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Abstract Green infrastructure (GI) is a low-carbon solution for urban rainwater management. Hydrological processes and the corresponding emissions of greenhouse gas (GHG) during rainfall events are optimized by GI when the latter is compared with a traditional urban drainage system. This study establishes an city-scale quantitative analysis, based on hydrological processes, with which to assess the contribution of GIs to low-carbon urban drainage systems and cities. The emission factor method is applied to measure GHG emissions. Attributable sources of emissions are wastewater treatment plants and wastewater and rainwater pumps. The amount and rate of change in GHG emissions were selected as indicators of the impacts of GI-based urban drainage systems and a case study was conducted in Dongying, China, based on 48 hydrological scenarios from 1970 to 2017. The amount of annual GHG emissions decreased by 3752.5 to 26238.9 tons of CO2 equivalent at an average of 10677.3 tons/a. The rate of annual GHG emissions decreased by 25.9–68.7% with an average reduction of 45.9%. An S-shaped logistic curve fit the data, indicated that annual rainfall is non-linearly and positively correlated with both the amount and rate of annual GHG emissions mitigated. The probability of benefits to GHG emissions in the 48 hydrological scenarios is analyzed based on a Pearson type III distribution curve. These findings can provide information that local authorities can use to guide policies towards their goals of applying GIs to mitigate GHG emissions in the urban drainage system.

Abstract A scientific evaluation of the energy efficiency and CO2 emission performance of the thermal power industry could not only provide valuable information for reducing energy consumption and carbon emissions but also serve as a tool to estimate the effectiveness of relevant policy reforms. Considering the opposite effects of energy conservation and carbon emission reduction on generation cost, this study respectively measures the energy and CO2 emission performance of the thermal power industries in China’s 30 provincial administrative regions during the period 2005–2012 from both static and dynamic perspectives. We implement the bootstrap method for the directional distance function to correct the possible estimate bias and test the significance of productivity changes where the weak disposability of undesirable outputs is also integrated. The empirical analysis leads to the following conclusions. The bootstrapping results could provide us with much valuable information because the initial estimates might result from sampling noise rather than reveal the real variations. In addition, some differences do exist between the energy and CO2 emission performance of China’s thermal power industry. Furthermore, technological progress is the main driving force for energy and CO2 emission productivity improvement and it works better for the former.

Smart grids, renewable energy integration, and climate change mitigation - Future electric energy systems

Abstract Smart grid is the new trend for clean, sustainable, efficient and reliable energy generation, delivery and use. To ensure stable and secure operation is essential for the smart grid, which needs effective stability analysis and control. As the smart grid has evolved through a growing scale of interconnection, increasing integration of renewable energy, widespread operation of direct current power transmission systems, and liberalization of electricity markets, the stability characteristics of it are much more complex than the past. Due to these changes, conventional stability analysis and control approaches have a series of drawbacks in terms of speed, effectiveness and economy. On the contrary, the emerging artificial intelligence (AI) techniques provide powerful and promising tools for stability analysis and control in smart grids and have attracted growing attention. This paper aims to give a comprehensive and clear picture of recent advances in this research area. First, we present a general overview of AI, including its definitions, history and state-of-the-art methodologies. And then, this paper gives a comprehensive review of its applications to security assessment, stability assessment, fault diagnosis, and stability control in smart grids. These applications have achieved impressive results. Nevertheless, we also identify some major challenges these applications face in practice: high requirements on data, imbalanced learning, interpretability of AI, difficulties in transfer learning, the robustness of AI to communication quality, and the robustness against attack or adversarial examples. Furthermore, we provide suggestions for potential important future investigation directions to overcome these challenges and bridge the gap between research and practice.

Abstract The current power systems are undergoing a rapid transition towards their more active, flexible, and intelligent counterpart smart grid, which brings about tremendous challenges in many domains, e.g., integration of various distributed renewable energy sources, cyberspace security, demand-side management, and decision-making of system planning and operation. The fulfillment of advanced functionalities in the smart grid firmly relies on the underlying information and communication infrastructure, and the efficient handling of a massive amount of data generated from various sources, e.g., smart meters, phasor measurement units, and various forms of sensors. In this paper, a comprehensive survey of over 200 recent publications is conducted to review the state-of-the-art practices and proposals of machine learning techniques and discuss the trend in a wide range of smart grid application domains. This study demonstrates the increasing interest and rapid expansion in the use of machine learning techniques to successfully address the technical challenges of the smart grid from various aspects. It is also revealed that some issues still remain open and worth further research efforts, such as the high-performance data processing and analysis for intelligent decision-making in large-scale complex multi-energy systems, lightweight machine learning-based solutions, and so forth. Moreover, the future perspectives of utilizing advanced computing and communication technologies, e.g., edge computing, ubiquitous internet of things and 5G wireless networks, in the smart grid are also highlighted. To the best of our knowledge, this is the first review of machine learning-driven solutions covering almost all the smart grid application domains. Machine learning will be one of the major drivers of future smart electric power systems, and this study can provide a preliminary foundation for further exploration and development of related knowledge and insights.

Low gas wellhead prices in North America have put its shale gas industry under high competitive pressure. Rapid technology innovation can help companies to improve the economic performance of shale gas fields. Cash flow models are paramount for setting effective production and technology innovation targets to achieve positive returns on investment in all global shale gas plays. Future cash flow of a well (or cluster of wells) may either improve further or deteriorate, depending on: (1) the regional volatility in gas prices at the wellhead – which must pay for the gas resource extraction, and (2) the cost and effectiveness of the well technology used. Gas price is an externality and cannot be controlled by individual companies, but well technology cost can be reduced while improving production output. We assume two plausible scenarios for well technology innovation and model the return on investment while checking against sensitivity to gas price volatility. It appears well technology innovation – if paced fast enough – can fully redeem the negative impact of gas price decline on shale well profits, and the required rates are quantified in our sensitivity analysis.

Abstract The Power Grid balance requires the organization of multiple supply plants to match the electricity demand of the users’, starting from the most accurate forecasts available and with the need of continuous adjustments based on the actual demand profile. The power dispatching is currently based on a day-ahead wholesale market, which fixes an hourly price based on the offers and bids of producers and buyers. In this paper an alternative approach is proposed, with the integration of performance indicators of the electricity generation plants. Optimization algorithms at the base of Smart Grids operation could support a multi-objective approach that overcomes a simple economic optimum. The aspects that have been considered are the renewable energy share, the primary energy consumption, the global emissions (i.e. CO2) and the local emissions (i.e. NOX, CO, PM, etc.). A precise calculation of these performance indicators is proposed for three real natural gas combined cycles, and the results are compared with the average data for the electricity produced in Italy and supplied to the Power Grid. The strong variability of those indicators highlights the importance of performing detailed analyses with up-to-date actual operation data, as the evolution towards sustainability targets in Smart Grids require an integrated approach.

Abstract Although aspects of long-term planning are commonly taken into account in current analyses of bioenergy policy scenarios, representations of the bioenergy supply chain are often spatially aggregated. Multiple questions such as where, when, and how bioenergy is deployed have thus not been sufficiently addressed within a single modeling framework. Moreover, techno-economic models that can capture the dependencies of bioenergy supply chain variables among end-use sectors still need to be explored. The present research connects these gaps by presenting the development of a spatio-temporal techno-economic optimization model for cross-sectoral bioenergy policy evaluations under high spatial resolution and long-term temporal resolution. The research recognizes not only the need for energy decarbonization, but also the importance of improving resource efficiency in the palm oil industry, in this case, Malaysia’s palm oil bioenergy industry. The findings highlight the need for multi-sectoral collaboration between the energy sectors to deliver cost-optimal energy decarbonization at the national scale. This is represented by the substitution of up to 30%, 27%, and 12% of the energy demands in the power, heat, and transport sectors with bioenergy, respectively. The conflict between policy targets was also highlighted, namely, that new policies prioritizing bioenergy in the power and transport sectors reduce CO2 more effectively than policies targeting CO2 reduction alone, however, requiring up to 37% more cost in meeting the CO2 reduction commitment. The findings also outline the requirement of co-locating bioenergy production facilities with the existing facilities (e.g., agricultural mills, coal plants) and extending the existing infrastructure network to deliver the bioenergy capacities needed to meet the policy targets.

Abstract This paper endeavours to study the changes in the distribution of aggregate employment variation among manufacturing industries. Within the structure developed, estimates of the effects of energy on the distribution of employment and on the distribution of changes in employment between various manufacturing industries are made. The evidence indicates that changes in energy have had a significant impact on employment patterns. Energy has had the effect of decreasing the share of projected employment and increasing vulnerability to cyclical changes in employment in industries with a high relative dependence on energy.