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This paper reviews current progress and future challenges of digital technology applications for energy system transition in the context of net-zero. A list of case studies for such digitization enabled optimal design and operation of energy systems at various temporal and spatial scales are reviewed in the paper, including model predictive control, enterprise-wide optimization, eco-industrial park data management, and smart city. The key technological innovations across these applications, such as virtual representation of physical entities, ontological knowledge base, data-driven high dimensional surrogate model based parameterization are also inspected in the paper. Future challenges in terms of data privacy and security are also discussed as potential barriers for digitalization enabled net-zero energy system transition.

Heating decarbonization is a major challenge for China to meet its 2060 carbon neutral commitment, yet most existing studies on China's carbon neutrality focus on supply side (e.g., grid decarbonization, zero-carbon fuel) rather than demand side (e.g., heating and cooling in buildings and industry). In terms of end use energy consumption, heating and cooling accounts for 50% of the total energy consumption, and heat pumps would be an effective driver for heating decarbonization along with the decarbonization on power generation side. Previous study has discussed the underestimated role of the heat pump in achieving China's goal of carbon neutrality by 2060. In this paper, various investigation and assessments on heat pumps from research to applications are presented. The maximum decarbonization potential from heat pump in a carbon neutral China future could reach around 1532Mton and 670Mton for buildings and industrial heating respectively, which show nearly 2 billion tons CO2 emission reduction, 20% current CO2 emission in China. Moreover, a region-specific technology roadmap for heat pump development in China is suggested. With collaborated efforts from government incentive, technology R&D, and market regulation, heat pump could play a significant role in China's 2060 carbon neutrality.

Growing national decarbonization commitments require rapid and deep reductions of carbon dioxide emissions from existing fossil-fuel power plants. Although retrofitting existing plants with carbon capture and storage or biomass has been discussed extensively, yet such options have failed to provide evident emission reductions at a global scale so far. Assessments of decarbonization technologies tend to focus on one specific option but omit its interactions with competing technologies and related sectors (e.g., water, food, and land use). Energy system models could mimic such inter-technological and inter-sectoral competition but often aggregate plant-level parameters without validation, as well as fleet-level inputs with large variability and uncertainty. To enhance the accuracy and reliability of top-down optimization models, bottom-up plant-level experience accumulation is of vital importance. Identifying sweet spots for plant-level pilot projects, overcoming the technical, financial, and social obstacles of early large-scale demonstration projects, incorporating equity into the transition, propagating the plant-level potential to generate fleet-level impacts represent some key complexity of existing fossil-fuel power plant decarbonization challenges that imposes the need for a serious re-evaluation of existing fossil fuel power plant abatement in energy transition.

Summary A growing number of governments are pledging to achieve net-zero greenhouse gas emissions by mid-century. Despite such ambitions, realized emissions reductions continue to fall alarmingly short of modeled energy transition pathways for achieving net-zero. This gap is largely a result of the difficulty of realistically modeling all the techno-economic and sociopolitical capabilities that are required to deliver actual emissions reductions. This limitation of models suggests the need for an energy-systems analytical framework that goes well beyond energy-system modeling in order to close the gap between ambition and reality. Toward that end, we propose the Emissions-Sustainability-Governance-Operation (ESGO) framework for structured assessment and transparent communication of national capabilities and realization. We illustrate the critical role of energy modeling in ESGO using recent net-zero modeling studies for the world's two largest emitters, China and the United States. This illustration leads to recommendations for improvements to energy-system modeling to enable more productive ESGO implementation.

A growing number of governments and companies are pledging net-zero emissions by 2050. For the US as a whole to achieve this requires eliminating or offsetting today's emission of ~6 billion tCO2e/year. There is a dearth of analysis for understanding requirements, costs, and impacts of this transition. The goal of this study is to help fill this gap by providing insights at visceral, human scales of how the nation will look following a pathway to net-zero and the localized benefits, costs, and impacts for different industries, professions, and communities. The analysis aims to inform debates on public and corporate policies needed to achieve net-zero, but specific policy recommendations are not offered.Energy service demands projected to 2050 by the EIA for 14 regions across the continental US provide the starting point for modeling. Five different pathways are constructed for meeting these demands by varying exogenously applied constraints to create the different pathways.End-use technologies to meet service demands are exogenously specified in 5-year time steps to determine final energy demands that must be delivered by the energy supply system. Pathways to net-zero emissions by 2050 are constructed by finding the energy supply mix that minimizes the 30-year NPV of total energy-system costs, subject to exogenous constraints. The model has perfect foresight and seamless integration between all sectors. These modeling results are “downscaled” to state or sub-state geographies to quantify local plant and infrastructure investments, construction activities, land-use, jobs, and health impacts, 2020 - 2050.

Modeling and optimization of a large-scale urban energy-water nexus system with sufficient spatial resolutions is a complex challenge. By proper clustering technique, a large-scale problem could possibly be divided into small ones with high spatial resolution and accuracy. Existing literature tends to lower the complexity of large-scale urban energy system problem by accumulating demand profiles on the spatial dimension. This study proposes a flexible clustering approach based on density clustering method with combined index assessment process. The flexible approach considers not only the spatial dimensions but also the complementarity effect of different demand profile and control the computational time of system design and optimization. The approach can increase the clustering flexibility by providing more clustering options than conventional method, take advantages of complementarity effect to further improve the system economic performance and control the solving time in an acceptable range. The proposed approach is evaluated by a case study of a new business district in Shanghai, China with a proposed future energy-water nexus system. After three combined index assessment, 45 new clustering maps are generated by the flexible clustering approach and the final optimal solution obtained by the proposed approach can further obtain 6.74% cost savings compared with conventional clustering approach.

The decarbonisation of energy provision is key to managing global greenhouse gas emissions and hence mitigating climate change. Digital technologies such as big data, machine learning, and the Internet of Things are receiving more and more attention as they can aid the decarbonisation process while requiring limited investments. The orchestration of these novel technologies, so-called cyber-physical systems (CPS), provides further, synergetic effects that increase efficiency of energy provision and industrial production, thereby optimising economic feasibility and environmental impact. This comprehensive review article assesses the current as well as the potential impact of digital technologies within CPS on the decarbonisation of energy systems. Ad hoc calculation for selected applications of CPS and its subsystems estimates not only the economic impact but also the emission reduction potential. This assessment clearly shows that digitalisation of energy systems using CPS completely alters the marginal abatement cost curve (MACC) and creates novel pathways for the transition to a low-carbon energy system. Moreover, the assessment concludes that when CPS are combined with artificial intelligence (AI), decarbonisation could potentially progress at an unforeseeable pace while introducing unpredictable and potentially existential risks. Therefore, the impact of intelligent CPS on systemic resilience and energy security is discussed and policy recommendations are deducted. The assessment shows that the potential benefits clearly outweigh the latent risks as long as these are managed by policy makers.

This paper investigates an optimal scheduling method for the operation of combined cycle gas turbines (CCGT). The objective is to minimize the CO2 emissions while supplying both electrical and thermal loads. The paper adopts a detailed model of the units in order to relate the heat and power outputs. The grid constraints as well as system losses are considered for both the electrical and thermal systems. Finally, the optimal power dispatch lies on the hybridization of a Mixed Integer Linear Programing (MILP) scheduling with a greedy search method. Different sets of simulations are run for a small 5-bus test case and a larger model of Jurong Island in Singapore. Several load levels are considered for the heat demand and the impact of the steam pipe capacities is highlighted.

Understanding the underlying dynamics of building energy consumption is the very first step towards energy saving in building sector; as a powerful tool for knowledge discovery, data mining is being applied to this domain more and more frequently. However, most of previous researchers focus on model development during the pipeline of data mining, with feature engineering simply being overlooked. To fill this gap, three different feature engineering approaches, namely exploratory data analysis (EDA) as a feature visualization method, random forest (RF) as a feature selection method and principal component analysis (PCA) as a feature extraction method, are investigated in the paper. These feature engineering methods are tested with a building energy consumption dataset with 124 features, which describe the building physics, weather condition, and occupant behavior. The 124 features are analyzed and ranked in this paper. It is found that although feature importance depends on specific machine learning model, yet certain features will always dominate the feature space. The outcome of this study favors the usage of effective yet computationally cheap feature engineering methods such as EDA; for other building energy data mining problems, the method proposed in this study still holds important implications since it provides a starting point where efficient feature engineering and machine learning models could be further developed.

In this paper, we develop a skeletal ontology for eco-industrial parks. A top-down conceptual framework including five operating levels (unit operations, processes, plants, industrial resource networks and eco-industrial parks) is employed to guide the design of the ontology structure. The detailed ontological representation of each level is realized through adapting and extending OntoCAPE, an ontology of the chemical engineering domain. Based on the proposed ontology, a framework for distributed information management is proposed for eco-industrial parks. As an example, this ontology is used to create a knowledge base for Jurong Island, an industrial park in Singapore. Its potential uses in supporting process modeling and optimization and facilitating industrial symbiosis are also discussed in the paper.

An ontology-based approach for Eco-Industrial Park (EIP) knowledge management is proposed in this paper. The designed ontology in this study is formalized conceptualization of EIP. Based on such an ontological representation, a Knowledge-Based System (KBS) for EIP energy management named J-Park Simulator (JPS) is developed. By applying JPS to the solution of EIP waste heat utilization problem, the results of this study show that ontology is a powerful tool for knowledge management of complex systems such as EIP. The ontology-based approach can increase knowledge interoperability between different companies in EIP. The ontology-based approach can also allow intelligent decision making by using disparate data from remote databases, which implies the possibility of self-optimization without human intervention scenario of Internet of Things (IoT). It is shown through this study that KBS can bridge the communication gaps between different companies in EIP, sequentially more potential Industrial Symbiosis (IS) links can be established to improve the overall energy efficiency of the whole EIP.

As an important way to increase industrial energy efficiency, Waste Heat to Power (WHP) technologies have been gaining popularity in recent years. In order to appraise the market potential of WHP technologies in Southeast Asia, a techno-economic assessment for WHP technologies is conducted in this paper. The technical and economic market potential of WHP in Southeast Asia is estimated to be 1788MW and 1188MW respectively. The main market drivers and barriers for WHP market expansion in Southeast Asia are also analyzed. Given the fact that WHP is a far cheaper power generation technology as compared with traditional and renewable power generation, the WHP market is expected to increase fast in the coming years. Mounting electricity price from grid, government emissions regulations and subsidies, the integration of WHP products with original equipment manufacturer, capital cost reduction induced by technology development are identified as the key drivers for the market growth. The above arguments are proofed through the analysis of a power plant WHP project in Southeast Asia.

With the development of digital technologies, especially big data analytics, digital innovations are taking root in various industries, including energy sector. Particularly, urban energy system is also experiencing digital transition; such digital transition not only offers new business models commercially, but also brings new research problems scientifically. The new capabilities enabled by these digital technologies are reshaping the generation, transmission, consumption and storage sections in the urban energy system, sequentially the traditional way of how urban energy system is designed and operated should be reexamined. Starting from here, there have been many studies regarding how various digital technologies can be applied all along the urban energy system value chain; these studies range from individuals’ energy consumption pattern characterization by using customer behavior data in smart home, to complex data-driven planning of regional scale energy system. More specifically, numerous computational models have been proposed by the scientific community to mimic the dynamics of various components at various levels in the urban energy system. However, the potential benefits of applying these numerical models are somehow underestimated; we believe there are still several gaps from numerical modeling to computational intelligence which need to be bridged. In such a context, in this paper we strive to present a systematic review on the status of urban energy system related digital innovations as well as prospective outlook on the future application of such digital technologies. Through the study of this paper, we hope to identify several key points where digitalization should be prioritized in urban energy system, picture a roadmap towards future digital technology enabled intelligent urban energy system, and finally points out the research gaps that need to be fulfilled over there.

This paper presents new insights into constructing a cyber-infrastructure system, called J-Park Simulator, for the resource and energy management of eco-industrial parks. The concept of Industry 4.0 is applied to develop the system. Advanced information modeling and managing technology – ontology technology is introduced to build the knowledge base for J-Park Simulator, which delivers a decentralized information managing system. The system allows information of the individual entities been managed locally by the entity themselves, in the meanwhile facilitates information sharing via the Internet and/or intranet. A bottom-up object-oriented approach is adopted to partition and organize information of the complex eco-industrial park. The proposed work addresses key issues in computer-aided design and operation of eco-industrial parks in the context of realizing Industry 4.0.

Based on share of energy, materials, resources and information, Eco Industrial Park (EIP) has become a popular form of industry cluster. Waste Heat Recovery (WHR) in EIP can significantly increase the total energy efficiency of the whole park, meanwhile reducing its greenhouse gas emission. The current paper proposes a methodology to assess the opportunities of WHR in EIP at park level. Four different steps are included in this methodology. The first step is identification of waste heat source plants and sink plants in EIP; the second step is the establishment of the waste heat transportation system; the third step is a Single-Objective Optimization Problem (SOOP); the fourth step is Multi-Objective Optimization Problem (MOOP). An EIP on Jurong Island Singapore comprising of five plants and two communities is used as a case study to demonstrate the capability of this methodology. Two different operation modes for the EIP are considered: with continuous waste heat and with discontinuous waste heat over time. The first scenario shows that SOOP and MOOP will deliver different WHR networks; the second scenario shows that waste heat discontinuity has great influence on the optimization of the WHR network.