Decrease Carbon Emissions Research Articles

Does the green finance reform and innovation experimental zone policy reduce carbon emission intensity? Evidence from Chinese cities

The reduction of carbon emission intensity cannot be achieved without policy support, of which green financial policy is a major component. The main aim of this study is to discuss the causality between the green finance reform and innovation (GFRI) experimental zone policy and the intensity of carbon emissions in China. Using data from 265 Chinese cities from 2005 to 2021, this study applies a time-varying difference-in-differences (DID) model to assess the effect of the GFRI experimental zone policy on the intensity of carbon emissions. The heterogeneity of impacts and the parallel-serial mediation effect of this experimental zone policy are discussed further. The following main findings are obtained. (1) The GFRI experimental zone policy dramatically reduces carbon emission intensity, and this effect is more prominent in cities with large sizes of credit and bond finance. (2) After considering the heterogeneity of government environmental concerns and fiscal pressures, this study reveals that the effect of the GFRI experimental zone policy on reducing carbon emission intensity is weaker in cities with high numbers of environmental protection penalties and fiscal pressures. As the number of environmental penalties and fiscal pressures increases, the effect of the GFRI experimental zone policy on reducing carbon emission intensity decreases; that is, the phenomenon of environmental protection paradoxes and policy conflicts has emerged. (3) Increasing financing for green projects and decreasing the energy intensity of the industry are the primary mediating channels for GFRI experimental zone policy to reduce carbon emission intensity, but the mediating channels for boosting green innovation and promoting energy structure adjustment are not prominent. Moreover, the total effect of the GFRI experimental zone policy on reducing carbon emission intensity is 72% direct and more than 20% indirect. This study concludes that in regions with high fiscal pressure, governments should rely more on green finance to decrease carbon emission intensity. Meanwhile, a green finance policy must be more supportive of zero-carbon energy production and reduce direct environmental penalties to decrease carbon emission intensity in the future.

Analysis of low-carbon rice farming behavior and its influencing factors in farmers under the distributed cognition perspective—empirical study based on 2,173 farmers in Jiangxi Province

IntroductionRice production is the core component of the food security strategy in China, but it is also a major source of methane and nitrous oxide. Promoting low-carbon rice farming (LCRF) to increase the carbon sink, decrease carbon emissions, and achieve low-carbon, high-yield production is an inevitable “win-win” choice for achieving “double carbon” targets and guaranteeing national food security. This study contributes to the advancement of research on farmers’ adoption decisions and fills gaps in LCRF’s technical research on farmers’ decision-making behavior. The results also provide a basis for formulating policies to encourage LCRF and protect cultivated land.MethodsThis study conducted field research on 2,173 farming households in Jiangxi Province, a traditional agricultural province in China, and examined the effects of personal forces, local forces, and cultural forces on LCRF adoption behavior by introducing the distributed cognition theoretical framework based on a status quo analysis and employing the multivariate ordinal logistic model.Results and discussionThe results of the study showed that: 1) the overall acceptance of LCRF behavior is currently not very high. In the 2173 questionnaires, the mean number of LCRF behavior items accepted by farmers was 3.10 items; 153 farmers did not adopt any LCRF behavior, and only 77 farmers adopted all LCRF behaviors. Most farmers (n = 535) adopted three LCRF behaviors. 2) In distributed cognition affecting LCRF behavior, acceptance was primarily affected by cultural forces, followed by local forces, while the effects of personal forces were limited. Therefore, it is recommended that training and promotion should be increased, policy subsidies should be increased, the land market should be improved, and LCRF demonstrations should be carried out to increase the acceptance of LCRF behavior among rice farmers.

One-step method to produce feedstock for green ammonia of H2:N2≥3:1 by solid oxide cell

It is significant to develop a green ammonia preparation method for decreasing carbon emission. This work demonstrates a zero-carbon method for preparing ammonia feedstock consisting of a ratio of H2:N2 ≥ 3:1 by direct electrolysis of air and steam as fuel gas through solid oxide electrolysis cell (SOEC). Through adjusting the working current and proportion of air/steam, the ratio of H2 and N2 can be achieved to more than 4:1, and the fuel is pure enough to be used for synthesizing ammonia. The production rate of ammonia feedstock reaches 1.79 × 10−6 mol/ (cm2·s), and the energy conversion efficiency (ECE) reaches 69.30 %. A stable 100 h electrolysis operation is carried out to generate H2/N2 required for ammonia production. This work provides a new efficient method for the synthesis of green ammonia.

The Impact of South Africa’s Carbon Tax Policy on Carbon Leakages on its Top Five African Trading Partners

The carbon leakage phenomenon, whereby decreased carbon emissions in locations with strict constraints on carbon emissions may be offset to some extent by increased carbon emissions in locations with less strict constraints on emissions, has the potential to undermine efforts to mitigating carbon emissions. As such the objective of this study was to use a natural experiment model to estimate the extent of carbon leakage associated with South Africa’s Carbon Tax on the combined carbon emissions of its top five trading African partners (Mozambique, Namibia, Botswana, Nigeria and Eswatini). This was a highly essential study and arguably a first of its kind, to infer causal impact of the carbon tax on carbon leakages using a single group interrupted time-series analysis regression discontinuity design. Results from the study showed that in the long-run, the carbon tax has decreased carbon emission in South Africa by 4.42 percent while increasing carbon emissions in South Africa’s trading partners by 1.003 percent. This shows evidence of a carbon leakage because of the carbon tax, and it is recommended that policymakers should consider trade restrictions to prevent the import of carbon-intensive commodities or institute universal, instead of unilateral, climate change mitigation policies.

Enhancing envrionmental sustainability via interval optimization for low-carbon economic dispatch in renewable energy power systems: Leveraging the flexible cooperation of wind energy and carbon capture power plants

In response to pressing environmental challenges, the power system is gradually developing towards a more efficient, clean, and sustainable direction. Carbon capture power plants (CCPPs) and wind power generation will play a significant role in this eco-friendly transformation. However, the high energy consumption of CCPPs and the anti-peak regulation characteristics of wind power lead to contradictions in their coordinated operation during peak shaving, restricting the system's low-carbon economic development. Aiming at the above challenges, an interval optimization method is proposed to coordinate wind power and CCPPs. Firstly, a flexible operation model of CCPPs is established, and the coordinated scheduling mechanism of wind power joint carbon capture operation and load-side demand response is introduced to optimize the system carbon emission through low carbon scheduling on both sides of the source and load. Secondly, interval numbers are used to manage the uncertainty of wind power output, and the interval-optimized scheduling model of wind power combined CCPPs is established. To solve the proposed model, the improved interval possibility method is adopted to transform the uncertain model into a deterministic one, which is solved by the CPLEX solver. Finally, the simulation outcomes using the IEEE 30-bus system framework reveal that the proposed approach significantly enhances the coordination between CCPPs and wind power. Notably, when compared to scenarios that solely focus on CCPPs, the proposed method achieves a substantial reduction in total costs by 16.67 % and decreases carbon emissions by 26.53 %. This sets a significant reference for low-carbon economic dispatch within power systems, providing key insights and foundational guidelines for future implementations, as well as novel strategies and technological support for the sustainable transformation of the power system towards lower carbon emissions.

Power PV Forecasting using Machine Learning Algorithms Based on Weather Data in Semi-Arid Climate

As the energy demand continues to rise, renewable energy sources such as photovoltaic (PV) systems are becoming increasingly popular. PV systems convert solar radiation into electricity, making them an attractive option for reducing reliance on traditional electricity sources and decreasing carbon emissions. To optimize the usage of PV systems, intelligent forecasting algorithms are essential. They enable better decisionmaking regarding cost and energy efficiency, reliability, power optimization, and economic smart grid operations. Machine learning algorithms have proven to be effective in estimating the power of PV systems, improving accuracy by allowing models to understand complex relationships between parameters and evaluate the output power performance of photovoltaic cells. This work presents a study on the use of machine learning algorithms Catboost, LightGBM, XGboost and Random Forest to improve prediction. The study results indicate that using machine learning algorithms LightGBM can improve the accuracy of PV power prediction, which can have significant implications for optimizing energy usage. In addition to reducing uncertainty, machine learning algorithms improve PV systems’ efficiency, reliability, and economic viability, making them more attractive as renewable energy sources.

Multi-source data-driven technology research on carbon emission dynamics prediction in electric power industry

Abstract This paper analyzes the trend of power generation structure and carbon emission changes in the power industry and decomposes and analyzes the influencing factors of carbon emission in the power industry by using the LMDI decomposition method. Combined with the analysis of the influencing factors of carbon emissions in the power industry from 2016 to 2022, the carbon emissions of the power industry in the Yellow River Basin are simulated by the scenario analysis method. Four simulation scenarios were constructed based on the economic scale, industrial structure, industrial electricity consumption intensity, thermal power fuel conversion rate, and power supply structure. The IPSO-LSTM model for carbon emission prediction was created after optimizing the LSTM neural network prediction model. Combining the scenario analysis method to set the amount of changes in the high carbon, baseline, and low carbon scenarios of the influencing factors, the carbon emissions from the power sector in different scenarios are predicted for the years 2025-2035. From 2025 to 2035, the carbon emissions from the power sector in the three scenarios, except for the energy transition scenario, show a trend of increasing, then decreasing, and then increasing over the study period. The energy transition scenario shows a pattern of increasing and decreasing carbon emissions from the power sector.

Biomass as an alternative feedstock to oleochemicals.

The huge demands for petrochemicals have led to a rapid increase in the production of these fossil-based derivatives. Biomass represents a promising feedstock for addressing the challenges related to petrochemicals in terms of the necessity to apply renewable sources and the need to decrease carbon emissions. Among the natural biomass products, most studies have attempted to upgrade natural oils owing to their promising advantages of worldwide availability, low-cost processing, and built-in functionality. This paper discusses the upgradation of natural oils to the most beneficial oleochemicals, including fatty acids, fatty alcohols, and fatty acid methyl esters. This review also covers the utility, physico-chemical properties, and the production processes for such materials. The interconnected reaction routes to produce oleochemicals and the affecting parameters (catalyst design, temperature, and pressure) are also elucidated. Furthermore, this article discusses the future perspective of oleochemicals based on their development in recent years.

Multiple Attribute Group Decision-Making with Neutrosophic Environment for Carbon Emission Prediction on Sustainable Urban Management

Sufficient CO2 is indispensable for vegetation, but space and oceanic vehicles, industrial chimneys and land use tons of extreme CO2 and are typically accountable for global warming, climate variations and greenhouse effect. Owing to COVID19, CO2 discharge was in 2020 at its lower level than first ten years. However, the time taken is not known to decrease, increase or change carbon emissions to an endurable point. Precise predicting of carbon production has real consequences for selecting the optimal ways of decreasing carbon emissions. A pressing necessity to control these carbon emissions is needed. The preliminary step is to precisely recognize the milestones and threat levels. Specific thresholds should be mapped that formulate the maximum levels of CO2 namely – the point of no return, risk point, and so on. This article focuses on the development of Multiple Attribute Group Decision-Making with Neutrosophic Environment for Carbon Emission Prediction (MAGDM-NECEP) method on Sustainable Urban Management. The MAGDM-NECEP architecture proficiently manages the multi-criteria nature of emission calculation, while neutrosophic logic accommodates ambiguity and uncertainty in input dataset. Furthermore, GSO enhances model parameters, improving prediction performance. The MAGDM synergy and neutrosophic logic offer strong decision-making abilities, whereas GSO fine-tuned the model parameter for superior outcomes. Empirical analysis establishes the efficiency of the presented technique in precisely predicting carbon emission, providing valuable insight for the environmentalist and policymaker in developing efficient mitigation strategy

Energy Management System: The Engine for Sustainable Development and Resource Optimization

In the background of traditional energy sources, people primarily rely on fossil fuels for electricity generation, but this leads to environmental issues and energy dependency. Therefore, the introduction of the Energy Management System (EMS) becomes crucial, which helps optimize electricity distribution and management to integrate new energy sources effectively, achieving a more sustainable, stable, and efficient electricity supply. It is not just a simple technical solution, but is a strategic approach aimed at optimizing the use and allocation of energy resources to maximize efficiency as well. The intelligent management provided by the EMS enables collaboration between new energy sources and the traditional power grid, reducing electricity costs and decreasing carbon emissions. This overview aims to delve into the core concepts, technological applications, and future trends of the EMS. It will comprehensively analyze the practical applications of the EMS in various fields from various perspectives, including system architecture and data analysis methods, and provide an in-depth understanding of their positive impact on energy efficiency, environmental sustainability, and economic development.

Public Administration in a Context of Engaging Local Communities for Solar Energy Spreading: Analysis of Innovative Models and Challenges for Ukraine

The article examines the opportunities and challenges related to the implementation of innovative solar energy models in urban and rural (settlement) territorial communities. It is identified that in urban communities, where there is high competition for space, solar energy opens up new possibilities for sustainable development through the integration of solar panels on the roofs of buildings, in office centers, and other urban structures, contributing to the reduction of dependence on traditional energy sources and decreasing carbon emissions. Innovative solutions, such as solar roads and facades generating energy, demonstrate significant potential for further integration into urbanized environments. At the same time, rural (settlement) areas face their own set of challenges and opportunities in using solar energy, where the lack of access to centralized energy supply makes it an ideal solution for providing local communities with electricity. Solar systems can be installed on individual houses, in schools, and hospitals, not only providing them with electricity but also contributing to social and economic development. Particularly, in the context of ensuring safety and improving the comfort level of local residents, the use of autonomous street lighting systems in remote areas, capable of operating without connection to the electrical grid due to being fully powered by solar energy, which is unlimited in supply and harmless to the natural environment, deserves special attention. Moreover, the agricultural sector can also reap significant benefits from the development of solar energy by using photovoltaic systems for irrigation, ventilation, and other needs of farming operations. At the same time, the transition to solar energy is accompanied by certain challenges, including integration into existing power grids, energy storage, regulatory and financial barriers, etc. However, progress in battery technology development, improvements in the political and economic climate, and increasing public support are contributing to the industry’s development. The article analyzes how solar energy can transform urban and rural landscapes, creating new business models and promoting sustainable and economically viable futures. A series of successful solar energy use cases that demonstrate its potential for urban and rural (settlement) areas are considered. The article emphasizes the importance of integrating solar energy into the overall energy system and the need to focus joint efforts of public authorities, business structures, and civil society to overcome existing challenges. The scientific novelty of the study lies in the comprehensive analysis and synthesis of contemporary cutting-edge approaches to the implementation of projects in the field of solar energy, which can ensure efficient use of solar energy under the conditions of various types of territorial communities, which is extremely relevant under the conditions of post-war recovery and development.

Dynamic capacitated facility location problem in mobile renewable energy charging stations under sustainability consideration

The deployment of mobile renewable energy charging stations plays a crucial role in facilitating the overall adoption of electric vehicles and reducing reliance on fossil fuels. This study addresses the dynamic capacitated facility location problem in mobile charging stations from a sustainability perspective. This paper proposes Two-stage stochastic programming with recourse that performs well for this application, and the location of the mobile renewable energy charging station (MRECS) management addresses the complex dynamics of reusable items. To solve this problem, we suggested dealing with differential evolutionary (DE) and DE Q-learning (DEQL) algorithms, as two novel optimization and reinforcement learning approaches, are presented as solution approaches to validate their performance. Evaluation of the outcomes reveals a considerable disparity between the algorithms, and DEQL performs better in solving the presented problem. In addition, DEQL could minimize the total operation cost and carbon emission by 7% and 20%, respectively. In contrast, the DE could decrease carbon emissions and total operation costs by 5% and 2.5%, respectively.

Impact of environmental fiscal reforms on carbon emissions of EURO-4 countries: CS-NARDL approach

This study investigates the effects of the Environmental Fiscal Reforms (EFR) policy on carbon emissions of the top 4 economies of Europe, using annual time series data from 1995 to 2022. The study used a cross-sectional nonlinear autoregressive distributed lag (CS-NARDL) model to examine the relationship. Estimates claim that the variables have an asymmetric long-term and short-term relationship. The negative impacts of carbon taxes on carbon emissions prove that emissions are reduced when polluting activities are taxed. Fiscal policy instrument such as taxation changes the behaviour of the private sector by disincentivizing polluting activities. On the other hand, government expenditure on environmental protection encourages the private sector to embrace and invest in green technologies, decreasing carbon emissions. The ECM term is negative and statistically significant at a 1 percent significance level for CS-NARDL model, implying a stable long-run relationship between variables. It demonstrates that short-run disequilibrium converges to long-run equilibrium at a speed of 47.2 percent. The study also propels new insight into the effectiveness of EFR policy, showing that taxes act as a counter-incentive and expenditure as a positive policy intervention for reducing carbon emissions. EURO-4 countries set an example for policymakers worldwide by rebalancing their energy mix and expanding the use of cleaner alternative energy sources.

Fiscal policy, green finance, and low carbon transformation nexus: a novel study unleashing the synergistic effects of carbon reduction and pollution in China.

Under the simultaneous demands of combating environmental pollution and decreased carbon emissions, it is critical to investigate the combined effect of agricultural contamination reduction and fiscal policy carbon reduction to support and promote green agriculture and low-carbon transformation. Based on provincial panel data of 2007 to 2020 in China, this paper employs the spatial Dubin model to empirically examine the pollution reduction and carbon reduction effects of fiscal policies supporting agriculture, as well as calculating the synergistic effect of pollution reduction and carbon reduction. Our study's findings reveal that non-point source agricultural pollution and agricultural carbon emissions have a tendency of growing and subsequently reducing, such as increasing from 2007 to 2015 and decreasing from 2016 to 2020. Second, results demonstrate that agricultural carbon emissions and agricultural pollution have a positive geographical dependency in each province, and fiscal policies supporting agriculture have high-high and low-low spatial clustering features. Furthermore, fiscal policies that promote agriculture can lower local agricultural carbon emissions and pollution while also having a considerable beneficial spillover impact on neighboring provinces. According to the study findings, the fiscal policy for supporting agriculture has a negative pollution reduction impact and a positive synergistic effect, resulting in a synergistic effect of agricultural pollution reduction and carbon reduction. The outcomes of this study can serve to promote carbon-reduction measures and provide recommendations for future policy development.

Power load analysis and configuration optimization of solar thermal-PV hybrid microgrid based on building

Solar thermal power system and photovoltaic coupled system can supply electric energy based on renewable solar energy. To explore the optimal configuration of hybrid microgrid driven by solar energy and to achieve a stable and sufficient electric power supply for the distributed energy system, this paper configures a solar thermal-photovoltaic hybrid microgrid and compares the performance of several microgrids based on the power load analysis of the building. The working fluid and the microgrid configuration are determined based on the performance evaluation of single and hybrid microgrids. The effect of solar irradiation on economic and environmental performance is analyzed under the meteorological data of Xi'an and Lhasa in China. Furthermore, the configuration optimizations of a hybrid microgrid based on the actual load and basic load of the building are carried out to examine the performance of the system. Results indicate that economic and environmental performances are sensitive to solar irradiation. More than 50 % of the area of solar thermal collector can be saved for Lhasa compared with Xi'an. Solar thermal-photovoltaic hybrid microgrid with battery can decrease carbon emission and avoid dependence on the external power grid.

The effects of distributed electric propulsion on the aerodynamics of a regional aircraft wing

A study is performed to comprehensively evaluate the aerodynamic performance of a regional aircraft designed for short-haul flights, with a particular focus on the effects of distributed electric propulsion (DEP) on aerodynamic performances. This effort aligns well with a core objective of the Clean Aviation program to decrease carbon emissions within the aviation sector. To address this challenge, we propose a concept that considers incorporating electric motors to complement the conventional propulsion system of a regional aircraft wing. These electric motors have the capability to draw power from either the traditional turboprop engine or exclusively from an on-board electrical generator, providing an innovative pathway for emissions reduction. The central focus of this study revolves around a detailed examination of the aerodynamic efficacy with respect to the placement of engines within the wing. It seeks to ascertain whether optimal aerodynamic efficiency is achieved through the adoption of a greater number of smaller-diameter motors, characterized by reduced power output to the shaft. Alternatively, the study explores the prospect of employing a reduced number of motors, each possessing larger diameters and correspondingly augmented power output. While a larger motor size may intuitively suggest superiority in terms of thrust, it remains imperative to meticulously evaluate the potential influence of the associated drag factor. This research contributes to the growing body of knowledge in the realm of aviation by shedding light on the intricate interaction between aerodynamic efficiency and power output in the context of propulsion system design.

Carbon footprint tracking apps. Does feedback help reduce carbon emissions?

One of the primary issues of sustainable consumption is that many individuals are uncertain about the environmental impact of their consumption choices and, consequently, they are uncertain how to adjust their habits to consume more sustainably. Carbon footprint tracking apps (CFTAs) seek to address this information gap by continuously educating consumers on their carbon emissions. Since the effectiveness of CFTAs has not yet been investigated at all, the authors developed a CFTA to explore whether and how such applications help shape consumption behavior. Over several survey rounds, 216 participants used the application to log their consumption in four main consumption domains. The results indicate that the feedback given can help decrease carbon emissions by 23%. The impact varies by consumption category, ranging from a 12% reduction in mobility to a 35% reduction in household activities. Moreover, individual traits influence the effectiveness of CFTA usage: Perceived green self-efficacy amplifies the feedback's effect, whereas a strong green self-identity diminishes it. Based on these results, the authors guide practitioners and academics on how to employ CFTAs to foster and research sustainable consumer behavior, and the authors show how to account for inter-individual differences when endorsing such apps. Future research should probe ways to boost effectiveness in specific areas and for particular personalities.

The effect of vehicle-to-grid integration on power grid stability: A review

This article explores the promising potential of vehicle-to-grid (V2G) technology, which has attracted considerable interest due to its capacity to enhance power grid stability, decrease carbon emissions, and boost renewable energy utilization. The study begins by examining recent developments in V2G technology, then proceeds to discuss the challenges associated with integrating V2G into the power grid, and finally presents strategies to improve grid stability. Despite the potential of V2G technology, certain challenges must be addressed, including grid stress, complexities in load management, and grid imbalances. The article outlines various strategies to improve power grid stability, such as smart energy metering, time-of-use (TOU) schemes, rolling prediction decision frameworks, and an array of control approaches. Additionally, the research highlights the application of machine learning algorithms for efficient EV scheduling and power control units, voltage stabilization systems, and energy management units to ensure grid reliability. Future research should focus on developing scalable infrastructure solutions, establishing universal standards, designing optimization algorithms, and devising strategies to enhance battery life, to facilitate efficient V2G integration.

Nexus between resource policy, renewable energy policy and export diversification: Asymmetric study of environment quality towards sustainable development

This study examines the environmental performance of the United Arab Emirates (UAE) towards its sustainable development amidst its heavy endowment with natural resources and export diversification. The UAE is among the economies that depend on both export diversification and natural resources for their sustainable economic development. The two mentioned indices (export diversification and natural resources) have been proven to be among the drivers of carbon emissions. Notwithstanding, the UAE has a desert environment with nearly continuous sunlight capable of generating and boosting its renewable energy development. With this potential, it is crucial to create a green energy strategy since renewable energy sources are gaining popularity as alternatives to conventional energy sources. Natural resources and export diversification have garnered mixed reactions in their role towards environmental development. While some researchers report that they induce degradation, others suggest that they mitigate environmental damage. Also, empirically, studies have found renewables such as hydroelectricity, wind and geothermal energy capable of moderating and mitigating the negative impact of carbon emissions on the environment. In this study, data from 1990 to 2019 is analyzed using the nonlinear autoregressive distributed lag (NARDL) and dynamic ordinary least squares (DOLS) scientific methods. The variables of interest are natural resources, export diversification and renewable energy. Findings from NARDL show that export diversification, natural resources and economic growth negatively impact the environment in the UAE by inducing carbon emissions. Specifically, positive and negative shocks of export diversification, natural resources and economic growth to carbon emissions increase and decrease carbon emissions, while positive and negative shocks of renewable energy to carbon emissions decrease and increase carbon emissions. This indicates that the UAE has the potential to achieve its carbon neutrality target with a renewable energy policy.

Probabilistic scheduling of a comprehensive energy hub integrated with renewable energy sources considering the correlation between uncertain variables

The energy hub (EH) is one of the customary and efficient energy supplement systems that have been presented, so far. Nowadays, distributed energy resources (DERs), especially renewable energy sources (RESs), are widely integrated into energy systems to supply increasing energy demands. Despite decreasing carbon emissions and other usefulness of employing RESs, uncertainties originating from unpredictable renewable generation besides demand and price uncertainties, confront energy management with new challenges on both the demand and supply sides. The correlation between uncertain parameters intensifies these challenges, severely. In this paper, the optimal operation of a comprehensive EH system including multiple energy carriers is considered in which some inputs are correlated random variables. Uncertainties of the input variables are managed by the k-means clustering technique and correlations among them are handled by the Cholesky decomposition technique. This EH is integrated with compressed air energy storage (CAES) besides other storage facilities. Also, power-to-gas (P2G) as an efficient storage and energy converter is employed in the EH. Other components of the studied EH are combined heat and power (CHP), wind turbine (WT), photovoltaic (PV) cells, boiler, absorption chiller (AC), Electric chiller (EC), fuel-cell power plant (FCPP), and plug-in electric vehicles (PEVs). Considering the undeniable role of demand response programs (DRPs) in reforming load patterns, an aggregated DRP execution besides a sensitivity analysis of load participation factors of DRP is studied in this paper. The proposed optimization problem is formulized as a MILP problem and solved by the GAMS software.