Utilization Rate Of Renewable Energy Research Articles

Ruthenium-based metal oxide for acidic oxygen evolution reaction: advances and challenges

Utilizing proton exchange membrane water electrolyzers (PEMWE) to produce hydrogen is a promising way to provide clean energy, reduce carbon emissions and improve the utilization rate of renewable energy. The slow kinetics of oxygen evolution reaction (OER) at the anode is considered to be the crucial obstacle to its conversion efficiency. Due to the strong acidic and oxidizing environment of OER reaction, it is essential to develop electrocatalyst with high activity and high stability to reduce the high kinetic barrier of OER and improve the reaction rate. Among them, Ru-based catalysts have outstanding catalytic activity and relatively low price, which has aroused great interest in its practical application. Many Ru-based catalysts with superior performance have been developed in recent years. In this review, we mainly focus on the development of Ru-based catalysts towards OER, with OER mechanisms discussed first, followed by discussion in the factors affecting the catalytic performance, and then introduce current research progress of Ru-based catalysts under acidic conditions. We finally conclude the prospect of the development direction of Ru-based catalysts in the future, hopefully guiding further development of Ru-based catalysts towards real world operation.

Optimal Configuration of Self-Consistent Microgrid System with Hydrogen Energy Storage for Highway Service Area

Targeting on the goal of carbon peaking and carbon neutralization, the transportation sector is facing the pressure of carbon reduction, emission reduction and energy transformation simultaneously. Building a green, flexible, self-consistent and sustainable road transportation energy integration system has become an inevitable trend for the future development in an area without power grid. This paper proposes a self-consistent micro grid system model for wind and solar power with hydrogen energy storage for a highway service area without power grid connection. On this basis, a micro grid optimal configuration model is proposed with the goal of minimizing the comprehensive cost of the micro grid in the service area, under the constraints of the battery, hydrogen energy storage system (ESS), and the power balance of the micro grid system. Particle Swarm Optimization (PSO) algorithm is employed to solve the problem for a case study to verify the effectiveness of the model and the method proposed. Experimental results show that the model proposed in this paper not only improves the renewable energy utilization rate of the system, but also improves the reliability of power supply, verifying the effectiveness of the micro grid configuration scheme for the highway service area containing hydrogen ESS.

Research Progress of Fixed Bed Dehumidification System: A Mini Review

Abstract: In recent years, the fixed bed dehumidification system has received extensive attention. Research shows that the fixed bed can be combined with the vapor compression refrigeration system to replace the traditional air conditioning (A/C) system, improve the dehumidification efficiency of the system and reduce energy consumption of A/C. In this study, the optimization and improvement methods of the fixed bed dehumidifier in recent years are introduced in detail firstly. It points out that adding heating/cooling devices to the fixed bed can greatly improve the dehumidification capacity and efficiency of the system, which is the main method to improve the fixed bed currently; Optimizing the structure of fixed bed is also an effective method, but the current research results have limited improvement on dehumidification performance; Optimizing the regeneration mode of fixed bed can improve the utilization rate of renewable energy and improving the overall dehumidification efficiency of the system, but the research in the direction is still insufficient; The parallel double packed bed structure can ensure the continuous operation of the fixed bed to improve the dehumidification efficiency of the system, but such systems are large in scale and high in cost. Then the dehumidification performances of various systems are summarized and comprehensively compared. Finally, the future optimization and improvement direction of the fixed bed is pointed out.

User multi-scenario energy storage configuration and operation strategy

With the transformation of China’s energy consumption side, users’ energy demand will become more diversified and comprehensive, and the coupling degree of energy consumption systems such as cold, heat, and electricity will deepen day by day. Among them, the energy storage system will play an important role in comprehensive energy service. Using energy storage equipment on the users’ side can significantly improve the utilization rate of renewable energy, realize peak shaving and valley filling, and thus reduce the energy cost of the system. User-side residential and commercial scenarios have different load characteristics and face different energy storage configuration requirements. Therefore, this paper studies the operation optimization strategy of multi-scenario energy storage configuration on the user side, studies the definition and constraints of the state of charge of electric energy storage for residential areas, and establishes the configuration and operation model of electric energy storage.

The Energy Management of Multiport Energy Router in Smart Home

Although smart home has received wide attention in recent years, numerous scholars focus more on energy optimization strategy than energy dispatch hardware device (named energy router). Meanwhile, this energy router should have several features, i.e., high renewable energy utilization, energy multi-port and low volume. Thus, this paper designs a nine-port energy router regarding smart home and proposes a multimode hierarchical management strategy for this energy router. First, for the multi-port demand of wind, solar, storage and utilization, this paper presents a nine-port energy router to improve the renewable energy consumption and power supply flexibility. In addition, to reduce the volume of the energy router, a non-isolated AC/DC hybrid topology is constructed through embedding the integrated power electronic converters, which achieves the miniaturization of the energy router. In order to improve the renewable energy utilization rate, the decentralized module control is proposed for the components of energy router to provide the voltage and frequency support for system, and realizes the power sharing of distributed generations (DGs). Furthermore, the power exchange control with three-mode switching is proposed to guarantee the global energy flow balance under complex conditions. Eventually, the feasibility of the energy router is verified by the simulation and experiments.

The optimization research on the coupled of active and passive energy supplying in public institutions in China

The building sector is one of the largest energy user and carbon emitters globally. To increase the utilization rate of renewable energy and reduce carbon dioxide emissions, the optimal technical scheme of active public institutions and coupled utilization of renewable energy is studied. In this study, the energy consumption of three types of public institutions in various regions of China was simulated by using DeST building energy consumption software, combined with energy conversion efficiency and data released by the National Bureau of Statistics, and the total energy demand and total energy supply of public institutions were predicted using the load density method. Based on the coupling mechanism of the MARKAL model, the optimal proportion of renewable energy in the energy supply of public buildings in different regions is determined. Through the study of the number of public institutions in various regions of China, energy consumption characteristics, construction area, and other related data, the reverse energy flow method is creatively proposed, and the active and renewable energy coupling algorithm from the energy demand side of public institutions to the energy supply side is established. The results show that the central region has the highest utilization rate of renewable energy in the public sector, reaching 36.18%. The use of renewable energy in public buildings in hot summer and warm winter zones decreased to 35.08%, and it was 12.82% in cold zones. By 2025, the proportion of renewable energy resources in China is expected to reach 29.2%. The energy coupling model and algorithm constructed in this paper can provide a basis for the coupling macro configuration of renewable energy in public institutions in China.

A Capacity Optimization Method for a Hybrid Energy Storage Microgrid System Based on an Augmented ε- Constraint Method

In general, microgrids have a high renewable energy abandonment rate and high grid construction and operation costs. To improve the microgrid renewable energy utilization rate, the economic advantages, and environmental safety of power grid operation, we propose a hybrid energy storage capacity optimization method for a wind–solar–diesel grid-connected microgrid system, based on an augmented ε- constraint method. First, the battery is coupled with a seasonal hydrogen energy storage system to establish a hybrid energy storage model that avoids the shortcomings of traditional microgrid systems, such as a single energy storage mode and a small capacity. Second, by considering the comprehensive cost and carbon emissions of the power grid within the planning period as the objective function, the abandonment rate of renewable energy as the evaluation index, and the electric energy storage and seasonal hydrogen energy storage system operating conditions as the main constraints, the capacity allocation model of the microgrid can be constructed. Finally, an augmented ε- constraint method is implemented to optimize the model above; the entropy–TOPSIS method is used to select the configuration scheme. By comparative analysis, the results show that the optimization method can effectively improve the local absorption rate of wind and solar radiation, and significantly reduce the carbon emissions of microgrids.

Renewable portfolio standard: When does it play a positive role?

This study focuses on investigating the interactions between an energy supplier and a grid operator under the renewable portfolio standard (RPS) policy. The energy supplier produces both green power and thermal power for the grid operator, who in turn is obliged to provide each type of power to the end consumers. The cost of generating green power is faced with a learning-by-doing effect. Several interesting results are obtained. First, the change trend of the electricity supplier's and grid operator's profits with the quota obligation is non-monotonic. Second, interestingly, with the increase of quota obligation, green power consumption may decrease and thereby increase carbon emissions. Third, both the grid operator's profit and the green power consumption levels can be increased through a higher operation cost. Our findings reveal the importance of considering the grid operator and learning-by-doing effect when improving the utilization rate of renewable energy.

An optimal dispatch model for virtual power plant that incorporates carbon trading and green certificate trading

The grid connection of large-scale clean energy provides the possibility for the establishment of a clean energy system. The urgent problem that needs to be solved is how to improve the utilization efficiency of clean energy to reduce carbon emissions. First, the virtual power plant (VPP) operation mode under the carbon trading and green certificate trading mechanism is analyzed. Secondly, this paper incorporates carbon trading mechanism and green certificate trading mechanism into the optimal dispatch model of VPP including wind power generation, photovoltaic power generation, gas turbines and energy storage device. Taking the net profit of VPP as the optimization goal, which can take into account both economic and environmental protection. Based on whether VPP participates in carbon trading and green certificate trading, three schemes are established and compared and analyzed. In addition, to cope with the volatility of renewable energy, the utilization rates of the renewable energy output scenarios for four typical days under the three schemes were compared and analyzed. To solve this problem, this paper proposes the Self-Conclusion and Variational Particle Swarm Optimization (SCV-PSO) algorithm. The simulation results show that the VPP optimal scheduling model and solving algorithm proposed can effectively improve the utilization rate of renewable energy and reduce the carbon emission under the premise of ensuring economic efficiency. It can provide a useful reference for the low-carbon economic operation of the power system in the future.

A comprehensive model and its optimal dispatch of an integrated electrical-thermal system with multiple heat sources

As essential parts of the combined heat supply network with multiple heat sources, the electrical-heating and heat storage technology can simultaneously achieve the goal of clean heating and renewable energy accommodation. The paper introduces the integrated electrical-thermal system with multiple heat sources that consist of combined heat and power (CHP), coal-fired boiler (CB), and electrical boiler (EB) with thermal energy storage (TES). Considering the integration of electrical and thermal networks, we construct a comprehensive model that includes the electrical energy transmission and the overall heat transfer and storage processes based on the heat current method. Based on the comprehensive model, the optimal dispatch of the integrated system is discussed, and an iteration solving method is proposed, which can achieve acceptable convergence and solving efficiency. The simulation results show the combined operation of multiple heat sources has higher system operation efficiency but not necessarily a higher utilization rate of renewable energy. In the test system the combined operation of CHP and coal-fired boiler can reduce the coal consumption of the system by 2%. However, the wind utilization of the system decreases by 4.17%. Moreover, the introduction of an electrical boiler with a TES device can improve the system's flexibility and reduce wind curtailment, wherein the test system is 12.11% more wind consumption. Further, the heat load level can affect the operation efficiency of CHP, the combined operation of multiple heat sources can achieve the balance between reducing coal consumption and increasing renewable energy power generation.

A specially-designed test platform and method to study the operation performance of medium-depth geothermal heat pump systems (MD-GHPs) in newly-constructed project

Medium-depth geothermal heat pump systems (MD-GHPs) use deep borehole ground heat exchangers (DBHEs) to extract heat from the medium-depth geothermal energy with depth of 2–3 km. To study the local geothermal condition and operation performance of MD-GHPs, so as to guide the system design and operation control, a test platform and method are raised and applied in a newly-constructed project, where a DBHE with depth of 2750 m is constructed. Results show that the maximum heat extraction rate of DBHE reaches 450 kW with inlet and outlet water temperature of 5.0 ℃ and 18.9 ℃, and the maximum heat supply rate of heat pump reaches 600 kW with supply and return water temperature of 51.8 ℃ and 40.6 ℃ under continuous operation mode. Then the energy efficiency of heat pump (COP) can still remain higher than 3.60 with renewable energy utilization rate higher than 72.2 % in this maximum load condition. Besides, the DBHE still extracts heat from outside ground to store heat when the heat pump system shuts off, thus the transient maximum heat extraction rate could reach 600 kW or even higher under intermittent operation mode. Finally, the optimization direction of DBHE and heat pump systems are analyzed, so as to improve the overall operation performance of MD-GHPs and achieve better energy conservation effect.

Community decision-makers’ choice of multi-objective scheduling strategy for integrated energy considering multiple uncertainties and demand response

• Construct the RE output uncertainty model by the probability box theory. • Use complex networks to classify the community residents. • A CDR model is built to mobilize various users to join in IESs scheduling. • Propose a utility model for decision-makers based on the S-shaped utility function. • Select the second optimal strategy by Muirhead mean operator. To address the impact of uncertainty on scheduling strategy in community integrated energy systems, a multi-objective optimal scheduling model under the constraint of various uncertainties and demand response is suggested. Firstly, a source-load uncertainty model and a comprehensive demand response model are built. Secondly, construct a multi-objective satisfaction model and utility model based on the energy supplier profit, resident cost, carbon treatment amount, and renewable energy utilization rate. Then, the best strategy is determined using the entropy weight method and the Muirhead mean operator. Finally, a multi-scene case of a residential area is used to validate the model's effectiveness. The results show that: 1) The source-load uncertainty model improves overall robustness. 2) The resident cost is reduced by 7.59%-9.84%, the carbon treatment amount is reduced by 17.71%-95.635%, and the energy supplier profit and the renewable energy utilization rate are increased. 3) The strategy with the best satisfaction or the best utility may not be the best choice among the objective functions of different relationships, so decision-makers need to choose according to the actual situation. The model is beneficial to the economy and environment, and it serves as a guide for community decision-makers in selecting a comprehensive energy multi-objective scheduling strategy.

Comprehensive evaluation for plan selection of urban integrated energy systems: A novel multi-criteria decision-making framework

An applicable and scientific plan of integrated energy system is conducive to the improvement of system operation efficiency and project investment profits. Different from the industrial park integrated energy system, the urban integrated energy system (UIES) is more closed to residents’ life with limited available energy resources, and concern more about energy efficiency and environmental protection characteristics. However, there are few studies focusing on plan selection of UIES investment or considering UIES actual requirements. Therefore, this paper innovatively proposes an intuitionistic fuzzy framework for UIES plan selection. Firstly, based on project demands and characteristics, a comprehensive evaluation index system is constructed from 4 dimensions of economic cost, energy utilization, environmental impact and social recognition. Secondly, under the consideration of method characteristics and calculation rules, a novel combined weighting method integrating the Best-worst Method and the CRITIC are proposed to determine index weights. Thirdly, drawing a balance between group utility and individual regret, decision-making information is aggregated and alternative plans are sorting. Lastly, a case of UIES in Shanghai is applied to verify the proposed framework. Results show that the initial investment, the utilization rate of renewable energy, and the comprehensive utilization rate have the greatest influence on UIES plan selection.

Research on optimal matching of renewable energy power generation system and ship power system

In order to realize the comprehensive utilization of multiple energy sources in renewable energy ships and improve the utilization rate of renewable energy and system economy, this paper studies the matching and optimization of renewable energy power generation system and ship power system. Firstly, the structure of renewable energy ship power system is introduced. Considering the natural conditions and the actual situation of the ship for the first time, the wind power generation, photovoltaic power generation and energy storage devices are configured in the ship power system. The objective function including system annual average cost and system reliability index is established. The constraint conditions are determined, and the system energy scheduling strategy is proposed. Then, the influence of the introduction of renewable energy generation system and energy storage device on the ship power system is analyzed. Finally, particle swarm optimization and improved algorithm are used to optimize and solve the configuration problem. The relationship between the loss of power supply probability and energy excess percentage in ship power system is analyzed for the first time, which provides a reference for the optimal allocation scheme of actual ship. The results are helpful to improve the economy and system reliability of renewable energy ships.

Application research of compressed-air energy storage under high proportion of renewable energy

Abstract China will strive to achieve a ‘dual carbon’ target: ‘carbon peak’ by 2030 and ‘carbon-neutral’ by 2060. In this context, improving the efficiency of renewable energy and reducing the use of thermal power are important ways to achieve the target. Clean, efficient and large-capacity energy-storage technology is the key to improving the utilization rate of renewable energy. First, this paper proposes to use compressed-air energy-storage technology instead of the old energy-storage technology to build an economical and environmentally friendly comprehensive energy park capacity optimization configuration model. Second, this paper uses the newly proposed improved chicken swarm optimization algorithm to solve the model, which is more accurate and faster. Finally, this paper analyzes a comprehensive energy park in north-west China. Through case analysis, it can be seen that the average utilization rate of renewable energy can reach 73.87% through the model proposed in this paper, while the average power-abandonment rate is only 9.32%.

Power balance control of RES integrated power system by deep reinforcement learning with optimized utilization rate of renewable energy

A power balance control method is proposed for renewable energy source (RES) integrated power systems based on deep reinforcement learning (DRL), with the reasonable utilization rate of renewable energy optimized. This method considers the dispatching problems of a high-proportion renewable energy grid from a new perspective. It is proposed that the dispatching of a high-proportion renewable energy grid must consider the reasonable utilization rate of renewable energy and conduct reasonable abandonment of wind and light. And in the offline training of DRL scheduling, the reasonable utilization rate is used as the element of the state vector to train the final power grid DRL control strategy. The control strategy has verified its effectiveness in the IEEE 14-bus system with the supporting datasets.

Research on Low-Carbon Economic Operation Strategy of Renewable Energy-Pumped Storage Combined System

Aiming at the problem of insufficient peak shaving capacity of the power system after large-scale renewable energy such as wind power and photovoltaics is connected to the grid, a new energy-pumped storage combined system low-carbon economic operation strategy is proposed. First, the energy storage characteristics of pumped storage are studied; wind power and photovoltaic output are shifted in time and space; and a new energy-pumped storage combined system model with photovoltaics, wind power, and pumped storage as the main body is established. Second, the carbon trading mechanism was introduced, and the reward and punishment ladder carbon trading cost model was established. Then, via the new energy-pumped storage combined system, the upper optimization target with the smallest investment cost and the lower optimization target with the largest system benefit are established, and a two-layer optimization model solution method based on linearly decreasing inertia weight particle swarm optimization and sequential quadratic programming algorithm is proposed. Finally, via the operating data of a pumped storage power station on an island in northern China, the new energy-pumped storage combined system low-carbon economic operation strategy was simulated and verified. The simulation results show that the low-carbon economic operation strategy of the new energy-pumped storage combined system proposed in this paper can effectively suppress load fluctuations, reduce the impact of wind and wind anti-peak regulation characteristics on the power grid, increase the utilization rate of renewable energy, and improve system economy.

Stochastic Optimization Operation of the Integrated Energy System Based on a Novel Scenario Generation Method

The application of integrated energy systems is significant for realizing the comprehensive utilization of various energy sources and improving the utilization rate of renewable energy. At present, the optimal operation of integrated energy systems is a research hotspot. However, shortcomings remain in the stochastic optimization operation and the scenario generation method. This paper proposes a stochastic optimization operation model of an integrated energy microgrid based on an advanced multi-scenario generation method. First, this paper establishes the time-divided probability distribution model of the forecasting error of the uncertain factors, such as photovoltaic (PV) power and load, which provide the basis for generating scenarios. Moreover, the covariance matrix is used to calculate the time correlation of the time-divided probabilistic distributed models, and the parameters of the covariance matrix are optimized. Second, based on multiple typical scenarios, the stochastic optimization operation model of the integrated energy microgrid is established. Finally, the real data is used to verify the proposed method. The results show that the nonparametric kernel density estimation method has the best fitting effect. On this basis, the time correlation and the operation costs are compared with the scenario sets generated by other methods, which proves the advantages of the proposed multi-scenario generation method and stochastic optimization operation model.

Quality study on different parts of Panax notoginseng root drying with a hybrid drying system powered by a solar photovoltaic/thermal air collector and wind turbine

In order to improve the utilization rate of renewable energy in the drying system and further solve the problem of insufficient solar energy supply, a novel hybrid drying system powered by a solar photovoltaic/thermal air collector and wind turbine was proposed. The heat energy and electricity required for drying process are provided entirely by solar energy and wind energy. In this paper, the drying kinetics, colour parameters, microstructure, thermal stability and ginsenoside contents of different parts of Panax notoginseng dried using the drying system and open sun were studied. Additionally, the economic analysis on the drying system was also conducted on. The drying time using the dryer to dry the main root slices was shortened by 33.3% compared with natural sun drying when the moisture content decreased from the initial value to 13%. For rhizome and fibrous roots, the drying time was shortened by 74.1% and 53.8%, respectively, when the moisture content was decreased to approximately 15% and 12%. Meanwhile, the total saponin contents of the main root slices and rhizome in the dryer were higher, exhibiting average values of 9.1% and 12.2%, respectively. Thus, the drying system is suitable for drying Panax notoginseng, and the payback period was approximately 1.03 year.

The optimal plan selection framework of rail transit photovoltaic power station under probabilistic linguistic environment

Building the rail transit photovoltaic power station (RTPPS) to improve the utilization rate of renewable energy has become an effective way to reduce carbon emissions. Unlike the photovoltaic power stations built in the wide area, the urban rail transit in the big city must consider the demand of landscape, the nearby utilization of electric energy and the impact of light pollution on residents. Hence selecting a reasonable RTPPS plan is important, but it is still the research gap. There are still some problems encountered in the RTPPS plan selection. First, there is lack of an effective decision index system of RTPPS plan selection; second, the existing decision model cannot effectively balance three kind relationships: ①the expression convenience of decision preference and ability to deal with uncertainty; ② the subjectivity and objectivity of the criteria weights in the linguistic environment; ③ the overall effectiveness and local disadvantages of the RTPPS plan. Therefore, the proposed decision index system is established from five aspects: economic performance, rationality of photovoltaic integrated design, effectiveness of light pollution control, energy efficiency and support degree. Meanwhile, according to the principle of probabilistic linguistic term set, combination weighting method and VIKOR model, the corresponding model was proposed to balance tha above relationships. It has the following characteristics：① it improves the ability to deal with uncertain information without changing the expression habit of expert decision preference; ② The weight of decision criterion not only reflects the subjective intention of decision-makers, but also avoids too subjective decision-making; ③ The local disadvantages of the plan are considered in the final result, so as to ensure that the optimal alternative will not be abandoned because of the local disadvantages. The proposed decision index system and decision model jointly construct the optimal plan selection framework of RTPPS to improve the plan selection rationality. The effectiveness of this framework is proved by a China case study.