Utilization Rate Of Renewable Energy Research Articles

An Effective Method of Equivalent Load-Based Time of Use Electricity Pricing to Promote Renewable Energy Consumption

The variability and intermittency inherent in renewable energy sources poses significant challenges to balancing power supply and demand, often leading to wind and solar energy curtailment. To address these challenges, this paper focuses on enhancing Time of Use (TOU) electricity pricing strategies. We propose a novel method based on equivalent load, which leverages typical power grid load and incorporates a responsibility weight for renewable energy consumption. The responsibility weight acts as an equivalent coefficient that accurately reflects renewable energy output, which facilitates the division of time periods and the development of a demand response model. Subsequently, we formulate an optimized TOU electricity pricing model to increase the utilization rate of renewable energy and reduce the peak–valley load difference of the power grid. To solve the TOU pricing optimization model, we employ the Social Network Search (SNS) algorithm, a metaheuristic algorithm simulating users’ social network interactions to gain popularity. By incorporating the users’ mood when expressing opinions, this algorithm efficiently identifies optimal pricing solutions. Our results demonstrate that the equivalent load-based method not only encourages renewable energy consumption but also reduces power generation costs, stabilizes the power grid load, and benefits power generators, suppliers, and consumers without increasing end users’ electricity charges.

Selection of an economics-energy-environment scheduling strategy for a community virtual power plant considering decision-makers’ risk attitudes based on improved information gap decision theory

To address the impact of decision-makers' risk attitudes (DMRA) and uncertainties on dispatch strategy in community integrated energy systems, a multi-objective dispatch model of a community virtual power plant (CVPP) is proposed. Firstly, a novel CVPP model that considers DMRA and a multi-objective satisfaction model of economics-energy-environment are built. Secondly, the information gap decision theory (IGDT) model is improved by considering uncertainties of renewable energy, loads, and DMRA. Thirdly, the VIKOR method is improved by using the weights from multiple objective functions derived by combining the entropy weight and full consistency method. And the conservative and risky strategy selection methods in the VIKOR method are used to select the optimal strategy from the obtained multi-objective Pareto solution set that satisfies decision-makers with risk preferences. Thus, the impact of DMRA on optimal strategy selection is fully described. Finally, the proposed model's effectiveness is validated using a multi-scenario example of a residential area. The results indicate that: 1) The novel CVPP provides realistic scheduling strategies based on the DMRA. 2) After implementing demand response, the resident cost and net carbon emissions are reduced by 9 % and 91 %, respectively. Energy supplier profit and renewable energy utilization rate are increased. The constructed IGDT model also improves multiple objectives. 3) The improved IGDT model's uncertainty and deviation factors allow for diverse scheduling strategies. Simultaneously, the improved VIKOR method provides a new method for decision-makers to select strategies. The model is a guide for selecting scheduling strategies and a way of encouraging the usage of renewable energy.

An auxiliary optimization method for electric heating renovation based on using a clustering algorithm

The large amount of pollutants emitted by coal heating in winter is one of the main causes of haze, which brings great trouble to people’s lives. To reduce air pollution caused by coal burning and improve air quality, the state advocates the transformation of traditional electric heating methods to accelerate the transformation of China’s energy system to a clean and low-carbon model and improve the utilization rate of renewable energy in China. Therefore, this article through the calculation of the residual capacity of the retrofit and the calculation of the air conditioning load to assist the electric heating transformation. Firstly, the average power of the transformer during a high power period is calculated instead of taking the maximum power directly, which solves the problem of small residual capacity caused by a large maximum load rate and maximizes the transformable capacity. Secondly, the cluster analysis of air conditioning heating as one of the typical electric heating methods is carried out by the K-means clustering method, and the air conditioning load is obtained as the data reference during the electric heating transformation to assist the optimization transformation of electric heating. The two methods are further studied and verified by two examples, and the theory guides the practice to assist the electric heating transformation and achieve the goal of energy saving and emission reduction.

Optimizing air conditioning loads scheduling: A bi‐objective dynamic optimization algorithm considering life loss impact

AbstractAir conditioning loads (ACLs) can actively contribute to optimizing energy usage and increasing renewable energy utilization rate through scheduling. Existing studies on the participation of ACLs in DR have mainly focused on developing ON/OFF strategies, little consideration has been given to the impact of DR on the life loss of ACLs. In light of this, the present study first introduces an equation for quantifying the life loss of ACLs. Building upon this, an optimizing ACLs scheduling model is established considering life loss impact. As the relationship between life loss and refrigeration power is a complex non‐linear high‐order fractional function, it cannot be solved by conventional commercial solvers. Therefore, a bi‐objective dynamic optimization algorithm is proposed based on non‐linear fractional programming (NFP) to address this problem. To assess the effectiveness of our algorithm, it is used in a microgrid case with three schemes. Scheme 3 is aquatinted to consider the economic cost and the life loss of ACLs simultaneously, aiming to maximize the comprehensive benefit of the scheduling scheme. The results confirm the efficiency and superiority of the proposed algorithm.

Stability analysis of pumped storage hydropower plant in abandoned open-pit mine affected by dynamic surface subsidence of combined mining.

The construction of a pumped storage hydropower plant (PSHP) in an abandoned open-pit mine is a potential alternative to green mining and energy storage, which can increase the utilization rate of renewable energy and develop residual resources of abandoned mines. Dynamic surface subsidence affected by combined underground and open-pit mining (CUOPM) seriously affects the construction and operation of the PSHP and is one of the critical scientific issues that needs to be solved immediately. The stability of the PSHP was analyzed and treatment scheme of the goafs was proposed based on on-site measurement, theoretical analysis, and numerical simulation. First, the distribution of goafs in the Haizhou open-pit mining area was investigated and surface subsidence value was obtained using InSAR technology and ground monitoring. Secondly, the surface subsidence mechanism affected by CUOPM is analyzed and indicates the subsidence maximum values and scope of influence are greater than those of single underground mining. A dynamic surface subsidence prediction model for combined mining is established based on the Knothe time function model. Thirdly, based on the CVISC model, the numerical calculation models were established by using FLAC3D, and the characteristics and laws of surface subsidence in different periods of CUOPM were studied. The comparative analysis of the observation results shows that the proposed model and numerical simulation calculation method have excellent applicability and accuracy. Finally, a stability evaluation method of PSHP was established, and the results of the evaluation show that the affected areas are the semi-ground powerhouse (SGPH) and the west side of the lower reservoir. The method of grouting filling was used to treat the goafs, and the results showed that it effectively alleviates the dynamic surface subsidence affected by CUOPM, and provides a safety guarantee for PSHP.

Energy flow optimization of an electric-gas-heat integrated energy system based on the energy circuit theory

The energy flow optimization of the integrated energy system (IES) can not only decrease the operating cost of the IES but also maximize the utilization rate of renewable energy. To deal with the problem that the optimization results fall into the local optimal solution due to the over-limit of IES state variables, based on the IES energy circuit theory, this paper established an energy flow optimization model of the electric-gas-heat integrated energy system by taking the weighted sum of the operating cost, state variables over-limit penalty cost, and wind and light abandonment penalty cost as the optimization objective. The growth optimizer (GO) algorithm can avoid the local convergence of the optimization results and accelerate the solving speed of the model. Therefore, the effectiveness and feasibility of the model and the optimization method are confirmed by the simulation example.

Optimal operation of electric-heat-gas-hydrogen integrated energy system considering source-load uncertainty

Faced with the threat of global climate change crisis, increasing the use of hydrogen-enriched compressed natural gas (HCNG) and the utilization rate of renewable energy can both reduce the carbon emissions of the integrated energy system (IES). This paper establishes an electric-heat-gas-hydrogen integrated energy system (EHGH-IES) framework considering source-load uncertainty, and the natural gas pipe network in the traditional IES is transformed into a HCNG pipe network with a certain hydrogen blending ratio. An optimal operation model of the EHGH-IES considering source-load uncertainty is proposed in this paper. The chance constrained programming method is adopted to deal with source-load uncertainty. We pay attention to comparing different confidence coefficient and hydrogen blending ratio in terms of the changes in the cost of the IES. The results show that the total cost of the EHGH-IES will increase by considering source-load uncertainty and using HCNG pipeline, but will bring some stability and environmental benefits to the IES by them.

Research on the calculation method of the reasonable utilization rate of renewable energy considering generation–grid–load–storage coordinated planning

The continuous advancement of the dual carbon goal will lead to fundamental changes in China’s energy structure, with renewable energy emerging as the dominant force in the power supply system. As the proportion of installed capacity for renewable energy continues to increase, the absorption capacity and reasonable utilization rate of renewable energy will become a concern for all sectors of society. At present, the degree of utilization of renewable energy has emerged as a significant factor limiting the development of renewable energy sources. Due to variations in the power structure, load characteristics, and flexible adjustment resources across different regions, blindly pursuing full absorption or higher utilization of renewable energy as a goal can result in increased reserve costs for the system and limited economic benefits, and hinder the development of renewable energy. Therefore, the present study develops a generation–grid–load–storage collaborative planning model aimed at achieving economic optimization by setting different renewable energy utilization rates and obtains the installed capacity of renewable energy and storage under different conditions in the planned year of Gansu Province. The economic analysis indicates that the optimal utilization rate of renewable energy in Gansu Province is projected to decrease from 100% during the period of 2024–2028 to approximately 90% from 2040 to 2060. It serves as a valuable tool for informing the planning and economic development of renewable energy and storage initiatives in Gansu Province, facilitating the achievement of its dual-carbon objectives.

Optimal Scheduling of Regional Integrated Energy System Considering the Integration of Electric Vehicles and the Life Cycle Assessment Method

With the increasing scale of renewable energy and electric vehicles (EVs), the optimization scheduling of the regional integrated energy system (RIES) is facing new challenges due to the integration of EVs and new energy into the system. To realize the low-carbon economic operation of the RIES, a bi-level optimization scheduling strategy for low-carbon economic operation of the RIES is proposed, which considers EVs' participation and lifecycle carbon emissions, and integrates the electricity market and carbon trading market. Firstly, there are many factors affecting the carbon emissions of EVs and the difficulties in prediction. From the indirect carbon emissions during the use of EVs, the relationship between the carbon emissions of EVs and the charging mode and carbon emission sources is comprehensively analyzed. A carbon emission model of EVs based on the Life Cycle Assessment (LCA) is established in this paper. Secondly, the charging model of EVs based on demand response is combined with the electricity market; The carbon emission model of EVs based on the LCA is combined with the carbon trading market, and a bi-level scheduling strategy based on the flexible interaction between carbon and electricity is proposed. Then the strategy is solved to minimize system economic costs and carbon transaction costs. Finally, simulation example analysis is conducted to demonstrate that the proposed strategy can effectively reduce the total charging costs of electric vehicle users, improve the utilization rate of renewable energy, and reduce carbon dioxide emissions, with good economic and environmental benefits.

Joint operation of mobile battery, power system, and transportation system for improving the renewable energy penetration rate

Future energy system will feature in a high-share of renewable energies (REs), which poses huge challenges to obtain full utilization of renewable power generation. To solve the problem, this paper presents a joint-operation two-stage mixed integer linear programming model to coordinate the power system and train transportation system by optimizing the logistics of mobile batteries and real-time charge/discharge in cities. The full/empty batteries are transported through the train transportation system between the load side and the renewable energy station, which improves renewable energy penetration, economics, and mobilities. The first-stage model deals with the preliminary logistic optimization of batteries including routes, time and capacities of transportation, and the second-stage model optimizes real-time schedule of charge/discharge of batteries. The aim of the proposed model is to minimize the transportation cost, maximize the utilization rate of renewable energy in an energy system and smooth the daily load curve. The proposed model considers technical constraints such as railway transportation capacity, load demand satisfaction and renewable energy consumption in the power system. The optimal logistics plan and real-time charging and discharging plan can be obtained for both full and empty battery transportation. The validity of the method is verified with real data from Northeast China and Northern China, including railway routes, renewable energy output, and load profiles. The results show that the renewable energy penetration rate increases from 84.4% to 95.3% in Northeast China and from 42.7% to 78.2% in Northern China under the 2050 scenario with the proposed method. The peak-to-valley difference in the daily load curve decreases by 84% and 100% in Northeast China and Northern China, respectively. Meanwhile, the case study shows that the costs of battery transportation decrease from 0.398 CNY/kWh and 0.377 CNY/kWh to 0.252 CNY/kWh and 0.254 CNY/kWh when energy density of battery increases from 0.170 kWh/kg to 0.250 kWh/kg. This indicates that mobile energy storage has great economics. Moreover, renewable energy penetration rate is increased and peak-to-valley difference of daily load curve is decreased with no additional transmission lines.

Research on Hydrogen Production System Technology Based on Photovoltaic-Photothermal Coupling Electrolyzer

Solar hydrogen production technology is a key technology for building a clean, low-carbon, safe, and efficient energy system. At present, the intermittency and volatility of renewable energy have caused a lot of “wind and light”. By combining renewable energy with electrolytic water technology to produce high-purity hydrogen and oxygen, which can be converted into electricity, the utilization rate of renewable energy can be effectively improved, while helping to improve the solar hydrogen production system. This paper summarizes and analyzes the research status and development direction of solar hydrogen production technology from three aspects. Energy supply mode: the role of solar PV systems and PT systems in this technology is analyzed. System control: the key technology and system structure of different types of electrolytic cells are introduced in detail. System economy: the economy and improvement measures of electrolytic cells are analyzed from the perspectives of cost, consumption, efficiency, and durability. Finally, the development prospects of solar hydrogen production systems in China are summarized and anticipated. This article reviews the current research status of photovoltaic-photothermal coupled electrolysis cell systems, fills the current research gap, and provides theoretical reference for the further development of solar hydrogen production systems.

Research on optimal scheduling method of active distribution network considering multi-type flexible resources

With the increase in the utilization rate of renewable energy, the volatility and uncertainty of active distribution networks have become more significant. Comprehensive consideration of the coordination and optimization of multiple resources can improve the flexibility of the system and promote energy consumption. Effective substitution Quantitative modeling is carried out considering a large number of flexible resources based on the flexibility of nodes and networks, combined with the physical characteristics of node flexible resources. Through the analysis of the implementation principle, control mode, and operation characteristics of the soft open point of network flexibility resource, the operation model of the soft open point with combined energy storage is established. On this basis, there are two elasticity evaluation indicators: elasticity adequacy and transmission adequacy. And we incorporate flexible benefits into optimization goals. Multiple optimization scheduling models have been created, including economy node flexibility, and network flexibility. Finally, multiple target particle swarm optimization algorithms will use the proposed scheduling model, combined with trend calculation and validation, using an improved IEEE33 node system.

Techno-economic and environmental evaluation of a supercritical CO2 coal-fired circulating fluidized bed boiler power generation

Replacing the steam cycle with the supercritical CO2 (S–CO2) cycle applied in coal-fired power generation is a demonstrated approach to greatly improving efficiency and flexibility. This promotes carbon reduction through efficient use of fossil fuels and improving utilization rate of renewable energy by deep peak shaving. Unlike most studies focusing on conceptual design and efficiency-based investigation, the techno-economic and environmental performance evaluations of the S–CO2 power generation are still rare and undiscovered. In this paper, techno-economic and environmental investigation of a 600 MW S–CO2 coal-fired circulating fluidized bed (CFB) boiler power plant is firstly considered. During this process, a series of economic cost and environmental assessment models and methods integrated with process simulation were proposed. The results showed that there exits an optimum turbine inlet temperature of 635 °C from the techno-economic perspective. The total cost of the S–CO2 power plant increases by ∼10 % and the cost of electricity (COE) decreases by 4.6 % compared to the same-capacity steam plants. The life cycle assessment (LCA) environmental impact potential of the S–CO2 power is far less compared to steam power plants. Its environmental impact is greatest on a global scale and relatively low on a regional scale, such as a country or region.

Research on Optimal Operation of Substation Self-consumption Load Considering Uncertainty

Under the goal of "double carbon", in order to reduce the power consumption, reduce the cost of carbon emission and increase the utilization rate of renewable energy, we propose the optimal operation method of substation self-powered load considering uncertainty. The optimal operation model of substation is established with the objective function of minimizing the operating cost, carbon emission cost and load power variance. The optimal operation strategy of the substation is solved by the affiliation function and particle swarm algorithm, and at the same time, the starting and stopping situations of each load of the substation are solved according to integer planning. A substation in Henan Province is analyzed as an example, and the results show that the method has obvious advantages in reducing the cost of electricity consumption, carbon emission cost, and peak-to-valley difference of the power grid when considering different objectives for optimization comparison under the consideration of photovoltaic and time-sharing tariffs.

Source-load cooperative multi-modal peak regulation and cost compensation mechanism in China’s ancillary service electricity market

To enhance the market participation initiatives from the power source and load sides, we propose a novel power system optimal scheduling and cost compensation mechanism for China’s peak regulation ancillary service market. Owing to China’s energy structure, thermal power accounts for nearly half of the country’s installed power generation capacity. Although the willingness of thermal power units to participate in peak regulation auxiliary services is low, we propose a peak regulation cost compensation and capacity-proportional allocation mechanism. This mechanism comprehensively considers the source-load initiative. From the source side, it encourages entities to participate in peak regulation, and the restriction of the peak regulation initiative is set to ensure that each entity benefits from the peak regulation transaction. From the load side, it takes the shiftable and sheddable load as the hybrid demand response and uses the price information to influence the power consumption behavior of the user side. Subsequently, a peak regulation scheduling model was constructed with the multi-objective minimum thermal power output fluctuation of the lowest system operating cost and minimum renewable energy abandonment. This was solved using a mixed-integer linear programming model and CPLEX. Finally, a power system consisting of wind-solar-hydro-thermal-storage and hybrid demand response with a modified IEEE 30-bus system was tested to verify the effectiveness. It was proven that the proposed method improves the utilization rate of renewable energy and optimizes the scheduling of the economic benefit system of each power generation entity.

The potential application of the triboelectric nanogenerator in the new type futuristic power grid intelligent sensing

AbstractTo meet the requirements of power grid operation control, constructing an extensive sensor network in the power grid is a future development trend. However, due to the high cost, the difficulty of continuous power supply for a long time, the large amount of energy consumption, and the environmental problems caused by abandoned batteries, the traditional power supply mode based on batteries for sensors is inconsistent with China's goal of carbon peak and carbon neutrality. It cannot meet the needs of the future grid. Therefore, it is significant to solve the energy supply problem of numerous sensors to promote the construction of new power grid and improve the utilization rate of renewable energy. As an emerging energy harvesting device, triboelectric nanogenerators (TENG) are suitable for developing self‐powered sensors and powering low‐power sensors due to their mall size, high efficiency, low cost, and environmental friendliness. At the same time, due to the abundant, stable, and widely distributed magnetic field around the equipment in the grid, it can provide continuous excitation for the TENG. Therefore, applying magnetic field energy‐harvesting TENG in the future power grid has a broad development prospect. This paper analyzes the development trend and the crucial problems facing the power grid in the future. We review the research and application of TENG based on the magnetic field in recent years and explain the mechanism of TENG in detail. Finally, the application prospect of TENG in the future power grid has been prospected. This paper can provide a reference for applying magnetic field energy harvesting triboelectric nanogenerators in future power grids.image

Optimization model for home energy management system of rural dwellings

In the context of the increasing popularity of distributed energy, this paper proposes a home energy management system method considering the working state of flexible load to deal with the problem of mismatch between home energy supply and demand. Specifically, in this paper, the flexible load in the system is studied, and the model of flexible load and its working state are established. On this basis, combined with the real-time electricity price of the grid, the model of home energy management system is constructed to minimize the electricity purchase cost and improve the utilization rate of renewable energy, the genetic algorithm is used to solve it, and the influence of time step, flexible load ratio and whether to use energy storage equipment on the optimization degree of the model are analyzed. The research results show that, while meeting the requirements of human comfort and electricity demand, the daily electricity purchase cost of the system with flexible load scheduling is 22.5% lower than that of the system without flexible load scheduling, and the utilization rate of renewable energy is increased by 16.5%. In terms of model optimization, shortening the prediction time step, increasing the proportion of flexible load and using energy storage equipment can further reduce the electricity purchase cost. The case study confirms the effectiveness of the proposed home energy management system model, which can provide an efficient optimal scheduling scheme for household energy saving.

Research on key technologies of large-scale wind-solar hybrid grid energy storage capacity big data configuration optimization

Due to the uncertainty and randomness of large-scale wind and light, the output power of the power grid has great fluctuations. If it is directly connected to the grid, it will affect the main grid. In addition, when the grid switches between on-grid/off-grid operation modes, there will be power shortages, shocks and oscillations. The scientific and reasonable configuration of energy storage system capacity big data can reduce the load power shortage rate, improve the utilization rate of renewable energy, and ensure the reliable operation of the power grid. For this reason, the key technology of large-scale wind-solar hybrid grid energy storage capacity big data configuration optimization is studied. A large-scale wind-solar hybrid grid energy storage structure is proposed, and the working characteristics of photovoltaic power generation and wind power generation are analyzed, and the probability model of photovoltaic power generation, wind power generation and load, as well as the charging and discharging model of battery and super capacitor are established accordingly. On this basis, the optimization objective function is set, the constraints are determined, and the large-scale wind-solar hybrid grid energy storage capacity big data configuration optimization model is constructed. And the PSO algorithm is used to solve the model to realize the big data configuration optimization of large-scale wind-solar hybrid grid energy storage capacity. The research results show that the proposed method of large-scale wind-solar hybrid grid energy storage system has good power supply reliability and economy, and can effectively improve the utilization rate of renewable energy.

Green hydrogen-based energy storage service via power-to-gas technologies integrated with multi-energy microgrid

Power-to-gas (P2G) is a promising solution to the issue of non-dispatchable renewable power generation. However, the high investment costs and low energy efficiency of P2G systems pose challenges. This study designs a green hydrogen-based Energy Storage as a Service (ESaaS) mode to improve the economic efficiency of P2G systems. In this ESaaS mode, the P2G system acts as an energy trading hub. The ESaaS operator manages the system and enables microgrids to access energy storage services. In return, the ESaaS operator generates revenue through electricity and hydrogen trading. To characterize stakeholders' behaviors, we develop a cost-minimizing model for multi-energy microgrid operation and a revenue-maximizing model for P2G investment and operation. To coordinate stakeholders involved in cooperation, we employ Nash bargaining-based model reformulation to determine investment and operation decisions and benefit distribution. Then, logarithm transformation and trading price discretization are introduced to efficiently solve the non-convex and nonlinear reformulated model. Simulation experiments using Shanghai's electricity market and meteorological data demonstrate that the ESaaS mode achieves 100% recovery of surplus renewable electricity and increases the proportion of renewable energy in microgrids from 59% to 83%. The results suggest that green hydrogen-based ESaaS is a feasible solution to increase the utilization rate of renewable energy and bring considerable economic and environmental benefits to all stakeholders.

Barriers to Renewable Energy Share of the Grid in the Context of Big Data and Countermeasures

Abstract China is moving forward, but it pays much attention to data analysis of infinite resource circulation. Therefore, this paper investigates the time-domain convolutional calculation method of deep learning and recurrent calculation method of deep learning and uses them to study and analyze the obstacles and countermeasures for the share of renewable energy in our country’s right grid. Then, the passing situation of renewable energy in China is analyzed. Information related to China’s advancement and utilization of inexhaustible resources from 2010 to 2020. From the analysis of the gathered information, it is unequivocal that the scale and share of inexhaustible resources of ancestors in our country’s rights are gradually increasing. Space occupancy in recent 3 years of inexhaustible resources generation in our country will be more than 133 million kilowatts, and the proportion is about 70% of our country’s right power supply; the solar power generation capacity will increase from almost nothing to 261.1 billion kilowatt-hours in 2020 from less than 300 million kilowatt-hours in 2010, which is nearly exponential growth. Finally, this paper combines the existing features of renewable energy in China, puts forward the problems and challenges encountered in the advancement of inexhaustible resources, and gives countermeasures that can be strengthened the utilization rate of renewable energy in the power grid.