Regional-Level Allocation of CO2 Emission Permits in China: Evidence from the Boltzmann Distribution Method

To achieve the commitment of carbon emission reduction in 2030 at the climate conference in Paris, it is an important task for China to decompose the carbon emission target among regions. In this paper, entropy maximization is brought to inter-provincial carbon emissions allocation via the Boltzmann distribution method, which provides guidelines for allocating carbon emissions permits among provinces. The research is mainly divided into three parts: (1) We develop the CO2 influence factor, including per capita GDP, per capita carbon emissions, carbon emission intensity and carbon emissions of per unit industrial added value; the proportion of the second industry; and the urbanization rate, to optimize the Boltzmann distribution model. (2) The probability of carbon emission reduction allocation in each province was calculated by the Boltzmann distribution model, and then the absolute emission reduction target was allocated among different provinces. (3) Comparing the distribution results with the actual carbon emission data in 2015, we then put forward the targeted development strategies for different provinces. Finally, suggestions were provided for CO2 emission permits allocation to optimize the national carbon emissions trading market in China

A Collaborative Optimization Model for Integrated Energy System Considering Multi-Load Demand Response

The integrated demand response strategy participates in the coordinated operation of the integrated energy system, which can effectively improve the flexibility and stability of the system operation. This paper adopts a multiple load demand response strategy to guide users’ energy consumption habits. Firstly, the cooperative operation structure of integrated energy system considering comprehensive demand response is designed by analyzing the characteristics of multiple loads. Secondly, according to the interactive relationship between the output exchange power and the demand response adjustment of units, a two-stage collaborative optimization model is established. Finally, results show that considering the demand response of electricity-heat-gas load requires higher output power flexibility of the generators and enhances the ability of the system to participate in the demand response. The overall economic benefit of the system can be improved, but the comprehensive satisfaction of users will be reduced

A Robust Scheduling Optimization Model for an Integrated Energy System with P2G Based on Improved CVaR

The uncertainty of wind power and photoelectric power output will cause fluctuations in system frequency and power quality. To ensure the stable operation of the power system, a comprehensive scheduling optimization model for the electricity-to-gas integrated energy system is proposed. Power-to-gas (P2G) technology enhances the flexibility of the integrated energy system and the power system in absorbing renewable energy. In this context, firstly, an electricity-to-gas optimization scheduling model is proposed, and the improved Conditional Value at Risk (CVaR) is proposed to deal with the uncertainty of wind power and photoelectric power output. Secondly, taking the integrated energy system with the P2G operating cost and the carbon emission cost as the objective function, an optimal scheduling model of the multi-energy system is solved by the A Mathematical Programming Language (AMPL) solver. Finally, the results of the example illustrate the optimal multi-energy system scheduling model and analyze the economic benefits of the P2G technology to improve the system to absorb wind power and photovoltaic power. The simulation calculation of the proposed model demonstrates the necessity of taking into account the operating cost of the electrical gas conversion in the integrated energy system, and the feasibility of considering the economic and wind power acceptance capabilities

Forecasting Carbon Emissions Related to Energy Consumption in Beijing-Tianjin-Hebei Region Based on Grey Prediction Theory and Extreme Learning Machine Optimized by Support Vector Machine Algorithm

Carbon emissions and environmental protection issues have brought pressure from the international community during Chinese economic development. Recently, Chinese Government announced that carbon emissions per unit of GDP would fall by 60–65% compared with 2005 and non-fossil fuel energy would account for 20% of primary energy consumption by 2030. The Beijing-Tianjin-Hebei region is an important regional energy consumption center in China, and its energy structure is typically coal-based which is similar to the whole country. Therefore, forecasting energy consumption related carbon emissions is of great significance to emissions reduction and upgrading of energy supply in the Beijing-Tianjin-Hebei region. Thus, this study thoroughly analyzed the main energy sources of carbon emissions including coal, petrol, natural gas, and coal power in this region. Secondly, the kernel function of the support vector machine was applied to the extreme learning machine algorithm to optimize the connection weight matrix between the original hidden layer and the output layer. Thirdly, the grey prediction theory was used to predict major energy consumption in the region from 2017 to 2030. Then, the energy consumption and carbon emissions data for 2000–2016 were used as the training and test sets for the SVM-ELM (Support Vector Machine-Extreme Learning Machine) model. The result of SVM-ELM model was compared with the forecasting results of SVM (Support Vector Machine Algorithm) and ELM (Extreme Learning Machine) algorithm. The accuracy of SVM-ELM was shown to be higher. Finally, we used forecasting output of GM (Grey Prediction Theory) (1, 1) as the input of the SVM-ELM model to predict carbon emissions in the region from 2017 to 2030. The results showed that the proportion of energy consumption seriously affects the amount of carbon emissions. We found that the energy consumption of electricity and natural gas will reach 45% by 2030 and carbon emissions in the region can be controlled below 96.9 million tons. Therefore, accelerating the upgradation of industrial structure will be the key task for the government in controlling the amount of carbon emissions in the next step

Wind Power Short-Term Forecasting Hybrid Model Based on CEEMD-SE Method

Accurately predicting wind power is crucial for the large-scale grid-connected of wind power and the increase of wind power absorption proportion. To improve the forecasting accuracy of wind power, a hybrid forecasting model using data preprocessing strategy and improved extreme learning machine with kernel (KELM) is proposed, which mainly includes the following stages. Firstly, the Pearson correlation coefficient is calculated to determine the correlation degree between multiple factors of wind power to reduce data redundancy. Then, the complementary ensemble empirical mode decomposition (CEEMD) method is adopted to decompose the wind power time series to decrease the non-stationarity, the sample entropy (SE) theory is used to classify and reconstruct the subsequences to reduce the complexity of computation. Finally, the KELM optimized by harmony search (HS) algorithm is utilized to forecast each subsequence, and after integration processing, the forecasting results are obtained. The CEEMD-SE-HS-KELM forecasting model constructed in this paper is used in the short-term wind power forecasting of a Chinese wind farm, and the RMSE and MAE are as 2.16 and 0.39 respectively, which is better than EMD-SE-HS-KELM, HS-KELM, KELM and extreme learning machine (ELM) model. According to the experimental results, the hybrid method has higher forecasting accuracy for short-term wind power forecasting

An Income Distributing Optimization Model for Cooperative Operation among Different Types of Power Sellers Considering Different Scenarios

To alleviate the shortcomings of large-scale grid connections for clean energy, which require stable thermoelectric units to provide backup services, a stable cooperative alliance among different energy types of power sellers must be established. Consequently, a reasonable method to distribute income is required, due to different contributions of each entity in the alliance. Therefore, this paper constructs a comprehensive correction algorithm for income distribution using an improved Shapely value method. We analyze the operating mode of the power seller, and establish the net income calculation model under both independent and alliance operations. We then establish an alliance operation optimization model that considers the constraints of unit output, as well as the balance between supply and demand, with the goal of maximizing income. Finally, an industrial park in a province of northern China is taken as an example to verify the model’s practicability and effectiveness. The results show that the power sales alliance can effectively promote clean energy consumption. The maximum reduction in thermal power generation and CO2 is 8510 MW and 684.515 tons, respectively. We apply the algorithm to income distribution and find that the thermal power seller’s income increased by ¥1,463,870, which enhances the stability of the alliance. Therefore, our income distributing optimization model guarantees the interests of each participant to the greatest extent, and serves as an important reference for income distribution

Multi-Objective Robust Scheduling Optimization Model of Wind, Photovoltaic Power, and BESS Based on the Pareto Principle

With the increasing proportion of distributed power supplies connected to the power grid, the application of a battery energy storage system (BESS) to a power system leads to new ideas of effectively solving the problem of distributed power grid connections. There is obvious uncertainty involved in distributed power output, and these uncertainties must be considered when optimizing the scheduling of virtual power plants. In this context, scene simulation technology was used to manage the uncertainty of wind power and photovoltaic output, forming a classic scenario. In this study, to reduce the influence of the uncertainty of wind and photovoltaic power output on the stable operation of the system, the time-of-use (TOU) prices and BESS were incorporated into the optimal scheduling problem that is inherent in wind and photovoltaic power. First, this study used the golden section method to simulate the wind and photovoltaic power output; second, the day-ahead wind and photovoltaic power output were used as the random variables; third, a wind and photovoltaic power BESS robust scheduling model that considers the TOU price was constructed. Finally, this paper presents the Institute of Electrical and Electronics Engineers (IEEE) 30 bus system in an example simulation, where the solution set is based on the Pareto principle, and the global optimal solution can be obtained by the robust optimization model. The results show that the cooperation between the TOU price and BESS can counteract wind and photovoltaic power uncertainties, improve system efficiency, and reduce the coal consumption of the system. The example analysis proves that the proposed model is practical and effective. By accounting for the influence of uncertainty of the optimal scheduling model, the actual operating cost can be reduced, and the robustness of the optimization strategy can be improved

A Dispatching Optimization Model for Park Power Supply Systems Considering Power-to-Gas and Peak Regulation Compensation

To ensure the stability of park power supply systems and to promote the consumption of wind/photovoltaic generation, this paper proposes a dispatching optimization model for the park power supply system with power-to-gas (P2G) and peak regulation via gas-fired generators. Firstly, the structure of a park power system with P2G was built. Secondly, a dispatching optimization model for the park power supply system was constructed with a peak regulation compensation mechanism. Finally, the effectiveness of the model was verified by a case study. The case results show that with the integration of P2G and the marketized peak regulation compensation mechanism, preferential power energy storage followed by gas storage had the best effect on the park power supply system, which minimized the clean energy curtailment to 11.18% and the total cost by approximately $120.190 and maximized the net profit by approximately$152.005