Renewable Energy Output Research Articles

A Novel Shared Energy Storage Planning Method Considering the Correlation of Renewable Uncertainties on the Supply Side

The shared energy storage service provided by independent energy storage operators (IESO) has a wide range of application prospects, but when faced with the interrelated and uncertain output of renewable energy on the supply side, how to size for energy storage capacity is a highly challenging problem. To this end, this paper firstly proposes a hybrid shared energy storage framework, in which the private energy storage of power suppliers and IESO jointly provide shared energy storage services for users. To generate low-dimensional scenarios that consider the correlation between the multiple uncertainties of wind and solar generation, combining the Kernel function estimation and the Gaussian mixture model forms the Bayesian estimation to fit the historical generation curve precisely. The uncertain output of same generation resources is coupled based on Kullback-Leibler divergence. The Frank-Copula function is used to couple the uncertain output between diverse generation resources. The energy storage planning problem is formulated as a Bayesian distributionally robust optimization model. The related algorithms are designed to solve the model. The effectiveness of the proposed uncertainties coupling and capacity sizing method is validated through a case study in the IEEE 118-bus system.

Study on the multitime scale rolling optimization operation of a near-zero energy building energy supply system

The combined cooling, heating and power system of near-zero energy buildings can provide cold energy, heat energy and electricity to users at the same time, realizing combined cold, heat and power generation. However, the randomness of renewable energy output and the volatility of user loads lead to an imbalance between energy supply and demand. Therefore, a three-stage multitime scale optimization operation method is proposed. This method establishes a three-stage optimization model of ‘day-ahead, day-in rolling, real-time feedback’ to optimize the output of the unit step by step. In the day-ahead phase, the optimization goal is to take the lowest daily operating cost while considering the environmental cost. In the day-in rolling stage, the optimization goal is to control the minimum cost of energy purchase in the time domain and the minimum penalty cost of unit output change. The real-time feedback phase optimizes with the minimum total device power adjustment. The results show that the cost saving rates of multitime scale optimization strategies on typical winter days, typical transition season days and typical summer days are 0.90%, 2.32% and 2.12%, respectively. Compared with the ideal cost of different typical days, the cost deviation rates are 0.21%, 0.51% and 1.97%, respectively. Compared with the optimization of the previous day, the total power of the grid connection line on different typical days is −8.0%, −10.65% and −6.23%, respectively. Therefore, this novel optimization method has good economy, stability and accuracy and has a guiding effect on the formulation of each equipment operation plan.

Thermodynamic analysis of a novel adsorption-type trans-critical compressed carbon dioxide energy storage system

Compressed CO2 energy storage technology is a feasible resolution to stabilize the fluctuation of renewable energy output and has significant development prospects. The main challenge currently facing is how to achieve high-density storage of low-pressure CO2. To get rid of the engineering application limitations caused by low-pressure CO2 liquefaction storage and large-scale cave storage, a new type of adsorption trans-critical compressed CO2 energy storage system is proposed in this paper. Using Fe-MOR(0.25) as an adsorbent, the storage density of CO2 can reach 390.94 kg/m3 at 298 K and 0.1 MPa. The thermodynamic simulation is carried out based on the first and second laws of thermodynamics. The results demonstrate that the system round trip efficiency, exergy efficiency, and energy storage density under the design conditions are 66.68 %, 67.79 %, and 12.11 kWh/m3, respectively. The results of sensitivity analysis indicate that the storage pressure and storage temperature of the high-pressure tank have compound effects on the system, and they are the key parameters affecting the performance of the system. Releasing pressure at critical points can cause abrupt changes in system performance. Heat exchanger effectiveness, compressor, and turbine isentropic efficiency improvements positively affect the system performance.

Day-ahead joint clearing model of electric energy and reserve auxiliary service considering flexible load

With the increasing of renewable energy penetration, adequate reserve capacity is more important to modern power system facing with various uncertain factors. Mobilizing the enthusiasm of units and demand response to participate in reserve auxiliary service can reduce the reserve providing pressure of conventional power supplies, which is conducive to the reliable and economical operation of system. The uncertain factors such as system random failure, prediction error of both load and renewable energy output are considered, and taking unit reserve, demand response such as flexible loads as system reserve resources, this paper establishes the risk cost models to optimize system up and down reserve requirement and make optimal allocation among units and flexible loads. A joint market clearing model of day-ahead electric energy and reserve auxiliary service is established in which both the units and flexible loads participate, and is solved by the robust optimization theory. The joint market clearing model takes the reliability and the economy of the system operation into account, and optimizes the clearing scheme for market decision makers, which can provide a decision reference for the market to resist the risk of uncertainty. Finally, the effectiveness of the model and method proposed in this paper is verified by a modified 10-machine 39-bus simulation example system.

Multi-stage robust clearing model considering renewable energy output uncertainty and unit effective reserve calculation in electricity market

With the advancement of China’s carbon policy, the proportion of renewable energy, mainly wind and solar, has increased, which brings greater challenges to the reservation of power systems. Due to the complexity of China’s power systems, adopting the method of the zonal reserve to ensure stable system operation is very difficult. Among the existing clearing results for calculating reservation, the unit reservation will be restricted by the network security constraints, leading to system operation risks. To obtain the unit reservations efficiently without breaking the security constraints, this paper proposes an effective reserve calculation method for engineering implementation. A box robust optimization is further utilized for Security Constrained Unit Commitment in the method to ensure the consumption of renewable energy, and the stability and clearing efficiency of the power system. Furthermore, a data-driven robust random optimization is employed for Security Constrained Economic Dispatch optimizing the economy of power systems. This multi-stage robust optimization model, which has good extensibility, is in line with China’s clearing process. The simulation analysis based on the actual operation data of a province in China and the IEEE 300-bus system verifies the correctness and feasibility of the proposed model and theory.

Scenario Analysis of Renewable Energy Development and Carbon Emission in the Beijing–Tianjin–Hebei Region

The Beijing–Tianjin–Hebei region (BTH) is a key area with large carbon emissions in China and a demonstration area for renewable energy development, facing the dual test of energy structure transformation and the achievement of carbon peak and neutrality goals. This study analyzes the main influencing factors of carbon emissions based on Kaya’s identity, establishes a socio-economic-energy-carbon emission coupled with system dynamics (SD) model, and designs five scenarios to predict and compare the future trends of energy consumption, renewable energy development and carbon emissions in BTH, respectively. The results show that (1) under the baseline scenario, energy carbon emissions in BTH will peak around 2034, and the intermediate development scenario, the transition development scenario and the sustainable development scenario all show that the region can achieve the emission peak target around 2030. (2) The renewable energy output value of BTH will reach CNY 486.46 billion in 2050 under the baseline scenario, and the share of renewable energy consumption will exceed 50% under the sustainable development scenario. (3) Increasing energy tax regulation and scientific and technological investment and adopting more stringent policy constraints can guarantee the lowest emission intensity while maintaining the current social and economic development level. This study predicts the development of a renewable energy industry and carbon emissions in BTH under different scenarios and provides policy recommendations for the future energy transition in the region.

Multi-Objective optimal scheduling of island microgrids considering the uncertainty of renewable energy output

The grid connection of wind power and photovoltaics significantly increases the uncertainty of the operation and scheduling of power systems. The maximum power that can be transmitted in a particular time slot often fluctuates randomly around a certain value according to a certain probability distribution, and a prediction error occurs between the predicted and actual power. When the actual maximum power that wind power and photovoltaics can generate is less than the power arranged in the scheduling plan, a significant increase in operating costs will occur, as well as an increase in the frequency fluctuation of the microgrid. This may harm the system. To study the effects of the uncertainty of renewable energy output on microgrid dispatching, this paper divides power sources into basic load and frequency modulated (FM) power sources according to the FM characteristics and establishes a multi-objective optimal dispatching model of island microgrids considering the uncertainty of renewable energy output, using the economic benefit and frequency fluctuation rate of the microgrids as the optimization target. Additionally, this paper proposes a two-step optimal scheduling method based on power classification: First, the Monte Carlo method is used to estimate the expectation and variance of the objective function, and a certain number of samples are obtained through sampling. Second, the particle swarm optimization algorithm is used to determine the installation plan of the FM and base load power sources to minimize the average sample cost. Finally, a backpropagation neural network is used to construct the frequency modulation strategy of FM power sources. An analysis with examples shows that, in the context of many samples, the model and proposed two-step scheduling method are reasonable and effective.

Static and dynamic environmental economic dispatch using tournament selection based ant lion optimization algorithm

The static and dynamic economic dispatch problems are solved by creating an enhanced version of ant lion optimisation (ALO), namely a tournament selection-based ant lion optimisation (TALO) method. The proposed algorithm is presented to solve the combined economic and emission dispatch (CEED) problem with considering the generator constraints such as ramp rate limits, valvepoint effects, prohibited operating zones and transmission loss. The proposed algorithm’s efficiency was tested using a 5-unit generating system in MATLAB R2021a during a 24-hour time span. When compared to previous optimization methods, the suggested TALO reduces the costs of fuel and pollution by 9.01 and 4.7 percent, respectively. Furthermore, statistical analysis supports the suggested TALO optimization superiority over other methods. It is observed that the renewable energy output can be stabilized in the future by combining a hybrid dynamic economic and emission dispatch model with thermal power units, wind turbines, solar and energy storage devices to achieve the balance between operational costs and pollutant emissions.

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.

Day-ahead interval optimization of combined cooling and power microgrid based on interval measurement

The combined cooling and power (CCP) microgrid plays an important role in achieving the goal of dual carbon, while the uncertainty of renewable energy (RE) output and the equipment operation mode bring challenges to the operation optimization. To address this problem, a day-ahead interval optimization model based on interval measurement of CCP microgrid is proposed. First, a large number of scenarios of RE output are generated using Latin hypercube sampling. And the scenarios are reduced by the fast backward reduction method (FBRM). The reduced scenarios are combined with the extreme value theorem to obtain the RE output interval range. Then, the demand response (DR) models of electrical load and cooling load are established to achieve peak-cutting and valley-filling. And the optimization model of ice storage air conditioning (ISAC) with different connection modes is established. Furthermore, the day-ahead interval optimization model is constructed of the CCP microgrid. Finally, the results of the day-ahead optimization model for ISAC under different operation modes are analyzed. The simulation results show the accuracy and effectiveness of the proposed model. The interval radius of the proposed method is reduced by 29.408% and 57.409% compared to the traditional interval method and the original scenario interval method, respectively. And the upper limit of the interval is reduced by 519.9923¥, 1987.6423¥, respectively. In the optimization with the participation of DR and ISAC, the operation cost can be reduced. The economic and stable operation of the CCP microgrid can be realized.

Renewable energy output, energy efficiency and cleaner energy: Evidence from non-parametric approach for emerging seven economies

In the contemporary era, environmental recovery is the primary objective of emerging and industrialized economies, where scholars and policy-makers propose adopting environmentally friendly energy resources such as renewables. Still, economies are paying less attention towards renewable energy consumption. In this sense, the current study aims to investigate the factors influencing renewable energy consumption in the emerging seven economies throughout 1990–2021. Specifically, this study explores the influence of renewable energy (electricity) output and energy efficiency on renewable energy consumption. Economic growth, carbon emissions, and technological innovation are control variables. Using various panel data approaches, the results verified slope coefficient heterogeneity, cross-section dependence, and the existence of the long-run cointegration relationship. Following the asymmetric distribution of data, this study uses novel method of moments quantile regression. The estimated results asserted that renewable energy output, energy efficiency, economic growth, and carbon emissions are the significant factors of renewable energy consumption. However, technological innovation substantially reduces renewable energy consumption. The robustness of the results is validated by bootstrap quantile regression, while the [1] panel Granger causality test confirms the two-way causal nexus between renewable energy consumption and the listed variables. Further, this study suggests increased investment in renewable energy output, the energy efficiency sector, and environmentally related technological innovation to encourage renewable energy consumption.

Hierarchical Stochastic Optimal Scheduling of Electric Thermal Hydrogen Integrated Energy System Considering Electric Vehicles

After a large number of electric vehicles (EVs) are connected to the integrated energy system, disorderly charging and discharging of EVs will have a negative impact on the safe and stable operation of the system. In addition, EVs’ uncertain travel plans and the stochastic fluctuation of renewable energy output and load power will bring risks and challenges. In view of the above problems, this paper establishes a hierarchical stochastic optimal scheduling model of an electric thermal hydrogen integrated energy system (ETH-IES) considering the EVs vehicle-to-grid (V2G) mechanism. The EVs charging and discharging management layer aims to minimize the variance of the load curve and minimize the dissatisfaction of EV owners participating in V2G. The multi-objective sand cat swarm optimization (MSCSO) algorithm is used to solve the proposed model. On this basis, the daily stochastic economic scheduling of ETH-IES is carried out with the goal of minimizing the operation cost. The simulation results show that the proposed strategy can better achieve a win-win situation between EV owners and microgrid operators, and the operation cost of the proposed strategy is reduced by 16.55% compared with that under the disorderly charging and discharging strategy, which verifies the effectiveness of the proposed model and algorithm.

Economic scheduling of mobile energy storage in distribution networks based on equivalent reconfiguration method

Compared with traditional stationary energy storage system (SESS), mobile energy storage system (MESS) has power transfer ability in both spatial and temporal dimensions. Thus, it can provide greater flexibility in power system auxiliary services. To simplify and clarify the optimal scheduling problem, a novel “virtual switch” indicator is defined to describe the scheduling of MESS and an equivalent reconfiguration method is proposed by analogy with distribution network reconfiguration. Based on the method proposed, the dynamic economic scheduling of MESS in distribution networks(DNs) can be formulated as a scenario-based stochastic optimization problem, where the uncertainties of traffic flow and renewable energy output are considered by generating scenarios using Monte Carlo simulation. The objective function is to maximize the profit of distribution network operator. The time and cost of MESS scheduling are transformed into those of virtual switch switching in distribution network reconfiguration. Therefore, the operation scheduling problem of a MESS is equivalent to a distribution network reconfiguration problem with a SESS and several virtual switches. In this way, a grid-traffic network coupling problem is simplified to a pure distribution network problem, which provides a novel way to solve the MESS scheduling problems. Finally, simulation results on the IEEE 33-bus distribution system and simple traffic network verify the validity and feasibility of the method proposed.

Economic-Environmental-Social Benefits Assessment of Wind Power Hydrogen Production Project Based on Cloud-MULTIMOORA Approach

This study explores methods to assess the benefits of WPHPP from three aspects: economic, environmental, and social. Decarbonization has become an inevitable trend in the electricity generation industry. Storage integrated renewable energy generation plants have enhanced the stability of the output of renewable energy to a certain degree. Wind power hydrogen production projects (WPHPPs) could be a promising solution to the Chinese government’s peak carbon dioxide emissions goal. To achieve this goal, an evaluation system consisting of 12 main benefits factors is established. Then, an assessment approach based on Cloud-MULTIMOORA is proposed to select the best alternative. Specifically, HELTS-Cloud model is used to describe the information given by decision-makers and experts. Then, the SWARA and CRITIC methods are applied to obtain the subjective and objective weights. Additionally, a constrained optimization model is introduced to obtain the comprehensive weights. Finally, the MULTIMOORA-EDA method is used to obtain the final ranking of alternatives. Additionally, a case study is made to certify the usability and feasibility of the proposed method. This paper provides a method for assessing the benefits of WPHPP and promotes the application of this new kind of green energy project in the future.

Multistage Economic Scheduling Model of Micro-Energy Grids Considering Flexible Capacity Allocation

Micro-energy grids integrating multiple energy sources can realize the efficient use of renewable energy and accelerate the process of energy transition. However, due to the uncertainty of renewable energy, the stability and security of system operations should be taken into account with respect to multi-energy coupling economic operations. Thus, it is essential to make flexible capacity allocations in advance of the actual scheduling of production in the micro-energy grid. With this motivation, this paper constructs a three-stage scheduling model corresponding to the running stage of the spot market. Specifically, the capacity of flexible, active devices is configured in the day-ahead stage; then, the intraday economic operation dispatching scheme is provided according to the capacity configuration. Based on the day-ahead and intraday optimization results, the system power balance is realized through the dispatching process using the reserve capacity of flexible active devices for deviations generated in the real-time stage of renewable energy. For the uncertainty of renewable energy output, the clustering method is applied to realize the clustering analysis of renewable energy output scenarios. In addition, the conditional value at risk (CVaR) theory is introduced to modify the three-stage stochastic optimization model, and the risk values caused by uncertainty are quantitatively evaluated. Finally, we simulate a practical case to verify the effectiveness of the proposed model. The results show that day-ahead flexible capacity allocation enhances the autonomy of the micro-energy grid system, ensures a certain degree of system operational security, and reduces balancing costs in the real-time stage. The higher the risk aversion factor, the more operational costs the system operator pays to avoid the risk. In addition, if the carbon penalty coefficient is higher, the overall carbon emission level of the micro-energy grid will decrease, but it will gradually converge to a minimal level. This paper guides the development of micro-energy grids and has important constructional significance for the construction of multi-energy collaborative mechanisms.

The Role of Sustainable Energy, Technology Adoption, and Green Finance on the Environmental Sustainability in China

Environmental sustainability (ES) has gained global attention due to uncertain environmental and economic conditions, and this phenomenon needs researchers’ intention. Therefore, the current article examines the impact of sustainable energy, such as renewable energy (RE) output and RE consumption, technology adoption, green finance (GF), and economic growth (EG) on environmental sustainability in China. The article has gathered the data from World Development Indicators (WDI) and the central bank from 1990 to 2020. The study employed autoregressive distributed lag (ARDL) to check the connection among the constructs. The findings exposed that the RE output and RE consumption, technology adoption, GF, and EG have positive ES in China. These findings guide the regulators in developing regulations related to improving ES using sustainable energy, GF, and technology adoption.

An online dispatch approach for distributed integrated multi‐energy system considering non‐ideal communication conditions

The distributed integrated multi-energy system (DIMS) has many advantages in terms of the utilisation of renewable energy sources and clean energy. Operation strategies for the DIMS based on a real-time profile have been extensively studied. In a DIMS online optimisation problem, besides fluctuations in the renewable energy output and load, inconsistent time scales of the transport dynamics of different energy flows and non-ideal communication (involving communication uncertainty and latency) result in suboptimal operation in dispatch scheduling. An online multi-time-scale optimal operation strategy is proposed for the DIMS to respond to the above challenges, using a hybrid algorithm comprising a model predictive control method and distributed collaborative consensus algorithm (CCA). The approach is based on a hierarchy, comprising rolling optimisation and adjustment. A rolling optimisation is established to schedule operations according to the latest forecast and status information. The rolling dispatch is then adjusted according to the ultrashort-term adjustment using the CCA. Meanwhile, the effect of the information transmission environment on real-time scheduling is considered, and the robust CCA is improved for the implementation of strategies under non-ideal communication conditions. Case studies and results are presented and discussed to show the effectiveness of the proposed approach with the better matching between demand and supply.

Cubist Regression, Random Forest and Support Vector Regression for Solar Power Prediction

At a time when the energy transition is inescapable and artificial intelligence is rapidly advancing in all directions, solar renewable energy output forecasting is becoming a popular concept, especially with the availability of large data sets and the critical requirement to forecast these energies, known to have a random nature. Therefore, the main goal of this study is to investigate and exploit artificial intelligence's revolutionary potential for the prediction of the electricity generated by solar photovoltaic panels. The main algorithms that will be studied in this article are cubist regression, random forest and support vector regression. This forecast is beneficial to both providers and consumers, since it will enable for more efficient use of solar renewable energy supplies, which intermittency makes their integration into the existing electrical networks a challenging task.

RETRACTED: Optimization scheduling of virtual power plant with carbon capture and waste incineration considering P2G coordination

In order to promote multi-energy complementation and low carbonization, an optimal scheduling model of virtual power plant (VPP) with carbon capture and waste incineration considering power-to-gas coordination is proposed. By introducing the collaborative utilization framework with a carbon capture plant-power to gas (P2G)-gas unit system, the CO 2 in the carbon capture can be used as the raw material of P2G, which produces natural gas to be supplied to the gas unit. In addition, the power consumption of the carbon capture and the flue gas treatment can be transferred through joint dispatching to smooth the fluctuation of renewable energy output, so that the wind power and photovoltaic can be indirectly dispatchable and flexibly utilized. In view of the high dimensional nonlinearity of the proposed optimization model and the difficulty in solving it, a new Gaussian compound differential evolution algorithm is designed to solve the model. The simulation results show that the proposed model and method can provide peak load shifting capacity and improve renewable energy consumption, effectively reducing the cost and carbon emission of VPP.

Multi-time-scale energy management for microgrid using expected-scenario-oriented stochastic optimization

The uncertainty of renewable distributed energy sources, load demand and electric vehicles bring great difficulties to the optimal scheduling of the grid-connected microgrid, this paper proposes a multi-time scale stochastic optimization model for microgrid with low operation cost and high reliability against these uncertainties. In the day-ahead scheduling stage, the Monte Carlo method is used to generate stochastic scenarios and simulate the uncertainty of the microgrid. The day-ahead stochastic optimization model is established considering the energy balance in the expected-scenario, the operation cost of distributed energy sources, the charging and discharging characteristics of electric vehicles, and the stable operation under multiple stochastic scenarios. Then the hourly robust scheduling decisions are obtained by solving the day-ahead stochastic optimization model to achieve the optimization objective of expected optimal and stochastic feasibility. In the intraday ultra-short-term scheduling stage, according to the updated minute forecast information, a rolling optimization strategy is carried out and the intraday optimal power allocation is obtained to minimize the operation cost in the prediction time domain. Even if the prediction error of the ultra-short-term forecast data is very small, it still adversely affects the stability of the system in the real-time operation stage. To eliminate the random fluctuations of renewable energy output and load demand in the real-time operation, the power deviation is smoothed through interacting with the power grid. Compared to the deterministic scheduling model, simulation results demonstrate that the proposed stochastic scheduling model and multi-time-scale optimization strategy are superior in minimizing the operation cost and reducing the negative impact of uncertainty on the economic operation.