Strategic Planning for Energy and the Environment

Advancing Sustainable Energy Development: A Study of the Factors Influencing Distributed Photovoltaic Industry in Heilongjiang Province

Pingping Fu — 2024-06-14

In today’s energy industry, the development of clean energy has become a global concern. As an important clean energy technology, photovoltaic power generation (PV) is widely used in various countries and regions, helping to reduce the dependence on traditional fossil energy sources and to reduce greenhouse gas emissions. Existing studies have some shortcomings in terms of factors affecting distributed PV grid-connected capacity. First, previous studies tend to focus on the influence of a single factor on the grid-connected capacity of distributed PV, ignoring the interaction effects among multiple factors. Second, the sample scope in existing studies is relatively narrow, often limited to specific regions or countries, and lacks comprehensive regional comparative analysis. In addition, previous studies often do not fully consider the moderating effect of carbon emission intensity on the influencing factors. Based on the above deficiencies, this study aims to build a regression model by analyzing panel data of central municipalities and provinces including Heilongjiang, Shandong and Beijing over the past three years, and construct an interaction model based on the interaction term. The empirical results show that effective sunshine hours, average feed-in tariff and annual electricity consumption are the main factors affecting the grid-connected capacity of distributed PV with a positive effect. The regional heterogeneity analysis also finds that carbon emission intensity enhances the impact of each factor on distributed PV grid-connected capacity. The regulation effect analysis confirms that the average feed-in tariff and annual electricity consumption have a regulation effect. With the results of this study, we fill the knowledge gaps left by previous studies and provide a new perspective to explore the influencing factors of distributed PV. A deeper understanding of the impact of these factors on distributed PV capacity helps us to better understand and optimize the potential of PV generation. In addition, our findings provide policy makers with recommendations and guidance on how to promote distributed PV generation, thereby contributing to the sustainable development of the clean energy sector.

Study on Zoning Effect of Shallow Geothermal Energy Suitability Based on Structural Matrix Method

Yan Ge — 2024-06-14

Geothermal energy is a clean, renewable resource that can be harnessed to help China achieve its twofold carbon goal and advance its energy security policy. Shallow geothermal, middle-deep geothermal, and dry hot rock geothermal all belong to the clean energy. The thermal energy held in rock, soil, groundwater, and surface water up to a depth of 200 metres is referred to as the shallow geothermal variety, which is characterized by temperatures below 25 $^{\circ}$ C. Its use is mostly dependent on ground source heat pump technology. The ground source heat pump ground pipe heat exchange system, the ground source heat pump groundwater heat exchange system, and the ground source heat pump surface water heat exchange system are all included in the shallow geothermal heat exchange mechanism. The structural matrix approach is used in this study to determine if the groundwater heat exchange system for the shallow geothermal ground source heat pump in Sanmenxia’s urban setting is appropriate. The results showed that: the most influential factor was hydrogeological conditions, followed by hydrodynamic and water chemistry conditions. Aquifer recharge capacity and water capacity was the necessary condition of suitability partition. Studied hydrodynamic field and chemical field, analyzed aquifer thickness and groundwater flow under current conditions and determined the appropriate zoning, proposed groundwater aquifers and recharge horizon.

Deep Thermal Power Peak Shaving Compensation Considering Operating Costs and Electricity Revenue

Yiqun Meng — 2024-06-14

Unlike traditional methods of electricity generation such as thermal power, renewable energy sources like wind power exhibit characteristics of randomness and volatility, leading to unstable power generation. With the continuous increase in grid-connected capacity of renewable energy sources like wind power, the demand for grid peak shaving is on the rise, highlighting the issue of inadequate peak shaving capacity in China’s power system. As a primary source for peak shaving in the country, thermal power units incur significant revenue losses when participating in deep peak shaving, and under the current mechanism, they cannot obtain sufficient compensation for deep peak shaving. In response to these issues, the paper proposes a method for compensating thermal power units for deep peak shaving. Firstly, considering the reduced efficiency of thermal power units during low-load operation, a cost model for the operation of thermal power units is established. Secondly, building upon the existing compensated peak shaving baseline, a method for compensating thermal power units for deep peak shaving is introduced. Finally, using a simulation of a local power grid in Liaoning Province as an example, an analysis of the compensation for thermal power peak shaving and overall profits under the existing mechanism and the proposed mechanism is conducted. The results obtained validate the rationality and effectiveness of the method proposed in the paper, offering insights for the development of a compensation mechanism for thermal power deep peak shaving.

Modelling and Assessing the Advantages of Integrating Wind, Solar, and Coal Systems within an Annual Bilateral Trading Setting

Meng Yiqun — 2024-06-14

The integration and co-location of new energy sources and thermal power generation within the same grid connection point, under the management of a single market operator, provide noteworthy potential for exploration in the northern part of our nation. This method is based on the premise of allocating new energy resources primarily for generating contracted electricity, while using thermal power to offer ancillary services. This strategy aims to integrate and use the distinct benefits of both thermal power and new energy sources. The yearly bilateral trade market has significant potential for growth and development. In order to model and evaluate the income generated by the interconnected system’s involvement in the yearly bilateral trading market, this study first establishes the definition of the interconnected system and examines its research significance in engaging in annual bilateral trading. This study utilizes the theoretical framework of master-slave games to analyze the involvement of a coupled system in yearly bilateral trade. The specific case examined in this research is a wind-solar-thermal coupling system located in a particular location of Liaoning Province. This study examines the economic advantages of engaging in the electricity market by considering the marginal cost and LCOE (Levelized Cost of Electricity) perspectives. The findings suggest a notable enhancement in the advantages of the integrated system inside the yearly bilateral trade market, in contrast to conventional power sources present in the market, as well as instances when these sources function autonomously without integration.

Enhancing Resource Allocation for Multi-Energy Storage Systems: A Comprehensive Approach Considering Supply and Demand Flexibility and Integration of New Energy

Xin Tian — 2024-06-14

This study presents an innovative optimization method for resource scheduling in multi-energy storage systems, focusing on improving resource allocation while considering supply-demand flexibility and renewable energy integration. As renewable energy gains popularity and multi-energy systems become more complex, effective utilization of energy storage to achieve supply-demand balance, optimize energy scheduling, and maximize renewable energy integration is crucial. To address this challenge, a Markov dynamic model is developed to capture the dynamic changes in energy supply and demand within the multi-energy storage system. The model is then solved using a reinforcement learning approach to optimize resource scheduling decisions. Numerical simulations and case studies are conducted to validate the effectiveness and feasibility of the proposed method, showcasing its potential to enhance operational efficiency and reliability in multi-energy storage systems amidst constantly changing energy patterns. This research provides valuable insights and decision support for the design and operation of multi-energy storage systems, contributing to the advancement of sustainable energy utilization and promoting sustainable development in the energy sector.

Demand Response Power-Gas Interconnection Energy System and Power Metering Based on SOGA

QianQian Cai — 2024-06-14

Under the double background of global energy crisis and environmental pollution, China vigorously develops renewable energy while accelerating the construction of energy Internet. Taking demand response into account, this paper proposes the optimal design of power-gas interconnection energy system and power metering, and establishes the optimization research model of IEGES (integrated electricity-gas energy system) with demand response. Firstly, the structure model of the electric-gas interconnection energy system with CHP (Cogeneration, combined heat and power) as the core is constructed, and the energy conversion relationship and different energy flow directions of the coupling equipment are expounded from three aspects. The natural gas source point, pipeline equation, power side branch equation, voltage and current equation are modeled and sorted out, and the square term in the equation is linearized by second-order cone programming method, and the mixed integer nonlinear programming problem is transformed into mixed integer linear programming problem. A single objective genetic algorithm with “elite strategy” was selected to solve the equipment capacity optimization problem of IEGES system with system economy as the optimization objective. After a long time of parameter combination attempts, the current population size is 30, the number of iterations is 600, the crossover rate is 0.8, and the heritability is 0.3. The above parameters can obtain better convergence results on the basis of considering the operation time. Finally, a stochastic optimization method of energy Internet considering integrated demand response and uncertainty of wind power is proposed, which aims to meet the energy demand of end users while minimizing the operating cost of the system. The comprehensive demand response strategy including internal and external demand response is considered in the model. Internal demand response is realized by adjusting the internal operation mode of EH, while external demand response is implemented by the end user’s active response, and the load is time-shifted or interrupted under the guidance of external signals.

Intelligent Energy System and Power Metering Optimization Based on the Energy Plan Model

Ce Peng — 2024-06-14

With the national carbon neutrality and carbon peaking policies proposed one after another, this paper proposes intelligent Energy system design and power metering based on the Energy Plan model. In this paper, the Energy PLAN model is proposed as the basic model for energy development planning during the 14th Five-Year Plan period, and the simulation calculation is carried out for the energy system in 2020. The research shows that there is a large gap between the peak and valley of power load, the peak time of power supply and demand does not match, and the response-ability of the energy supply department is poor. Thus, the problems to be solved in the development of energy are determined. In addition, the accuracy of the model is further verified by comparing the actual data with the simulation results of Energy PLAN, and the direction of energy structure reform is determined. By coupling the NSGA-II (Non-dominated Sorting Genetic Algorithms-II) algorithm with the development model, a dual-objective multi-time scale scheduling model is established, which aims at the minimum operating cost and the best energy and environmental benefit coefficient. Secondly, the proposed model is solved using the NSGA-II algorithm. In order to ensure the diversity of solutions and promote the Pareto front approach to the ideal Pareto front, the fuzzy dominance method is used to perform a fast non-dominated sorting of the population, and the maximum satisfaction method is used to select the Pareto optimal compromise solution. Example analysis shows that when the power demand level is L*-L*-L*, the power input under “H-M”, “M-L” and “H-L” scenarios is 106.02 × 10³, 102.73 × 10³ and 94.2 × 10³ GWh, respectively. However, when the power demand level is H*-L*-L*, the power input is always 142.64 × 10³ GWh. With the decrease in the transmission rate, the measurement data obtained by the user’s electric acquisition platform becomes less and less, and the performance of state estimation becomes worse. When λ=50%, the proposed model still maintains a high estimation accuracy.

Carbon Emission Measurement of Prefabricated Buildings Based on LCA and Research on Energy Saving and Emission Reduction Strategy of Renewable Energy

Jinglei Song — 2024-06-14

In the face of diverse quantification methods for building carbon emissions, this article delves into the Life Cycle Assessment (LCA) approach to comprehensively measure the carbon emissions of prefabricated buildings throughout their lifespan. It meticulously identifies the carbon emission sources in prefabricated buildings and analyzes the measurement models relevant to their emissions in both physical and chemical stages. Prefabricated buildings hold profound implications for transforming the construction industry and advancing its sustainable development path. Examining the energy-saving characteristics and emission reduction potential of prefabricated buildings from a life-cycle perspective, this article analyzes the carbon emission measurement model during the prefabricated building transformation stage. This comprehensive analysis of the building atomization path and influential carbon emission factors lays a theoretical foundation for transitioning prefabricated buildings towards energy-saving and emission reduction strategies. Using the LCA method, carbon emissions were calculated, revealing a positive correlation with building size. Notably, through the prefabricated construction method detailed in this article, carbon emissions were significantly reduced by 30% compared to traditional construction methods.

Investigating Electricity Usage Profile for Residential Consumers in India

Archana — 2024-06-14

Electricity has become one of the essential needs in modern society. The demand and necessity for electricity have increased with population, trade, and industry, which has resulted in increased stress on the grid. As the development of an electricity storage system is costly, utilities are working on customer segmentation to classify the consumers according to their usage patterns to understand their demand and further develop resources accordingly. In this work, the K-Means clustering algorithm has been applied to a two-year monthly consumption data set (January 2019 to December 2020) of 100 residential consumers from Kutheda village Hamirpur (Himachal Pradesh, India) to visualize the diverse consumption behaviour. It resulted in four different clusters of consumers. It also showed that there is a group having a large number of consumers whose consumption is low and consumption pattern is very less flexible in different seasons. Another group of consumers with high consumption also shows high flexibility in consumption patterns, but their number is less. Also, the winter season shows the highest consumption of electricity and the monsoon season shows the lowest consumption of electricity in this particular region. This analysis will certainly help the electricity provider to make production planning for a different group of consumers in a specific season based on electricity consumption patterns.

Decomposition Analysis of CO2 Emissions in Greece from 1996 to 2020

Eleni Tsepi — 2024-06-14

To meet Greece’s commitments under the Kyoto Protocol (KP) and the UN Framework Convention on Climate Change (UNFCCC), it’s crucial to dissect the primary influencers of its greenhouse gas emissions. This study utilized the logarithmic mean Divisia index method in its additive form to delve into the main drivers behind carbon dioxide emissions in Greece from 1996 to 2020. The analysis revealed that the primary catalyst for the rise in CO₂ emissions was the growth in GDP. Conversely, advancements in energy efficiency, as indicated by a decrease in energy intensity, played a significant role in reducing CO₂ emissions. While population growth contributed to an increase in emissions, a decline in emissions intensity showcased a reduction in emissions per unit of output or consumption. The period from 1996 to 2020 witnessed a 38% change in emissions, demonstrating the effectiveness of Greece’s environmental policies. These findings highlight the pressing need for a sustained focus on enhancing renewable energy sources (RES) penetration and ensuring comprehensive attention across all sectors to further reduce emissions.

Feasibility Analysis of Solar Energy and Role of Lithium-Ion Battery Reserved in Electric Vehicle Market: A Path Towards Green Transportation

Mohammad Amir — 2024-06-14

Solar-based vehicular electrification is bound to play a vital role in Green House Gas (GHG) emission reduction from conventional transports and supporting the fight against environmental degradation. This paper presents the feasibility of solar-based electric vehicle charging (SEVC) in India, a shift of the transportation sector to renewable energy implementation. Solar-electric vehicles may transform the scenario in the coming future in India on a larger scale. Extensive negative environmental impact is produced by transport activities as it pours out 14% of global GHG emissions. The transportation sector is the second major contributor to pollution in India. Currently, dealers of non-renewable resources like coal, petroleum, etc. are facing a tough time meeting increasing demand. This results in limited resources which are being suppressed by the dealers and producers to meet this need which leads us to an insecure future, having scarcity of minerals and fuel. In this paper, we perform a graphical analysis of fuel consumption in India, the growing demand for green transportation in the Indian market and solar potential in our country. In Feb 2023, India’s government revealed the finding of a 5.9 million tons stockpile of unexplored lithium deposits in the Jammu and Kashmir state. So, the price of batteries would be significantly impacted by this, making EVs more accessible to customers. This paper also includes challenges in adopting solar-based transportation, steps of the Indian Government towards green transportation and recommended policies.