on the principle of hierarchical coordination and distributed state estimation, the method of estimation-coordination-correction is adopted to acquire system state information quickly and accurately. method: By incorporating the constraints between the dynamic states of the system dynamic components and the bus voltage phasors, the state transition equations for bus voltage phasors are constructed based on the predicted dynamic states and the nodal injection power equations. This allows taking the dynamic model constraints into account when predicting bus voltage phasors. Then, based on the principle of hierarchical coordination and distributed state estimation, the method of estimation-coordination-correction is adopted to acquire system state information quickly and accurately. Results: Furthermore, an IEEE 9-bus system and an IEEE 39-bus system are used to validate the proposed method. The proposed method is compared with other algorithms to prove its superiority. result: Furthermore, an IEEE 9-bus system and an IEEE 39-bus system are used to validate the proposed method. And the proposed method is compared with other algorithms to prove its superiority. Conclusion: The simulation results show that the proposed method can effectively improve the accuracy of the state estimation results of power systems under dynamic conditions. conclusion: The simulation results show that the proposed method can effectively improve the accuracy of the state estimation results of power systems under dynamic conditions. other: None

Multiagent deep meta reinforcement learning for sea computing-based energy management of interconnected grids considering renewable energy sources in sustainable cities

In a sustainable city with a large amount of renewable energy, the difficulty inherent in the coordination of the load frequency control strategies of grid units and area units lead to severe frequency fluctuations. Conventional load frequency control has difficulty overcoming the above problems due to its lack of adaptive control ability and robustness. To overcome this problem, this paper proposes a sea computing-based grid-area coordinated load frequency control (SCGAC-LFC) method, which equates each grid unit and area unit in each area as independent agents. Instead of different units relying on different strategies, all units collectively obtain the LFC policy that suits the market requirements. In its online application, no communication is needed since each unit can arrive at its own decision. In addition, this paper proposes a curriculum multiagent deep meta-actor-critic (CMA-DMAC) algorithm, which introduces meta-reinforcement learning and curriculum learning to guide the agent training to improve the robustness and quality of the obtained SCGAC-LFC strategies. Using a simulation of the four-area LFC model for the China Southern Grid (CSG), our proposed method carries significantly lower frequency error and regulation mileage payment.

Meta-learning based voltage control strategy for emergency faults of active distribution networks

With the increase of energy demand and the continuous development of renewable energy technology, active distribution networks have become increasingly important. However, the introduction of a large amount of renewable energy has made the structure of ADN increasingly complex and fragile, and emergency fault caused by emergencies may often occur. Voltage control in the emergency fault event is particularly important. In this context, this paper presents a meta-learning based voltage control strategy for renewable energy integrated active distribution network. A general regression neural network is first applied to extract features from the operation data. Then, the local cross-channel interaction network is adopted to capture targeted information that is most related to emergency fault from the features and induce knowledge transfer to update the voltage control strategy. This allows the proposed strategy to make optimal decisions quickly when only limited data are available under an emergency fault that has never occurred. Comparison results based on a 69-bus distribution network validate the effectiveness and robustness of the proposed strategy.

Hierarchical energy optimization of flywheel energy storage array systems for wind farms based on deep reinforcement learning

Due to the volatility and intermittency of renewable energy, injecting large amounts of renewable energy into the grid will have a tremendous impact on the stability and security of the network. In this paper, we propose the hierarchical energy optimization of flywheel energy storage array system (FESAS) applied to smooth the power output of wind farms to realize source-grid-storage intelligent dispatching. The energy dispatching problem of the FESAS is described as a Markov decision process by the actor-critic (AC) algorithm. In order to solve the problems of stability and low sampling efficiency of the AC algorithm, the soft actor-critic (SAC) algorithm, a deep reinforcement learning (DRL) algorithm based on the model-free off-policy method of the maximum entropy framework, is adopted. Furthermore, SAC and prioritized experience replay (PER) are utilized to greatly improve learning efficiency and sample utilization. The experimental results show that SAC-PER has better performance and stability in energy optimization of the FESAS.

Decision-making model of sustainable ecological environment management based on enterprise environmental management under a low-carbon economy

With the development of industrialization, ecological and environmental problems have become increasingly prominent. In addition, the direct burning of crop straw and livestock manure also causes serious damage to the ecological environment. From the perspective of low-carbon economy, in order to promote the sustainable development of the ecological environment, this paper puts forward the idea of integrating the environmental management of enterprises, establishes the decision-making model of ecological environment management, and carries out related research. The experimental results show that the average error of cross-section water quality prediction is 2, the average error of pollutant discharge prediction is 2.1, and the highest ecological security degree is 1.28. The data show that the system dynamics method can effectively reduce the section water quality prediction error and pollutant discharge prediction error of the management decision-making model, and improve the accuracy of the model prediction. In the analysis experiment of the biomass energy potential of agricultural waste, livestock manure and straw waste accounted for 59.26% and 30.18% of the total, respectively. It can be seen that agricultural waste has a high content of convertible biomass energy, which can provide a large amount of renewable energy for human beings.

The influence of the first filling period length and reservoir level depth on the operation of underground hydrogen storage in a deep aquifer

Underground storage is a method of storing large amounts of renewable energy that can be converted into hydrogen. One of the fundamental problems associated with this process concerns determining the timing and amount of injected gas in the first filling period for the operation of an underground storage facility. Ascertaining the hydrogen flow rate is essential to ensure that the capillary and fracturing pressures are not exceeded. The value of the flow rate was assessed by modelling the injection of hydrogen into a deep aquifer. The best initial H2 injection period was found to be five months. The volume of the cushion gas and the total storage capacity expanded with the extension of the first filling period length. The working capacity grew as the depth increased, reaching maximum values at depths of approximately 1200–1400 m. This depth was considered optimal for storing hydrogen in the analysed structure.

Two-Stage Scheduling Strategy for Integrated Energy Systems Considering Renewable Energy Consumption

With the crisis of energy and environment, the integrated energy systems (IES) have a bright prospect in future energy reform owing to the excellent economic and environmental performance. The IES combines a large amount of renewable energy (RE), which guarantees its economic and environmental benefits. Many uncertainties of RE threaten the performance of IES. How to promote RE consumption under strong uncertainties is a crucial problem in the scheduling of IES. In this work, a two-stage scheduling strategy for IES combining day-ahead scheduling and real-time scheduling is proposed to guide the system operation. The stage of day-ahead scheduling can obtain the optimal scheduling scheme one day in advance based on the forecast data of RE, and the stage of real-time scheduling is introduced to cope with the uncertainties of RE. The model of IES is established based on the energy hub (EH), and the entire strategy is implemented on this model. The model integrates various energy conversion equipment to give full play to the advantages of coordination and complementation of IES. The improved particle swarm optimization (IPSO) is proposed as the solution algorithm of the whole strategy, which improves the traditional PSO through random nonlinearity change inertia weight strategy and best solution perturbation operator (BSPO). Compared with the traditional PSO, IPSO has more excellent performance for solving IES scheduling model. Finally, different schemes under different situations are compared.

Research on optimal scheduling decision of multi‐microgrids based on cloud energy storage

In microgrids, energy storage systems (ESS) can mitigate the intermittency and uncertainty associated with large amounts of renewable energy connected to the grid. Cloud energy storage (CES) is a solution proposed by scholars over the last two years. It provides energy storage and trading services that can alleviate high investment costs and small-scale wind and light abandonment problems. However, large-scale energy mutualisation and fine scheduling of energy become significant challenges for further development. This study proposes a CES scheduling architecture based on multi-microgrids. CES extends the scope of power trading from a single microgrid to multi-microgrids, with the microgrid capable of interacting with neighbouring microgrids for power and reducing the transmission pressure on the larger grid. In addition, this study defines the game strategy for power trading between cloud storage operators, considering the reduction in electricity costs due to dishonest behaviour by users. The simulation results indicate that the proposed dispatching architecture and strategy can effectively improve the consumption rate of new energy sources, mitigate grid fluctuations, and further reduce electricity costs for users while also being resilient to dishonest user behaviour.

Electric Power Network Interconnection: A Review on Current Status, Future Prospects and Research Direction

An interconnection of electric power networks enables decarbonization of the electricity system by harnessing and sharing large amounts of renewable energy. The highest potential renewable energy areas are often far from load centers, integrated through long-distance transmission interconnections. The transmission interconnection mitigates the variability of renewable energy sources by importing and exporting electricity between neighbouring regions. This paper presents an overview of regional and global energy consumption trends by use of fuel. A large power grid interconnection, including renewable energy and its integration into the utility grid, and globally existing large power grid interconnections are also presented. The technologies used for power grid interconnections include HVAC, HVDC (including LCC, VSC comprising of MMC-VSC, HVDC light), VFT, and newly proposed FASAL are discussed with their potential projects. Future trends of grid interconnection, including clean energy initiatives and developments, UHV AC and DC transmission systems, and smart grid developments, are presented in detail. A review of regional and global initiatives in the context of a sustainable future by implementing electric energy interconnections is presented. It presents the associated challenges and benefits of globally interconnected power grids and intercontinental interconnectors. Finally, in this paper, research directions in clean and sustainable energy, smart grid, UHV transmission systems that facilitate the global future grid interconnection goal are addressed.

Optimization of a Rolling Triboelectric Nanogenerator Based on the Nano-Micro Structure for Ocean Environmental Monitoring.

The serious environmental pollution and energy crisis have become a global issue, which makes it a pressing task to develop sustainable and clean energy sources. There exists a large amount of renewable energy in the ocean; unfortunately, most resources are underutilized. In this work, we demonstrate a performance-enhancing rolling triboelectric nanogenerator (TENG) based on nano–micro-structured polytetrafluoroethylene (PTFE) films. The nano–micro structure on the PTFE surface can increase the effective contact area and enhance the triboelectric effect, which is beneficial to improve the output performance. As a result, the output voltage and output current are 25.1 V and 7.3 μA, respectively. We further investigate the effect of nano–micro PTFE concentration on the output performance. The TENG based on a 45% concentration of nano–micro PTFE presents the maximum output power. Furthermore, this TENG can effectively harvest water wave energy with various amplitudes and frequencies, which has the potential to harvest ocean energy for environmental monitoring.

A Novel Approach for the Power Ramping Metrics

One of the biggest concerns associated with incorporating a large amount of renewable energy into power systems is the need to cope with significant ramps in renewable power output. Power system operators need to have statistical information on the power ramping features of renewable generation, load, and net-load that can be used to mitigate ramping events in the case of a large forecast error to ensure the power system's flexibility and reliability; on the other hand, for economic considerations. So far, there is no consensus on a precise definition for the ramp event and so far there are hardly any metrics describing the ramping features of a power system. The paper introduces new metrics describing the power ramping features in a power system. The new metrics are ramp regularity factor (RRF), ramp intensity factor (RIF), and maximum ramp ratio (MRR). In addition, the coefficient of variation (CV) is used to characterize the average value of power ramps. The new ramp metrics are applied to the output power of Belgium's aggregated wind farms in 2017 and 2018. The results obtained by comparing the two years demonstrate that the two years have the same ramping behavior, although the average installed wind capacity has been increased. The new metrics can also be applied to other renewable sources (PV, tidal power, etc.), load, and net-load at any stage of operation.

Research on Probabilistic Power Flow Calculation of DC Distribution Network

With the development of distributed power sources based on wind power and photovoltaics and the increase of DC loads, DC power grids have attracted more and more attention. Compared with the AC distribution network, the DC distribution network can save a lot of commutation links while absorbing a large amount of renewable energy and DC load, reducing losses and improving economic benefits. This paper combines the probabilistic power flow calculation method based on the three-point estimation method and the Nataf inverse transform used in the AC power grid with the DC power flow model, and proposes a method suitable for the probabilistic power flow calculation of the DC distribution network. Comparing the results and analyzing the in fluence of the load distortion rate and the increase in the capacity of new energy absorbed on the calculation error of the estimation method, it demonstrates the accuracy and effectiveness of the calculation method in this paper.

Optimal Design and Analysis of Sector-Coupled Energy System in Northeast Japan

As for research on sector-coupled energy systems, few studies comprehensively deal with energy carriers and energy demand sectors. Moreover, few studies have analyzed energy conversion functions such as Power-to-Gas, Power-to-Heat, and Vehicle-to-Grid on the energy system performance. This study clarifies the required renewable resources and costs in the sector-coupled energy system and cost-optimal installed capacity and operation. We formulated an optimization model considering sector coupling and conducted a case study applying the model in the Tohoku region. As a result, due to sector coupling, the total primary energy supply (TPES) is expected to decrease, and system costs are expected to increase from 1.8 to 2.4 times the current level. System costs were minimized when maximizing the use of V2G by electric vehicles and district heating systems (DHS). From the hourly analysis, it becomes clear that the peak cut effect by Power-to-Heat and the peak shift effect by Vehicle-to-Grid result in leveling the output of electrolyzer and fuel synthesizer, which improves the capacity factor reducing capacity addition. Since a large amount of renewable energy is required to realize the designed energy system, it is necessary to reduce the energy demand mainly in the industrial sector. Besides, in order to reduce costs, it is required to utilize electric vehicles by V2G and provide policy support for district heating systems in Japan.

Static Stability Analysis Based on Probabilistic Power Flow Calculation considering P2G Technology

At present, integrated energy systems have received extensive attention, but there is no basic framework for stability analysis of coupled systems. The injection of a large amount of renewable energy also has a great impact on the stability of the system. This paper focuses on how to analyze the static stability of the coupling system with uncertainty, which mainly considers the uncertainty of wind power generation and photovoltaic power generation and also considers the influence of P2G technology on the whole system. Firstly, this paper analyzes the principles of wind power generation and photovoltaic power generation and constructs the probability model of renewable energy power generation power. Then, the three-point estimation method is used to process the data, and the probability distribution of the unknown quantity is obtained by probabilistic power flow analysis. Finally, the probability distribution of each eigenvalue is obtained by analyzing the sensitivity of the characteristic roots to the voltage. Thus, the static stability of the system is judged. The applicability of proposed methodology is demonstrated by analyzing an integrated IEEE 14-bus power system and a Belgian 20-node gas system in this paper.

Thermoeconomic evaluation of double-reheat coal-fired power units with carbon capture and storage and waste heat recovery using organic Rankine cycle

Coal-fired power generation units using carbon capture and storage (CCS) can reduce CO2 emissions significantly whereas CCS consumes large amounts of renewable energy, leading to an efficiency penalty. To decrease the steam extraction and promote energy utilization efficiency, this study employs organic Rankine cycle (ORC) to recover the waste heat of reboiler condensate and utilizes the waste heat in CO2 compressed processes to provide energy for carbon capture system. Three technical scenarios are proposed: (1) Coal-fired carbon capture power generation system: the medium-pressure cylinder exhaust is throttled by an auxiliary turbine and exchanges heat with H6 heater to provide energy for a reboiler; (2) Coal-fired carbon capture power generation system using ORC to recover the residual heat of reboiler condensate; (3) Solar-aided coal-fired power generation system using ORC and CCS: waste heat in CO2 compression processes is supplied for a reboiler and ORC recovers the residual heat of reboiler condensate. The energy balance and overall performance of different scenarios are investigated with EBSILON, and the parameter sensitivity of scenario III is conducted. Results reveal that all scenarios improve the thermal performance of the system, and the thermo-economics of scenario III is the best at the heat ratio of 0.4.

Demystifying market clearing and price setting effects in low-carbon energy systems

Abstract The pursuit of climate stabilisation scenarios requires large-scale decarbonisation of power, heat, industry, and transport sectors. Cost-efficient climate neutrality strategies will primarily involve direct electrification of final energy demands, based on variable production from wind and solar PV as the primary source of renewable energy. Cross-sectoral integration is going to increase traditional electricity demands and is also expected to contribute with valuable flexibility in future electricity markets, particularly through bi- and multivalent technologies. However, the nature of market clearing and price formation effects in wholesale electricity markets with large amounts of renewable energy and significant cross-sectoral integration remains unknown. In this paper, we show and quantify that cross-sectoral demand bidding is going to be crucial for the price formation in future electricity markets. We found in a large-scale cross-sectoral capacity expansion and long-term cross-border transmission expansion study for the future integrated European system that bi- and multivalent cross-sectoral electricity consumers will be making market bids based on their valid opportunity costs. These price-setting technologies mainly include direct resistive and heat pump heating units. Unlike previous work, we can show and quantify that there will likely not be too many zero or even negative price situations due to excess renewable production in the system since the additional cross-sectoral consumers will, depending on their actual flexibility potential, use low-carbon electricity to supply final energy sector demands. Our results demonstrate how important it is to adequately capture cross-sectoral interactions and a variety of technology combinations in a high spatio-temporal solution when analysing future market clearing and electricity price formation effects in low-carbon energy systems. We anticipate our contribution to be a starting point for more sophisticated models incorporating detailed representations of cross-sectoral flexibility to investigate low-carbon energy markets further.

Role of optimal transmission switching in accommodating renewable energy in deep peak regulation-enabled power systems

Due to the shortage of fossil energy and the pollution caused by combustion of fossil fuels, the proportion of renewable energy in power systems is gradually increasing across the world. Accordingly, the capacity of power systems to accommodate renewable energy must be improved. However, integration of a large amount of renewable energy into power grids may result in network congestion. Hence, in this study, optimal transmission switching (OTS) is considered as an important method of accommodating renewable energy. It is incorporated into the operation of a power grid along with deep peak regulation of thermal power units, forming an interactive mode of coordinated operation of source and network. A stochastic unit commitment model considering deep peak regulation and OTS is established, and the role of OTS in promoting the accommodation of renewable energy is analyzed quantitatively. The results of case studies involving the IEEE 30-bus system demonstrate that OTS can enable utilization of the potential of deep peak regulation and facilitate the accommodation of renewable energy.

Dynamic simulation of gas turbines via feature similarity-based transfer learning

Since gas turbine plays a key role in electricity power generating, the requirements on the safety and reliability of this classical thermal system are becoming gradually strict. With a large amount of renewable energy being integrated into the power grid, the request of deep peak load regulation for satisfying the varying demand of users and maintaining the stability of the whole power grid leads to more unstable working conditions of gas turbines. The startup, shutdown, and load fluctuation are dominating the operating condition of gas turbines. Hence simulating and analyzing the dynamic behavior of the engines under such instable working conditions are important in improving their design, operation, and maintenance. However, conventional dynamic simulation methods based on the physic differential equations is unable to tackle the uncertainty and noise when faced with variant real-world operations. Although data-driven simulating methods, to some extent, can mitigate the problem, it is impossible to perform simulations with insufficient data. To tackle the issue, a novel transfer learning framework is proposed to transfer the knowledge from the physics equation domain to the real-world application domain to compensate for the lack of data. A strong dynamic operating data set with steep slope signals is created based on physics equations and then a feature similarity-based learning model with an encoder and a decoder is built and trained to achieve feature adaptive knowledge transferring. The simulation accuracy is significantly increased by 24.6% and the predicting error reduced by 63.6% compared with the baseline model. Moreover, compared with the other classical transfer learning modes, the method proposed has the best simulating performance on field testing data set. Furthermore, the effect study on the hyper parameters indicates that the method proposed is able to adaptively balance the weight of learning knowledge from the physical theory domain or from the real-world operation domain.

Application of random matrix model in multiple abnormal sources detection and location based on PMU monitoring data in distribution network

With the conversion of the global power economy and energy structure, access to a large amount of renewable energy has led to a decrease in power system inertia. The slight abnormal disturbance in the distribution network may have a significant impact on social and economic development. Aim at enhancing power stability and system resiliency; this study focuses on the detection and location of multiple abnormal sources in the distribution network. Most traditional methods use models relying on precise line parameters, subject to poor adaptability to the distribution network with a large number of nodes, and rapidly changing topology. Therefore, this study proposes a novel random matrix model, driven by monitoring data from phasor measurement units distributed on the overhead transmission lines. In this model, linear shrinkage (LS) theory, and Marchenko–Pastur law are combined for noise reduction to ensure the dynamic character and anti-noise ability. Moreover, data dimensions and sample points may be at the same level in an extensive scale network. The LS and standard condition number rule (SCN) are used for estimating the number of abnormal sources. Finally, the effectiveness of this paper's model is verified in PSCAD. The results indicate that the method has specific dynamic performance and anti-noise ability.