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Energy's centrality to life, work, and progress underscores its indispensable role in modern society. However, burgeoning energy consumption and finite resources have prompted concerns regarding resource sustainability. Consequently, renewable energy sources have gained prominence as alternatives, integral to societal advancement. Therefore, with the increase in the price of fossil fuels and its limitations and the attention to renewable energies, in this research, we decided to study the implementation processes of the development of this type of energy about possible future scenarios and possible implementation policies at the time of each scenario. Let's pay Considering the dynamic complexities of this issue over time and the involvement of government sectors in policy discussions and private sectors in the implementation and participation of these two groups simultaneously, using dynamic system modeling to design a dynamic model that economic aspects, technical, political and environmental aspects should be evaluated simultaneously in a causal-disability manner. To implement this model, two scenarios of government support and non-support for this industry were taken into consideration, and in each scenario two policies of workforce training and technology import for the use of private sector managers and in the best scenario two policies of tax exemption and reduction of technology import tariff for Government decision-making was reviewed. The results showed that when this industry enjoys government support, the speed of development of renewable energy projects can be greatly increased by importing equipment. Without governmental backing, expedited progress is achievable through skilled labor training and employment. Tax exemptions are the most effective policy tool for stimulating renewable energy project processes, production, and sales. The findings underscore the pivotal role of strategic governmental intervention in facilitating renewable energy development, which is essential for meeting energy demands while mitigating environmental impact. • Identification of variables influencing the process of renewable energy in a novel system dynamic (SD) • Simultaneous goal-oriented analysis by establishing connection between strategies and variables with governmental scenarios • Utilization of the SD to comprehensively examine the interrelationships between variables and dynamically analyze them • Implementation of policies within the designed model frameworkto determine policy implications and managerial applications. [ABSTRACT FROM AUTHOR]

With the shift towards low-carbon economies, energy security has emerged as an increasingly vital concern for governments worldwide, particularly in recent years, because of ongoing conflicts and increased volatility in the international energy market. Both laws and policies are essential tools for mitigating climate change challenges; however, its role in energy security remains unexplored. Drawing on climate-related legislative activities in 127 countries from 2000 to 2021, this study examines the short- and long-term impact of climate laws and policies on energy security performance (ESP). The empirical results demonstrate that both recent and longstanding climate laws and policies have enhanced the national ESP on a global scale. Furthermore, we find that laws outperform policies regarding energy security improvement. Additional analyses indicate that the impact of climate laws and policies on ESP is more effective across high- and upper-middle-income countries, and less effective in low-income countries, especially those in Africa. Our findings highlight the significance of climate legislative efforts and provide important implications for global governments in securing their green transition. • This study identifies the positive relationship between short- and long-term climate legislation and energy security. • The impact is particularly pronounced in countries with strong legal frameworks. • Laws are more effective than policies in enhancing energy security. • Climate legislations are more effectively in high- and upper-middle-income countries. • The impact of climate legislation on energy security varies significantly across global regions, with developed areas performing best. [ABSTRACT FROM AUTHOR]

Residential heat pumps are a critical energy transition technology, but their installed costs are often much higher than incumbent technologies – imposing a policy imperative, in the UK and elsewhere, to reduce deployment costs. Here, we systematically review historic and forecast UK and international data on heat pump installed costs. Installed costs are reported using three main metrics: Single Point Cost, Experience Rates and Percentage Cost Reduction. Experience Rates offer the highest quality metric, but there are few available studies, particularly for non-equipment costs. Single Point Cost is the most widely reported metric, but conflates diverse technologies and contexts. Percentage Cost Reduction is a relatively common forecasting metric, but may lack rigour. Historic data suggests that there has been no significant reduction in the average installed cost of heat pumps over the past decade in the UK, while modest cost reductions were seen internationally. At times, particular countries have aligned deployment growth with significant cost reduction. Forecast data suggests reductions in installed costs of 20–25 % between 2020 and 2030 in the UK – significant, but relatively modest compared to current UK policy goals. We conclude that heat pump deployment policy support should be resilient to fluctuations in installed costs, as the market grows. • We systematically review historic and forecast domestic heat pump installed costs. • High quality installed cost data is scarce, both in the UK and internationally. • No reduction found in average installed costs over the past decade in the UK. • Forecasts suggest significant but relatively moderate cost reductions by 2030. • Policy support should be resilient to fluctuations in installed costs over time. [ABSTRACT FROM AUTHOR]

In the context of the expanding diversity of energy demands, an increasing number of heterogeneous Multi-energy Microgrids (MEMGs) are engaging in the collaborative framework of the Multi-energy Multi-microgrid System (MEMMG). However, following this trend, the existing centralized Integrated Energy Management System (IEMS) control strategy is unreliable for future energy systems, characterized by more complex optimization control and a flexible system structure. This paper introduces a hierarchical Multi-agent Deep Reinforcement Learning (HMADRL) approach for distributed IEMS in MEMMG. Firstly, by employing a hierarchical approach, this method simplifies control complexity by segmenting the overarching control challenge into tasks of collaborative planning and action control, which are distributed across varied multi-agent scenes. Considering both macro and microeconomic factors, alongside carbon emissions, the optimal operation of MEMMG is realized through a bottom-up edge multi-agent control approach, in contrast to traditional top-down centralized methods. Secondly, in the phase of the inter-MEMG collaborative strategy, the Centralized Training Decentralized Execution (CTDE) framework is adopted to overcome the problems of unstable training environments and large-scale agent training, and each heterogeneous microgrid can develop local strategies independently with the assurance that their internal data will not be overly exposed. Thirdly, within each MEMG, the Trust-Region (TR) model is introduced for multi-agent action control, adeptly addressing the effects of mutual exclusion in decision-making time series. Simultaneously, an initialized Hot Experience Pool (HEP) is implemented, efficiently reducing exploration in complex, high-dimensional spaces. Finally, the off-time agent model is integrated into the HMADRL environment and undergoes secondary training based on real interactions, thereby deriving the optimal energy management policy. The proposed method markedly reduces reliance on exact physical modeling systems. Comparative simulations validate the proposed control scheme's efficacy. • A collaborative multi-energy multi-microgrid optimization model based on hierarchical multi-agent deep reinforcement learning is established. • Incorporate the collaborative strategies between multiple microgrids and the optimal of multiple energy systems within each microgrid. • A multi-agent exploration mechanism combining the trust region module and hot experience pool module is proposed. • A multi-agent retraining mechanism for adaptive structural flexibility in multiple microgrid systems is proposed. [ABSTRACT FROM AUTHOR]

This study developed a methodological approach for long-term electricity demand forecasting and applied it to the electricity demand in Cuba, which is crucial for transitioning from a fossil fuel-dependent system to renewable energy sources. The methodology employs enhanced complete ensemble empirical mode decomposition with adaptive noise (ECEEMDAN) applied for obtaining long-term trends from historical electricity usage data decomposition, combined with a long short-term memory (LSTM) deep learning model for prediction. Comprehensive datasets, including historical electricity consumption, economic indicators, and demographic data, are utilized in the analysis. Monte Carlo simulations, then, are integrated to address uncertainties in prediction and explore 50 different scenarios of future electricity demand. The study forecasts varying scenarios for the energy demand of Cuba by 2050, with the extreme low scenario projecting a decrease of up to 7.9% compared to the 2019 level. This research offers a groundbreaking framework specifically designed to aid Cuba's energy sector stakeholders in informed decision-making during this critical energy transition. The adaptability of the methodology makes it applicable for long-term projections in various sectors, offering a reliable tool for global decision makers. • Novel integration of ECEEMDAN with LSTM for enhanced electricity demand forecasting. • Incorporates uncertainty modeling for realistic long-term electricity demand scenarios. • Employs a multivariate dataset for comprehensive energy context analysis. • Provides strategic insights for energy planning and policy in transitional contexts. • Addresses crucial energy transition challenges in developing countries like Cuba. [ABSTRACT FROM AUTHOR]

New energy resources compromising intermittent and fluctuating natures have been integrated into the power grid on a large scale, and thermal power units are obliged to promote the depth of deep peak shaving and flexible response-ability to cope with this new scenario. The safety, flexibility, and high efficiency of coal-fired units are the ultimate goals of its transformation, and there is a mutual influence and mutual restriction relationship among these indicators. To exploit the flexibility of thermal power units, take into account efficiency and safety, and play a supporting role in the new power system, a control strategy that weighs various indicators needs to be developed. Based on heat flow model and dynamic state space model, and updated matrix in real time, the key process parameters affecting the flexibility of heat transfer can be fully characterized. By using the observed state and calculation, the load response index and the energy efficiency index are optimized under the boundary constraint. The results show that the multi-objective optimization methods can avoid over regulation, reduce the flue gas flow by 3.23%–3.59%, and reduce the fluctuation of state quantity under the premise of satisfying the load response rate, about 2.32%–2.52% away from the safety boundary of the design temperature parameter to achieve the flexibility of frequency modulation, high efficiency of energy transmission and operation safety. • Conduct characteristics of thermodynamic process based on heat current model of thermal power unit. • Obtain system operating status and parameters through energy state observation and actual data of the unit. • Characterize energy efficiency of heat transfer process base on entransy theory. • Construct objective function to satisfy security, energy efficiency and flexibility. • Acquire the standardized optimization strategy of migrating the maximum frequency regulation capability. [ABSTRACT FROM AUTHOR]

Large-scale energy storage is highlighted as key for decarbonisation, yet there lacks consensus on the optimal types of storage required. Seasonal Thermal Energy Storage (STES) is an established feature of effective energy transitions in some countries, such as Denmark and the Netherlands, but it remains a marginal technology in the UK. This paper contributes to understanding how STES may develop in the UK, and the mechanisms and challenges for widescale STES deployment, through case studies of five local heat projects. Based on an analysis of both the emergence and absence of STES in local energy projects, we identify key factors enabling and inhibiting STES deployment, intersecting technological, economic, organisational and governance. We conclude that the limited extent of STES in the UK reflects the inconsistent alignment of these factors in local level heat projects, and disconnects between national energy policy, local energy planning, and project-level contingencies. Our findings suggest that without resolving these tensions, the UK heat transition will continue to be haphazard rather than strategic. While the situatedness of low carbon heat supply, storage and demand suggests moves towards more local energy governance, this needs to be accompanied by multi-level alignment and capacity building. • Seasonal Thermal Energy Storage (STES) is marginal in the UK, deployed in some local heat projects, but absent in most. • STES emergence relies on novel economic, business, ownership and operational models, and their alignment. • These factors relies on local actors and capacities, yet there is weak support for these in national energy policy. • STES and renewable heat emergence is inconsistent and overly contingent on local factors. • Studying STES emergence shows the situated contingencies of energy transitions. [ABSTRACT FROM AUTHOR]

The gas crisis of 2022 put pressure on electricity prices in Europe, prompting the Swiss government to launch a national energy-saving campaign. To effectively quantify potential savings and guide timely decision-making, this campaign called for rigorous near-real-time modeling of changes in electricity consumption habits. The proposed approach estimates national electricity consumption at an hourly resolution across three consumer categories using thousands of streamed smart-meter load curves. These curves are aggregated to produce a national consumption estimate using scaling factors that account for differences among Swiss distributors. These factors are derived by regressing historical annual consumption against public socio-economic variables. The obtained national load curve is adjusted for the influence of weather conditions, the calendar and global trends, in order to compare different periods with a reference scenario. Such external effects are modeled with splines using Generalized Additive Models, trained on a 5-year dataset, to precisely measure each contribution on the national consumption and evaluate the consumers' response to the saving plan. The results indicate a reduction of approximately 4.8% of the adjusted electricity consumption during winter 2022–2023, equivalent to an average monthly savings of 246 GWh, distributed across residential, service, and industrial sectors. • Swiss electricity demand is estimated from load curves and public socioeconomic data. • A 4.8% energy saving in winter 2022 is observed compared to the pre-COVID-19 period. • Model reveals differences in the savings behavior of three consumer groups over time. • The method enables real-time monitoring and savings opportunities for practitioners. • The scaling method can be easily adapted for use in different regions or countries. [ABSTRACT FROM AUTHOR]

Agrivoltaic systems are promising solutions to address global food and energy challenges by combining agriculture and solar photovoltaics. However, the lack of appropriate regulations to define and guide their implementation constrains the growth of agrivoltaic systems in the U.S. This study uses a shading and radiation tool to evaluate an existing agrivoltaic incentive program that defines agrivoltaic designs based on shading reduction limits and panel height requirements. Our analysis indicates that structuring policy requirements around shading, and not light availability, may lead to an underestimation of crop suitability by neglecting diffuse radiation. Furthermore, we show that agrivoltaic systems can avoid increasing panel height if policy acknowledges use-case scenarios where farming only occurs between rows. In light of these insights, this study proposes two key policy recommendations: (1) benchmark crop suitability based on daily light integral (DLI) requirements for a shade-intolerant crop selected to represent a prevalent crop in the region, and (2) include an incentive scenario where agriculture is only required between rows. These two recommendations can potentially incentivize designs that are practical and closer in cost to conventional solar farms, thereby accelerating the adoption of cost-effective agrivoltaic systems. • Challenges and opportunities are evaluated for an existing incentive program. • New policy recommendations are presented to encourage cost-effective designs. • A shading and radiation tool is used to compare existing and proposed criteria. • Both fixed tilt and tracking systems are investigated for agrivoltaic systems. • Proposed criteria allow for lower material usage than the existing ones. [ABSTRACT FROM AUTHOR]

As Europe faces the dual challenge of decarbonizing its energy sector while ensuring energy security, REPowerEU reinforces the ambitious targets outlined in the Green Deal, complementing the "Fit for 55" package. This work analyses sustainable biomethane production in Europe, with a specific focus on Italy, and its conversion into sustainable fuels. Under the light of existing policy targets and regulatory instruments, the study explores innovative and sustainable agro-energy chains, with biomethane as energy vector for producing sustainable aviation fuels (SAF) and methanol for maritime. Decentralized biomass digestion and centralized biomethane conversion in refinery are combined, considering three key Gas-to-Liquid (GTL) pathways: Fischer-Tropsch and methanol synthesis, and gas fermentation/alcohol-to-jet. A simulation model was used to estimate the performances of these routes, providing insights on process yields and energy balances. Additionally, preliminary investment cost estimates are considered by reviewing existing references and extrapolating unit-specific cost data. As by 2030 biomethane in Europe could supply 38 bcm, the routes analysed could cover 4 to 11% of jet fuel demand, and from 25 to 56% of maritime fuel needs. By 2050, with a potential EU biomethane supply of 91 bcm, these pathways might meet 9–25% of jet fuel demand, and 48–105% of maritime fuel demand. In Italy, by 2030, 5.6 bcm of biomethane could enable these pathways to meet 7–18% of jet fuel demand, and a remarkable 69–152% of maritime fuel demand. By 2050, with 8.2 bcm of biomethane, these routes might cover 8–22% of jet fuel demand, and potentially satisfy 91–198% of maritime fuel demand. Moreover, these pathways would also yield other added-value by-products (e.g. naphtha, diesel, waxes, hydrogen, gasoline), which should be considered in comparing them. The average investment costs for each route were estimated at 791,970 USD/t/day for the Fischer-Tropsch based GTL plant, 130,275 USD/t/day for the methanol-based GTL plant, and 669,740 USD/t/day for the GTL plant involving gas fermentation/alcohol-to-jet conversion. [Display omitted] • Decentralized biomethane-centralized Gas-to-Liquid can decarbonize EU aviation and maritime sectors. • By 2030, GTL routes may satisfy 4–11% jet fuel and 25–56% of maritime fuel EU demands. • By 2050, 9–25% of jet and 48–105% of maritime fuel EU needs could be covered by these paths. • 2030 Italian jet fuel (7–18%) and maritime (69–152%) demands could be met by these routes. • At 2050, 8–22% of jet fuel and 91–198% of maritime needs could be satisfied for Italy. [ABSTRACT FROM AUTHOR]

In order to increase the economic benefits of residential photovoltaic (PV), many countries have considered diverse compensation policies for surplus energy, such as fixed feed-in tariff, time-varying feed-in tariff, and net metering, where the surplus energy is offset against consumption. More recently, there has been a growing interest in schemes that allow end-users to trade surplus energy via peer-to-peer (P2P) electricity trading, rather than relying on government incentives. This study conducts a quantitative analysis of on how the introduction of P2P trading affects the benefits of each stakeholder, offering a guide for policy adoption. Firstly, this study devises a P2P trading mechanism based on credit auctions tailored for residential, while considering the application of the increasing block rate tariff often applied to residential customers in the real world, and assesses its benefits in comparison to the current net metering scheme. Multiple case studies were undertaken, encompassing various scenarios, including the level of purchase price of utility for uncleared auction offers, the proportion of prosumers participating in the trading network, and the level of capacity of PV. This study shows that the P2P trading does not always yield positive benefits for prosumers compared to the prior policy. Nevertheless, this study identifies specific conditions under which the P2P trading can stimulate residential PV installations. • Credit P2P trading is modeled under the practical tariff system. • It is analyzed whether the P2P trading is a more efficient compared to the net metering. • Case studies are conducted to identify the conditions where prosumers obtain positive benefits. • The P2P trading does not always beneficial for prosumers over the net metering scheme. [ABSTRACT FROM AUTHOR]

The transition of the European iron and steel industry (ISI) towards low-carbon manufacturing is crucial for the European Union (EU)'s 2050 climate neutrality objective. One emerging solution is electrification by using hydrogen (H 2) as iron ore reductant, which increases specific electricity use per tonne of steel up to 35 times compared to the conventional, most adopted coal-based technology. This study develops three scenarios, encompassing a moderate to an accelerated ISI transition, to evaluate the impact of the ISI decarbonisation on the power system CO 2 emissions, generation mix and volume, and marginal prices in 2030. The study first estimates future electricity and H 2 demand by considering country-specific technologies deployment and energy intensities. Then, these estimates serves as input to the model METIS to simulate European power system operations through a unit commitment and economic dispatch problem. The study shows that the power system can accommodate a transition of the ISI that substitutes 28% of the coal-based production with low carbon technologies, mainly based on H 2. This leads to a 25% reduction in direct CO 2 emissions and a demand increase of 20 TWh of electricity and 40 TWh HHV of H 2. Furthermore, a 50% reduction in indirect power system emissions is achieved, compared to 2018, thanks to the substantial renewable power capacity deployment foreseen in the coming years. The study also demonstrates that a reduction of indirect CO 2 emissions by over 85% can be achieved by deploying 1.2 and 2.7 GW of renewable power generators, and 200 and 400 MW of electrolyser capacity for each million tonne of steel produced annually with low-carbon technologies. Additional renewable capacity that ensures green steel production is also key to maintaining stable electricity prices. • The interactions between an electrified steel sector and the power system. • Electricity and hydrogen demands are derived from latest projects development. • Energy intensities are calculated at highly granular level. • The targets of the Fit-for-55 package allow an advanced steel decarbonisation. • Additional renewable technologies stabilises electricity marginal prices. [ABSTRACT FROM AUTHOR]

As power systems around the world are rapidly evolving to achieve decarbonization objectives, it is crucial that power system planners and operators use appropriate models and tools to analyze and address the associated challenges. This paper provides a detailed overview of the properties of power market models in the context of the clean energy transition. We review common power market model methodologies, their readiness for low- and zero‑carbon grids, and new power market trends. Based on the review, we suggest model improvements and new designs to increase modeling capabilities for future grids. The paper highlights key modeling concepts related to power system flexibility, with a particular focus on hydropower and energy storage, as well as the representation of grid services, price formation, temporal structure, and the importance of uncertainty. We find that a changing resource mix, market restructuring, and growing price uncertainty require more precise modeling techniques to adequately capture the new technology constraints and the dynamics of future power markets. In particular, models must adequately represent resource opportunity costs, multi-horizon flexibility, and energy storage capabilities across the full range of grid services. Moreover, at the system level, it is increasingly important to consider sub-hourly time resolution, enhanced uncertainty representation, and introduce co-optimization for dual market clearing of energy and grid services. Likewise, models should capture interdependencies between multiple energy carriers and demand sectors. • Review methodologies and assumptions commonly used in power market models. • Identify model design features critical to analyzing the clean energy transition. • Survey current state-of-the-art in modeling low-carbon power markets. • Identify key model improvements needed for future deep decarbonization scenarios. • Highlight importance of tailoring tools for specific power market applications. [ABSTRACT FROM AUTHOR]

There is growing interest in energy sufficiency (ES); however, it remains conceptually debated and contested. This study presents a novel approach to measuring ES using unique microdata on the energy consumption of Japanese households. This approach determines lower and upper thresholds regarding domestic energy consumption for each household type and then identifies whether people really meet an adequate level of domestic energy services, with their diverse energy needs considered. The result illustrates that most people in Japan live in ES, despite about one-tenth being in energy poverty (EP) and one-twentieth in energy extravagance (EE). In addition, this study empirically clarifies the characteristics of people in ES, as well as those in EP and EE. People in ES and EE generally belong to higher income groups, live in larger houses, and are less committed to energy-saving behaviours; the opposite applies to people in EP. As a policy implication, this study suggests more progressive energy tariffs and carbon pricing, as well as improvement in access to renewable energy for the EP population, to engender an inclusive low-carbon energy transition and allow everyone to achieve ES. • First evaluation of people's 'energy sufficiency' (ES) using Japanese microdata. • Identified households in energy poverty, energy sufficiency and energy extravagance. • One tenth of people are in energy poverty and one twentieth in energy extravagance. • The prevalence of energy sufficiency is much related to income and housing size. • More progressive energy tariffs are suggested to allow everyone to achieve ES. [ABSTRACT FROM AUTHOR]

The residential sector's role in global energy consumption highlights the need to understand residents' energy behaviors. However, there is a significant knowledge gap regarding women's preferences for energy-efficient appliances, particularly in developing countries like Pakistan, where women have limited decision-making power within their households. Grounded in the theory of planned behavior this study establishes framework by incorporating constructs such as personal norms, perceived behavioral control, and subjective norms, along with an additional construct of place attachment. By using the data of women of Pakistan and using partial least square structural equation model (PLS-SEM), the findings reveal that place attachment and perceived behavioral control substantially influences the women's attitudes and intentions to purchase energy-saving appliances. However, personal norms and subjective norms are found to be non-significant factors. Notably, subjective norms are found to significantly influence the purchase willingness through attitudes. The insignificant findings reflect the prevailing patriarchal culture that may influence women's decision-making power towards purchasing appliances. The overall study holds significant relevance in the context of Pakistani households, where understanding the adoption of energy-efficient appliances from a gender perspective can provide an opportunity to initiate efforts towards a clean and sustainable future, as well as gender equality by assisting policymakers in establishing gender-oriented energy policies. • This study focuses on women's energy-efficient appliance choices in residential energy. • Grounded in Theory of Planned Behavior with place attachment, it offers a comprehensive model. • Findings revealed place attachment and perceived behavioral control as pivotal factors in women's energy choices. • The study further reflects societal norms that indirectly affect decisions, shedding light on gender biases in appliance purchases. • The study provides valuable insights for gender-sensitive energy policies in Pakistan, promoting sustainability and gender equality. [ABSTRACT FROM AUTHOR]

• Existing energy policies' impacts on long-term CO2 mitigation is limited. • Delayed NDC enhancement might result in significant reduction on energy service. • Early enhancement with carbon trading could effectively reduce the system cost. • Cost-optimal enhancing plan requires huge investment in developing regions. CO2 mitigation in the global energy system is critical in tackling climate change, each region should design its long-term strategies and implement policies accordingly to promote the energy transformation. To evaluate the impacts of existing energy policies and propose possible enhanced NDCs (Nationally Determined Contributions), this paper applied a 14-region global model to explore the transitions of the global and regional energy system. With the modelling of early and late NDCs enhancing plan for 2-degree target, the required energy transition of each plan was analyzed, together with the vital challenges and potential economic impacts. Model results show: 1. Existing energy policies can reduce the annual emission growth rate to 0.6% for 2015 – 2020, while their influence on long-term mitigation is limited; 2. If enhanced NDCs begin from 2030 onwards after realizing current goals, 2-degree target would become quite challenging in later period, some regions will have to cut the energy service drastically; 3. With the assumption of early enhancement and free carbon trading in the cost-optimal enhancing plan, the total cost of global energy system could be reduced by 10% in 2050, compared with the former plan; 4. Under this cost-optimal enhancing plan, developing regions may face great challenges in short to medium term, 122% of additional investment should be put in power sector in 2030, international support on both technology and finance would be essential. [ABSTRACT FROM AUTHOR]

• Nanogrid Energy Management with uncertain demand-supply and battery is addressed. • A weather forecast-integrated Markov decision framework is proposed. • Heuristic and Near-Optimal policies are designed, compared, and discussed. • Simulation results are provided for residential & commercial building environments. • A comprehensive treatment for handling constraints under uncertainty is presented. With the rapid growth in renewable energy and battery storage technologies, there exists significant opportunity to improve energy efficiency and reduce costs through optimization. However, optimization algorithms must take into account the underlying dynamics and uncertainties of the various interconnected subsystems in order to fully realize this potential. To this end, we formulate and solve an energy management optimization problem as a Markov Decision Process (MDP) consisting of battery storage dynamics, a stochastic demand model, a stochastic solar generation model, and an electricity pricing scheme. The stochastic model for predicting solar generation is constructed based on weather forecast data from the National Oceanic and Atmospheric Administration. A near-optimal policy design is proposed via stochastic dynamic programming. Simulation results are presented in the context of storage and solar-integrated residential and commercial building environments. Results indicate that the near-optimal policy significantly reduces the operating costs compared to several heuristic alternatives. The proposed framework facilitates the design and evaluation of energy management policies with configurable demand-supply-storage parameters in the presence of weather-induced uncertainties. [ABSTRACT FROM AUTHOR]

• Multi stakeholder analysis of economic efficiency of Italian White Certificate Scheme. • Incentive scheme is beneficial for all stakeholders involved except from utilities. • Energy efficiency services could change the role of utilities in the energy market. • Need of new services and business model for utilities in the energy market. There is growing interest worldwide in more effective policies to promote industrial energy efficiency and mitigate climate change. The White Certificates Scheme is a market-based mechanism aimed at stimulating the adoption of Energy Efficiency Measures. The Italian White Certificates scheme - one of the most long-standing and articulated - is a successful example of industrial energy efficiency policies, considered an interesting and remarkable case by other countries, especially due to its robustness in terms of the volume of certificates traded. Despite the considerable interest in White Certificates, an in-depth analysis of the economic efficiency of the mechanism from the perspective of different stakeholders is still lacking. To address this gap, this study develops a cost-benefit evaluation framework and a multi-stakeholder economic efficiency analysis of the Italian White Certificates scheme focusing on the Italian State, utilities, players in the energy efficiency value chain, and energy users. Our findings (also corroborated with sensitivity analyses) show that the White Certificates Scheme has led to several positive impacts for almost all stakeholders involved, with the exception of energy utilities that have suffered a major economic loss mainly due to a reduction of energy sold to end users. Such loss is likely to promote a deep change in the role of utilities in the energy market in terms of the services they offer and their business models. Our findings, in addition to providing useful directions for future research, offer interesting insights and implications for policymakers who may take inspiration from the pros and cons of the Italian White Certificates scheme when promoting energy efficiency through incentive mechanisms. [ABSTRACT FROM AUTHOR]

Highlights: • A detailed joint transmission and generation expansion planning model of power systems is implemented. • The effects of CO 2 and local pollutant emission taxes on expansion planning are endogenously analyzed. • Pollutant taxes may have a significant spatial distributive effects on planning decisions. • Availability of renewable energy capacity may improve the effectiveness of pollutant taxes. Abstract This work analyzes the impacts on the power system expansion planning of implementing CO 2 and local pollutant emission taxes under five different policy-relevant scenarios. To do this, we have formulated and implemented an optimization model based on a mixed-integer linear program, which determines the optimal expansion plan considering the installation of both large-scale power plants and renewable-based distributed generation. An important characteristic of the proposed model is that it includes a detailed formulation of the power system. Moreover, differently than existing literature, special attention is given to the analysis of the spatial-temporal distributive effects of pollutant taxes, considering both global and local pollutant emissions. The method is applied to the main Chilean power system. Our results indicate that global and local pollutant taxes significantly impact both planning and operational decisions in the power system. In particular, pollutant taxes may have significant spatial distributive effects, as shown in the analysis of 13 regions of Chile, leading to damages in some specific regions while relatively benefiting others. Our results also show that the availability of renewable energy capacity may improve the effectiveness of pollutant taxes. Particularly, adding 1.5 GW of hydro capacity to the Chilean system allows avoiding around 32 GWh of fossil fuel generation per year, saving more than 1.5 billion US$ in the 10-year horizon considered. The proposed method and qualitative results are sufficiently generic to apply to any other jurisdiction. [ABSTRACT FROM AUTHOR]

Highlights • The application of big data to energy economy modelling is a critical research gap. • Big data has the potential to reduce multiple blind spots in energy economy models. • Bottom-up hybrid models are a logical starting point for big data integration. • Moving from data scarcity to data abundance could completely transform the field. • We explore the research possibilities for revolutionising the state-of-the-art. Abstract Energy economy models are central to decision making on energy and climate issues in the 21st century, such as informing the design of deep decarbonisation strategies under the Paris Agreement. Designing policies that are aimed at achieving such radical transitions in the energy system will require ever more in-depth modelling of end-use demand, efficiency and fuel switching, as well as an increasing need for regional, sectoral, and agent disaggregation to capture technological, jurisdictional and policy detail. Building and using these models entails complex trade-offs between the level of detail, the size of the system boundary, and the available computing resources. The availability of data to characterise key energy system sectors and interactions is also a key driver of model structure and parameterisation, and there are many blind spots and design compromises that are caused by data scarcity. We may soon, however, live in a world of data abundance , potentially enabling previously impossible levels of resolution and coverage in energy economy models. But while big data concepts and platforms have already begun to be used in a number of selected energy research applications, their potential to improve or even completely revolutionise energy economy modelling has been almost completely overlooked in the existing literature. In this paper, we explore the challenges and possibilities of this emerging frontier. We identify critical gaps and opportunities for the field, as well as developing foundational concepts for guiding the future application of big data to energy economy modelling, with reference to the existing literature on decision making under uncertainty, scenario analysis and the philosophy of science. [ABSTRACT FROM AUTHOR]

Highlights • India's forecasted economic growth suggests that emissions will almost quadruple by 2050. • Our analysis shows that India can achieve its NDC target with currently implemented policies. • India's emissions intensity in 2030 will be 36% below 2005 levels in baseline scenario. • Much of the emission growth will come from the industry sector. • Four industry subsectors are projected to represent 78% of total direct emissions in 2050. Abstract India and China are the world's most populous nations, but they have experienced a very different pattern of economic development. As a result, India currently contributes less than one-quarter of the amount of China's carbon dioxide (CO 2) emissions. However, India's forecasted economic growth suggests that those emissions will almost quadruple, with much of this rise coming from the industry sector. Whole-economy scenarios for limiting global warming suggest that direct CO 2 emissions should decrease significantly, but leave unanswered the question of how this can be achieved by real-world policies. This study describes a bottom-up model that can be used to assess the impacts of emissions mitigation policies and the linkages between the physical drivers and energy growth of India's key industries. It focuses on capturing the main physical drivers of this growth, to identify and prioritize the subsectors to address and develop sustainable, low carbon pathways to support economic growth. This analysis shows that India can achieve its Nationally Determined Contribution (NDC) while achieving substantial economic growth using its currently planned policies. The study describes in detail the methodology and underlying assumptions that are needed by policy makers to inform targeted policy interventions and provide a baseline scenario in the case of no major new technology breakthroughs and no new adopted policies. [ABSTRACT FROM AUTHOR]

Highlights • First causal demand response effect evaluation of a mandatory billing demand charge. • Novel two-level time series modelling using observational consumption and survey data. • Matching on pretreatment outcomes to identify causal effects. • Giving a critical discussion of how demand response effects can be causally evaluated. • The proposed methods can be used for a large range of electricity consumption studies. Abstract As peak demand for electricity continues to rise, distributors have begun charging small and medium-sized users for their short term demand rather than just their energy use. This is not only to meet the political aspirations for increased demand-side flexibility that now exist in many corners of the world, but to make sure that users are charged for the costs they incur. As it is only until recently that this type of users have come to face demand charges, there are however very few studies on what the actual effects of such pricing policies are, and those studies that do exist suffer from different methodological shortcomings that reduce their validity as a basis for real-world policy evaluations. This study provides the first state-of-the-art causal analysis of the demand response effects of a billing demand charge involuntarily introduced to small and medium sized users (35–63 A), using novel two-level time series models on retrospective observational consumption and survey data. Our analyses suggest that the tariff has induced an average response of −0.32 kWh/day per user over a two year long posttreatment period in comparison to a matched control group, equal to 7.4% of their daily average use during the pretreatment period. The response seems to have increased over time and to be greater during wintertime: around −0.70 kWh/day or 16.2% of the treated users' average daily use during the pretreatment period. Comparing the individual users' response to the size of their financial incentive to respond given the new tariff as well as their self-reported perception of the relative importance of electricity expenditures, we did not find any support for the common assumption that users with a higher financial incentive to respond do so to a greater extent. This might suggest that small and medium-sized commercial users, just as residential users, may exhibit non-financial drivers and barriers for engaging in demand response that may be vital to understand as policy makers and industry continue to seek increased demand-side flexibility. [ABSTRACT FROM AUTHOR]

Graphical abstract Effective policy coordination interval between renewable portfolio standards (RPS) and carbon cap-and-trade policy. Highlights • Policy coordination between RPS and carbon cap-and-trade in China are evaluated. • RPS and carbon caps have a very different impact on regional power sector. • An effective policy mix interval by 2030 is estimated. • The dependence of CO 2 emission reduction policy on RES-E is examined. Abstract China has planned to implement renewable portfolio standards (RPS) and national carbon cap-and-trade to promote the low-carbon transformation of society. How to design and coordinate the goals of these policies becomes a key issue. This study quantitatively evaluates the effects of RPS and carbon caps in China's power sector using a multi-regional power optimization model combining with decomposition method. Based on the framework, this study focuses on two questions: (1) How to coordinate policy goals of RPS and carbon caps to ensure the effectiveness of each policy in the face of policy overlapping? (2) How to design power generation and transmission planning to achieve the goals of different policy mixes? Results show that these two policies contain overlapping elements, yet remain different impacts on regional power structures, inter-regional power and coal dispatches. An effective policy interval for RPS and carbon caps is estimated to examine the effectiveness of any policy mixes. If an RPS with a 17% non-hydro renewable share is implemented by 2030, the effective interval of carbon caps will be between a 27.5% and 38.2% reduction in carbon intensity. Results support that there are strong inner linkages between reasonable power system planning and current status of policy mix. [ABSTRACT FROM AUTHOR]

Highlights • Tanker network structure and flow changes by oil price variations are predictable. • The proposed approach improves time sensitive in maritime network analysis. • Two-way linkages on oil price and network structure and flow changes are clarified. • The differences in the oil import- and export-dependent countries are analyzed. Abstract Understanding maritime network structure and traffic flow changes is a challenging task that must incorporate economic, energy, geopolitics, maritime transportation, and network sciences. The reported research examines whether there are linkages between oil price fluctuations, maritime network structure and traffic flow changes, and investigates whether tanker maritime network structure and traffic flow changes are driven by oil price fluctuations. This is essential knowledge for developing strategies energy policies. To address this, a systems-based approach is introduced to construct a maritime transportation network based on trajectory data. The maritime network structure and traffic flow changes are derived from monthly transport. Supporting methods were then used to reveal two-way linkages, long-term balance relationships and shock responses attributable to oil price fluctuations, the maritime network structure and traffic flow changes. Evidence suggests that there are different two-way linkages between oil price fluctuations, maritime network structure and traffic flow changes in oil import- dependent and export-dependent countries. International crude oil price fluctuations contribute to maritime network structure changes for many of the countries examined, and showed rapid growth peaks in the second or third month. Our results have important policy implications for national tanker transportation strategies within the context of oil price fluctuations, suggesting diversification of import origins and export destinations as well as adjustment of shipping mode to cope with the supply shocks caused by oil price fluctuations. [ABSTRACT FROM AUTHOR]

Highlights • Building thermal performance is governed by a number of national policies. • The focus included attributes, technology and innovation, barriers and reflections. • The perspectives of the stakeholders did not fully align with the policies. • The industry had no objection to employing low carbon technology alternatives. • Industry should be consulted continuously in forming future policies. Abstract Current policies have implied that improving thermal performance of the built environment would be included in a strategy to reduce the greenhouse gas emissions in the UK and meet the national targets of the Climate Change Act by 2050. However, the perceptions of the industrial stakeholders in this matter have not, to date, been clear. This study aimed to uncover stakeholder perspectives on thermal performance of the built environment and investigate whether their perspectives aligned well with the national policies. Focusing on attributes of the built environment, technology and innovation, barriers and reflections on reality, technical feedback was gathered from experienced industrial stakeholders via a one-day workshop and emails. The analysis showed that despite being familiar with the national policies, the perspectives of the industrial stakeholders did not fully align with the national policies in most aspects. However, the industry had no objection to employing low carbon technology alternatives in the future. The study concluded that consultation with industry should be carried out continuously to assist in the formation of future national policies to significantly improve the thermal performance of the built environment. Future research should be extended to comparing the stakeholder viewpoints and national policies from environmental and economic perspectives on a European/global scale. [ABSTRACT FROM AUTHOR]

Highlights • Capacity expansion planning model for the integration of CSP-TES. • Evaluation of the systemic economic and operational benefits provided by CSP-TES. • Analysis of multiple technology and policy scenarios for the Chilean power system. • Study of complementarities between CSP-TES and other technologies. • Investigation on the role of CSP-TES as a flexibility provider to the system. Abstract This paper presents a study about the impacts of the integration of concentrated solar power (CSP) with thermal energy storage (TES) in electric power systems. The main tool for this study is a comprehensive long-term power system capacity expansion planning model that integrates a specific module to represent the operation of CSP-TES power plants. The model determines the optimal investments on generation and transmission assets over a twenty-year planning horizon, ranging from 2018 until 2037, and employs projections for the various parameters involved (e.g. load growth, capital costs for the different generation technologies, fuels costs). One of the main features of the model is its ability to capture the hourly operational dynamics of the system through the consideration of multiple representative days for each of its investment periods. This feature allows a better understanding of the role of CSP-TES as a significant provider of flexibility to support a high penetration of variable renewable energy sources, as compared with traditional planning models based on load blocks. The model is applied to a case study for the Chilean electricity system. In order to study the impacts of CSP-TES, various scenarios of future capital costs and carbon tax levels are defined and analyzed for two market dominant CSP-TES technologies. The results show that for low CSP-TES capital costs, or high carbon taxes, the integration of CSP-TES in the system is significant towards year 2037, potentially reaching about one third of the total dispatched energy in the Chilean electric power system, yielding important operational, economic, and environmental benefits. [ABSTRACT FROM AUTHOR]

Highlights • Create city-specific energy performance grading model using city disclosure data. • Method to grade buildings that is interpretable, reproducible, scalable, and robust. • Develop a clustering algorithm to grade buildings on performance. • Validated approach demonstrates significant improvements over current methods. Abstract As the effects of anthropogenic climate change become more pronounced, local and federal governments are turning towards more aggressive policies to reduce energy use in existing buildings, a major global contributor of carbon emissions. Recently, several cities have enacted laws mandating owners of large buildings to publicly display an energy efficiency rating for their properties. While such transparency is necessary for market-driven energy reduction policies, the reliance on public-facing energy efficiency grades raises non-trivial questions about the robustness and reliability of methods used to measure and benchmark the energy performance of existing buildings. In this paper, we develop a building energy performance grading methodology using machine learning and city-specific energy use and building data. Leveraging the growing availability of data from city energy disclosure ordinances, we develop the GREEN grading system: a framework to facilitate more accurate, fair, and contextualized building energy benchmarks that account for variations in the expected and actual performance of individual buildings. When applied to approximately 7500 residential properties in New York City, our approach accounts for the differential impact of design, occupancy, use, and systems on energy performance, out-performing existing state-of-the-art methods. Our model and findings reinforce the need for more robust, localized approaches to building energy performance grading that can serve as the basis for data-driven urban energy efficiency and carbon reeduction policies. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Representative pathway models for co-production of fuels and high-value co-products. • Economic sensitivity to co-products, policy, carbon prices, and methodology. • Cost tradeoff of $7.3 million capital to $1 million yr−1 operational at 10% IRR. • High capital of algal fuels requires significant co-product credits for economic viability. • WWT integration with algal growth represents economic challenge. Abstract This study presents an extensive systems-level multi-pathway sustainability assessment of algae biofuel production that demonstrates the necessity of high-value co-products, examines the impact of public policy scenarios, and identifies improvements and pathway directions required for economic viability. Engineering process models for several fuel and co-product pathways were leveraged to perform high fidelity techno-economic analysis. These pathways included: baseline hydrothermal liquefaction; protein extraction followed by hydrothermal liquefaction; fractionation into high-value chemicals with fermentation followed by hydrothermal liquefaction for fuels; and a small-scale first-of-a-kind plant coupled with a wastewater treatment facility. From these models, it was shown that hydrothermal liquefaction as a fuel-only pathway is not economically viable. Likewise, the benefits of coupling with wastewater water treatment are insignificant compared to the impact of reduced facility size resulting in increased capital costs. These models were also used to examine public policy scenarios, uniquely presenting their impact on the breakeven cost of fuel production and sensitivity to scenario assumptions. Specifically, depreciation type was shown to be irrelevant for writeoffs faster than 10 years. Due to discounting, short-term subsidies were found to capture 50% of the subsidy value in 6 years with an additional 24 years required for full subsidy valuation. Integration of a carbon economy was shown to decrease biofuel production costs, particularly for the protein pathway due to the co-product accounting. Finally, a metric of normalized costs was used to compare algal biorefineries to corn and cellulosic ethanol production, showing that algal systems are uniquely different due to significantly higher capital costs, though operational costs are comparable. This work demonstrates that, to reach economic viability, algal biofuel production must either utilize higher value non-fuel co-products or achieve drastic reductions in capital costs. [ABSTRACT FROM AUTHOR]

Highlights • We addressed a novel energy-efficiency program for the home appliances and energy. • We discussed appropriate energy policies for the government decision-making. • Energy rebound, consumer behavior, and innovation are assumed in the problem. • First scenario has the most advantages and least disadvantages than the others. • The manufacturer subsidy has the most performance between the discussed policies. Abstract In recent years, due to the sustainability concerns, energy-efficiency programs that address eco-friendly issues such as energy efficiency and clean production have increased in the world. Furthermore, eco-efficient appliances, as the direct result of research and development (R&D) activities have become a popular topic in the sustainability programs of the most developed countries due to their high-tech features. In this paper, we discuss a residential energy-efficiency program for the new energy-efficient appliances and related energy consumption, simultaneously, in a sustainable supply chain consists of the energy supplier, manufacturer, and the government. Also, we consider the technological rebound effect of the efficiency improvement, consumer behavior, and ability to innovate of the manufacturer in the problem, as the novel assumptions. To solve the problem, we propose a multi-stage game model using the Bertrand model. Also, we investigate three structures, based on the Nash and leader-follower games as the scenarios. The findings reveal the equilibrium pricing decisions for the supply-chain players to maximize their profits, besides the best energy policy and supply-chain structure for the efficient management of household energy consumption. More precisely, the first scenario with the Nash structure between the manufacturer and energy supplier has the most advantages and the least disadvantages than the other scenarios. Also, the manufacturer subsidy has the most performance in the discussed energy-efficiency program. Finally, the present study shows the significant effects of considering the energy rebound, innovation abilities of the manufacturer, and consumer behavior assumptions in the household energy problems. [ABSTRACT FROM AUTHOR]

Highlights • Overview of US Federal Energy Management (FEMP) lighting efficiency program. • Estimated total benefits of the 2017 lighting update from $33 to 217 million USD. • Estimated energy savings 0.33–4.76 TWh for this update. • Total benefits methodology with monetized full fuel cycle greenhouse gas emissions. • Presents the FEMP lighting categorization structure. Abstract Lighting consumes a great deal of electrical energy in buildings, and the U.S. federal government is the largest single energy consumer in the United States. Therefore, the way that the U.S. federal government manages its lighting energy use is of great importance. One important way that energy use is managed is through Federal Energy Management Program (FEMP) energy efficiency requirements. However, there is not much literature on how these requirements are set and how to value the resulting savings. This paper documents the methodology used to develop these for the 2017 FEMP lighting update. A methodology for calculating the total benefits is presented that goes beyond site energy saved, and includes full fuel cycle energy savings, energy cost savings, emissions offset and monetized emissions costs. The results show total benefits of $33 million USD including 475 kilotons of carbon dioxide (CO 2) offset under the low compliance scenario, $104 million USD including 1494 kilotons of CO 2 offset under the medium compliance scenario, and $217 million USD including 3124 kilotons of CO 2 offset under the high compliance scenario. [ABSTRACT FROM AUTHOR]

Highlights • Prices versus quantities analysis for best welfare on carbon reduction are introduced. • Supplementary research on carbon reduction policy decision with prices versus quantities are summarized for the first time. • The evolution of hybrid carbon reduction policies is presented. • Further improvements of prices versus quantities for carbon reduction are proposed. Abstract Weitzman's price-quantity analysis framework has been widely used, and in the field of climate economy it is applied to choose proper emissions reduction instruments for best welfare under uncertainty. This article summarizes the principle and method of selecting carbon emission reduction instruments by price versus quantities decision criterion. The probability of uncontrollable outcomes under uncertainty is reduced by comparing marginal abatement costs with marginal abatement benefits. On this basis, the following outreach research are summarized on some key issues such as dynamic analysis, assumptions improvements, elements expansion and hybrid policies. This topic drives a lot of research but no review has been conducted. We sum up the time-correlated cost and multi-period policy research to extend the static framework to dynamic analysis. There are also many studies that relax the strong assumptions of original framework including correlated uncertainty between cost and benefit, stock effect of carbon, nonlinear marginal cost and benefit, distributional inefficiency and extension of partial equilibrium. In addition, more elements around emissions control have been included and specified such as uncertainty, incomplete enforcement, technology innovation and multiple pollutants. We also summarize the researches of hybrid policy which combines quantity and price control to be a new regulation method and dual regulation which suggest applying both quantity and price control in different departments. It is found that early researches favor carbon tax while quantity regulation has gradually become popular. Finally, this article puts forward some research directions worth more exploring at both theory and application level. [ABSTRACT FROM AUTHOR]

Abstract With the dual challenges of reducing emissions from fossil fuels and providing access to clean and affordable energy, there is an imperative for a transition to a low carbon energy system. The transition must take into consideration questions of energy justice to ensure that policies, plans and programmes guarantee fair and equitable access to resources and technologies. An energy justice framework is outlined to account for distributional, procedural and recognition inequalities, as well as emerging themes such as cosmopolitan and non-Western understandings of justice, in decision-making relating to energy systems. The spectrum of research offers critical perspectives on the energy transition as well as tools for decision-making and policy processes. Quantitative, qualitative and mixed methods all contribute to our understanding of the problems and the success of responses. The studies presented in this special issue illustrate that the field of energy justice is a rapidly growing arena. There is constant innovation taking place in enabling the transition with new structures, processes and metrics being introduced to guide decision-making and a more holistic view of the community emerging where acceptance, mobilisation and empowerment are opening possibilities for a just transition to a low carbon energy system. The importance of introducing the interdisciplinary approach between social sciences and natural sciences as well engineering implementation supported by scientific data and experiments shall be emphasized in future studies. [ABSTRACT FROM AUTHOR]

Small-scale distributed generation (DG) installations, like rooftop solar photovoltaics, are adopted earlier in high-income households than in other portions of society. This can limit grid electricity export capacity for latecomers, causing inequitable access and slower renewables deployment. In search for an equitable solution, this work challenges current DG permissioning practices: instead of applying rules of thumb to assign DG, we propose a policy that represents grid limitations at all hierarchies of the grid (i.e., customer, distribution, sub-transmission and transmission levels). A method to predict interest in household DG installations over time based on demographic and socioeconomic variables is presented and validated. This is leveraged to test a novel policy for small-scale DG deployment: sharing the grid ; and to explore its potential to improve equity in grid access and speed of renewables deployment. Proposed, current and alternative policy paths on small-scale DG adoption are tested to identify benefits and trade-offs. Using Ireland's national utility as case study, we perform a long-term simulation of solar photovoltaic adoption. Sharing electricity export capacities increases participation from 77.9 % to 100 %, unlocking access to the grid for 364,064 electricity customers compared to current policy—without the need for infrastructure upgrades. Furthermore, the proposed policy achieves the same overall level of installed capacity as current policy 44 % faster, accelerating small-scale DG rollout, benefiting households and decarbonisation targets. Ultimately, to determine equitability, we connect these results to demographic variables and find that grid limits are not biased towards any demographic group studied, suggesting that policy can be tailored to prevent DG access inequity. • We propose energy policy to prevent DG access inequality in electricity grids. • We model and test three allocation policy paths for small-scale DG access in Ireland. • The progression of accepted and rejected DG installations is studied. • Customer access and participation increase if the grid is seen as a shared resource. • The connection between demographics and access to DG installations is studied. [ABSTRACT FROM AUTHOR]

Highlights • Renewable quotas incorporating equity and duty are allocated at regional level. • The entropy method coupled with Management by Objectives principles is adopted. • Quotas scheme is dominated by differences in resource endowment and infrastructure. • Nexus for renewable-rich and developed areas is vital to fulfill renewable quotas. • Renewable revolution needs a twin-track process of nearby and external consumption. Abstract Feed-in tariff and renewable portfolio standards are two major policies for renewable power generation and China is the first country to adopt both policies at the national level. The black box for regional allocation of renewable portfolio standards, however, may lead to the instability and inefficiency of this policy. This paper introduces the principles of Management by Objective to build a clear policy framework for renewable portfolio standards in China and employs the entropy method to equally and reasonably assign regional responsibility for renewable energy development. A comparison is made between our allocation and the goal-directed policy to determine whether historical responsibility and infrastructure construction should be further considered in crafting renewable portfolio standards. The trends for regional renewable quotas are influenced by the twin-track approach of nearby utilization and external transmission. Finally, based on our analysis, the policy implications behind the new incentive are presented to urge the establishments of supporting facilities. [ABSTRACT FROM AUTHOR]

Highlights • Historical institutionalism is applied to energy justice for the first time. • Critical junctures provide opportunity windows for policy actors to influence change. • Chernobyl is identified as a critical juncture in German nuclear policy. • Small businesses are a key driver of energy policy change. Abstract The German government presented the decision to phase out nuclear energy as a nationally accepted rebalancing of inequality in the energy sector. We expose how this radical change was delivered through a myriad of change agents, most notably through the rise of small energy companies. Critical junctures, in this case the Chernobyl disaster rather than Fukushima, offer moments in time when national policy systems are destabilized. They provide opportunities for changing policy in a perceived pro-energy justice direction if a new consensus can be forged. The paper concludes with a discussion on how energy scholars must engage more with policy analysis frameworks if long-term effective solutions are to be found to persistent energy inequalities. [ABSTRACT FROM AUTHOR]

Highlights • Uncertainty of with Final and Useful energy statistics is explored for the first time. • Data about end-use conversion efficiencies is collected and updated. • In the UK, The average Final to Useful conversion efficiency is of 67%. • A large share of Useful energy consumption (85%) has an uncertainty below 25%. • Internet of Things will improve end-use energy statistics, including Useful Energy. Abstract The use of Useful energy as an energy indicator for sustainability and energy efficiency policy making has been advocated for since the 1970s. Useful energy is the energy delivered by conversion devices in the form required to provide an energy service. This indicator has not been employed mainly because of concern over the reliability of the underlying data, however its uncertainty has never been quantified. This study is a first attempt to rigorously quantify the uncertainty that is associated with Final and Useful energy balances. A novel methodology based on a Bayesian approach is developed, previously unpublished data about average end-use conversion device efficiency is compiled and the Useful energy balance of the United Kingdom is calculated. The uncertainty analysis shows that the largest source of uncertainty is the allocation to energy end-uses, where the uncertainty of the energy flows goes from a median value of 5% to one of 34%. Useful energy consumption for transport and for heating has low uncertainty (4–10%) and overall, 85% of consumption has uncertainties below an acceptable 25% threshold. Increased availability of energy consumption sensing technology will enable the improvement of end-use energy statistics. If governments and statistical offices seize this opportunity, Useful energy has the potential to become an important indicator for the development of energy efficiency policies and thus help stimulate policy action in end-use sectors. [ABSTRACT FROM AUTHOR]

Highlights • Inter-state natural gas infrastructure investment across socioeconomic context. • Existing pipeline capacity is insufficient to satisfy future demand for natural gas. • Geographic distribution of investments within the U.S. is heterogeneous. • Risks of under-utilization of pipeline capacity in a low-carbon future economy. Abstract Changes in the natural gas market have spawned the need for pipeline infrastructure planning. Previous studies have analyzed natural gas infrastructure development largely independent of the interactions of the natural gas sector with the broader economy. However, natural gas infrastructure development is strongly influenced by broader domestic and international socioeconomic conditions. We couple a global Human-Earth system model with state-level detail in the United States (GCAM-USA) that provides the broader socioeconomic context for natural gas supply and demand with a natural gas infrastructure investment model (NANGAM) to examine inter-state natural gas pipeline infrastructure development in the U.S. under a range of socioeconomic scenarios. Here we show that existing pipeline infrastructure in the U.S. is insufficient to satisfy the increasing demand for natural gas and investments in pipeline capacity will be required. However, the geographic distribution of investments within the U.S. is heterogeneous and depends on the capacity of existing infrastructure as well as the magnitude of increase in demand. Our results also illustrate the risks of under-utilization of pipeline capacity, in particular, under a scenario characterized by long-term systemic transitions toward a low-carbon economy. More broadly, our study highlights the value of integrated approaches to facilitate informed decision-making. [ABSTRACT FROM AUTHOR]

Highlights • A scalable distributed formal analysis (DFA) is devised for stability analysis of networked microgrids under disturbances. • A distributed quasi-diagonalized Geršgorin (DQG) theory is developed to identify systems’ stability margins. • A microgrid-oriented decomposition approach is established to decouple networked microgrids. • A novel framework is designed to effectively implement DQG-based DFA in networked microgrids. Abstract A scalable distributed formal analysis (DFA) via reachable set computation is presented to efficiently evaluate the stability of networked microgrids under disturbances induced by distributed energy resources (DERs). With mathematical rigor, DFA can efficiently compute the boundaries of all possible dynamics in a distributed way, which are unattainable via traditional time-domain simulations or direct methods. A distributed quasi-diagonalized Geršgorin (DQG) theory is then combined with DFA to identify systems’ stability margins. A microgrid-oriented decomposition approach is established to decouple a networked microgrid system and enable calculations of DFA and DQG while also preserving the privacy of each subsystem. Numerical tests on a networked microgrid system validate that DFA and DQG facilitate the efficient calculation and analysis of networked microgrids’ stability. [ABSTRACT FROM AUTHOR]

Highlights • The substitution effect of RPS and REC trade for the FIT was calculated and analyzed. • A multi-region power market model was proposed and developed. • REC trade can reduce the government’s expenditure on subsidies for renewable energy. • FIT subsidy provides guarantees of the local power sectors’ profit. • RPS, REC trade and FIT subsidy need to be considered together. Abstract The Feed-in Tariff (FIT) has been successfully used to promote the development of renewable energy; nevertheless, it may cause financial burden on the governments at the same time. Compared with FIT, Renewable Portfolio Standards (RPS) and the Renewable Energy Certificate (REC) trading have been considered to reduce the government’s expenditure caused by the subsidization. To examine the effectiveness of RPS and REC trading, the development of renewable energy and the environmental and economic benefits under different policies have been quantitatively investigated by using a multi-region power market model and China has been chosen as a case study. The obtained results show that: (i) REC trading can efficiently reduce the government’s expenditure on subsidies for the development of renewable energy; (ii) Compared to FIT, RPS and REC trading will reduce the power sectors’ profit; and (iii) RPS and REC trading may not be enough to achieve the target on renewable energy especially when the capital cost is high, therefore, RPS, REC trade and FIT subsidy should be implemented as complementary policies, not independent. [ABSTRACT FROM AUTHOR]

This study examines the use of water-use fees in California’s bidding-based power markets to balance freshwater conservation and reduction of the marine ecosystem impact of coastal once-through-cooled power plants. An hourly power dispatch is simulated using the state’s 2014 demand and generation capacity data. Fees on ocean water withdrawals of $5–120/acre-ft are simulated in three scenarios that test the grid’s ability to simultaneously mitigate its impact on marine ecosystems, conserve freshwater, and incentivize recycled water use. Although fees modeled represent a small share of generator fuel costs, results show that they trigger declines in ocean water withdrawals of up to 11% that are almost always cost-effective if accounting for effects on system-wide fuel costs and CO 2 emissions. An appropriately designed fee-structure reduces ocean water withdrawals by 9% without increasing freshwater consumption elsewhere. Wholesale electricity price increases of 5–10% are concentrated in Northern California, and marine ecosystem benefits are partly offset by increases in NO x and SO 2 emissions inland. Overall, this study finds that water-use fees could be an effective strategy for reducing the marine ecosystem impacts of California’s power sector, particularly because they can also address short term fluctuations in freshwater scarcity. Keywords: Energy-water nexus, once-through cooling, scarce water, environmental pricing, energy policy, electricity dispatch, power systems. [ABSTRACT FROM AUTHOR]

This article contributes to the literature on energy justice by revealing how the principles of energy (in)justice manifest at the domestic scale. We use data from a retrofit intervention trial to reveal recognised and hidden vulnerabilities and practiced distributive and procedural energy fairness in the lived experiences of low-income older and/or frail householders near Melbourne, Australia. Combining the capability and practice approach for the transition to lower carbon housing to provide a rich description, we chart householder functionings of heating and paying energy bills and their choices in keeping warm and affording energy before and after simple retrofits. Energy justice was experienced on four separately distinguishable levels of social relationships: intra-households, household-energy retailer relations, immediate social networks and wider social relations. The outcomes of the trial showed that combinations of simple retrofits improved householder heating capabilities. Policies and programs aimed at transitioning to low-carbon energy systems need to acknowledge and address the changing demand for energy of an ageing population, and acknowledge social differentiation within households. This includes using a capabilities approach to recognising multiple vulnerabilities. [ABSTRACT FROM AUTHOR]

This paper explores the energy justice nexus, drawing on Sen and Nussbaum’s concept of capabilities. Our contribution operationalises most of the ten capabilities defined by Nussbaum, and examines them for all households of a single country (Belgium) in relation to household access to energy and especially to affordable warmth. We argue that the three dimensions highlighted by environmental-justice theories – income distribution, procedures producing unequal distributional outcomes, and cultural and political recognition of vulnerable and marginalised social groups – are more evident when posed contra the range of differences between energy-poor households and other types of households of a country. Thus, we propose a five-group typology of households that also takes into account the social aid granted in the country. Using Belgium as an example, this typology is used to compare across these five groups with respect to the extent to which energy poverty is associated with other difficulties of daily life beyond just housing and health. A new simple statistical index is developed to summarise these comparisons. The approach of the energy justice nexus is thus systemic rather than causal between access to energy and potential capabilities’ deprivation. The data used is a large-scale quantitative survey that is part of the Generation and Gender Programme (GGP), and it enables to proxy most of Nussbaum’s capabilities with several questions asked in this GGP survey. As these GGP surveys are standardised and realised in 16 countries, our approach is transferable to other nations/regions as well. Results show that energy poverty in Belgium is associated with deprivation of several capabilities, in more areas than expected: not only regarding housing, health, and mobility, but also regarding access to culture and recreational activities, as well as the feeling of fulfilment and ontological security. Furthermore, the comparison between energy-poor people and other energy-access groups makes the issue of social stigma clearer, and thus so the dimension of political recognition. In terms of policy, these results suggest to fighting energy poverty as a transversal issue. [ABSTRACT FROM AUTHOR]

Policies associated with the central heating supply system affect the livelihoods of people in China. With the extensive consumption of energy for central heating, large quantities of CO 2 emissions are produced each year. Coal-fired heating boiler plants are the primary source of emissions; however, thermal power plants are becoming much more prevalent, and gas-fired heating boiler plants remain uncommon. This study quantified the amount of CO 2 emitted from the central heating supply system in China using a mass balance method with updated emission factors from the IPCC. Emissions increased from 189.04 Tg to 319.39 Tg between 2006 and 2015. From a spatial perspective, regions with larger central heating areas, durations and coverages produced more CO 2 emissions. The central heating method depends on the level of electric power consumption, policies and regulations, and resource reserves at the local scale. Compared with the use of only coal-fired heating boiler plants to provide central heating, using thermal power plants and gas-fired heating boiler plants reduced CO 2 emissions by 98.19 Tg in 2015 in China. A comparison of the CO 2 emissions under various central heating scenarios showed that emissions will be 520.97 Tg, 308.79 Tg and 191.86 Tg for business as usual, positive and optimal scenarios through 2025, respectively. China has acknowledged the considerable potential for reducing central heating and will make efforts to pursue improved heating strategies in the future. [ABSTRACT FROM AUTHOR]

Energy subsidies have been criticized due to their economic inefficiency and promotion of wasteful usage of energy and associated carbon emissions. Conversely, environmental taxes are advocated as efficient policy instruments. But removing subsidies and taxing energy can be politically challenging because vulnerable households rely on low energy prices. This study analyzes the impact of energy price hikes on different income groups using an energy-extended input-output approach. Our results show that higher-income groups benefit more from low energy prices than low-income groups when tracing both direct and indirect (supply chain) effects of energy price variations. Energy subsidies are a very expensive option to transfer income to poor households. For example, in Latin America and the Caribbean, using energy subsidies would cost about $12 to transfer $1 of income to households in the poorest quintile. Recycling a small fraction of fiscal revenues from energy subsidy removal or energy taxation could be sufficient to compensate vulnerable households from the effects of price hikes. Cash transfers to poor households and targeted subsidies for public transportation or food are the most effective measures to compensate households for welfare loss. [ABSTRACT FROM AUTHOR]

Detailed analysis of pathways to future sustainable energy systems is important in order to identify and overcome potential constraints and negative impacts and to increase the utility and speed of this transition. A key aspect of a shift to renewable energy technologies is their relatively higher metal intensities. In this study a bottom-up cost-minimizing energy model is used to calculate aggregate metal requirements in different energy technology including hydrogen and climate policy scenarios and under a range of assumptions reflecting uncertainty in future metal intensities, recycling rate and life time of energy technologies. Metal requirements are then compared to current production rates and resource estimates to identify potentially “critical” metals. Three technology pathways are investigated: 100 percent renewables, coal & nuclear and gas & renewables, each under the two different climate policies: net zero emissions satisfying the well-below 2 °C target and business as usual without carbon constraints, resulting together in six scenarios. The results suggest that the three different technology pathways lead to an almost identical degree of warming without any climate policy, while emissions peaks within a few decades with a 2 °C policy. The amount of metals required varies significantly in the different scenarios and under the various uncertainty assumptions. However, some can be deemed “critical” in all outcomes, including Vanadium. The originality of this study lies in the specific findings, and in the employment of an energy model for the energy-mineral nexus study, to provide better understanding for decision making and policy development. [ABSTRACT FROM AUTHOR]

Transitioning electricity generation sectors to renewable energy is a necessary element of climate change mitigation but a challenging one with years of technical and social lock-in creating considerable inertia and a pace of change insufficient to meet the task. Transition management has emerged as a promising approach to proactively manage and accelerate transitions in sectors like energy and water and has the potential to do so in manner that would help secure energy justice. However, the approach has been criticised for ignoring the political dynamics of transitions, risking procedural and recognition injustice for traditional energy production regions while increasing opposition to a transition. This paper uses a qualitative case study approach to understand how a transition to renewable energy generation could be achieved in a way that secures energy justice for traditional energy production regions like those of Gladstone in the Australian state of Queensland. The paper augments the transition management approach with the “just transitions” concept, which has emerged from labour movements as a means to mitigate negative impacts on workers and communities in traditional energy production regions, to create a “Just Transition Management” framework that is applied as a diagnostic tool to the case study. While results suggest a transition is imminent within Queensland, a lack of: consistent, supportive federal policy; long-term visions, and a clear process leaves it at risk of being captured by the powerful incumbent resources sector utilising an “environment vs. jobs” narrative. Yet, the results also suggest that adopting a just transition management approach has the potential to successfully resist the influence of the resources sector, by providing cheap, secure renewable energy and supporting affected workers into roles in the renewable energy sector using a reflexive, representative network governance approach. Data analysis reveals how the application of just transition principles has already engaged communities and unions that were previously sceptical of renewable energy. Overall, this study suggests that the just transition management framework assists in identifying the political barriers to transitions and energy justice more broadly, while also providing a management approach which creates powerful niche actor-networks to counter the narratives and influence of the incumbent resource sector. Thus it could support successful transitions that achieve the distributional, recognition and procedural justice needed for energy justice globally. [ABSTRACT FROM AUTHOR]

Our study investigates solar energy justice in the province of Saskatchewan, Canada. In 2017, we were engaged by the Saskatchewan Power Corporation (SaskPower), a government owned electric utility, to conduct stakeholder engagement workshops for the development of new solar energy programs in Saskatchewan. In coordination with SaskPower we developed a deliberative dialogue approach to the consultation process. Select stakeholders were invited to participate in a half-day workshop. In this workshop participants were asked for input on the principles that would guide SaskPower’s solar energy strategy, the barriers that prevent solar energy from being installed in the province, and their ideas for effective solar energy programs. Participants worked in small groups to design solar energy programs, creating opportunities for mutual learning and deliberation. This research is the first application of deliberative dialogue to the design of solar energy programs of which we are aware and offers an example of due process in the program design stage of energy planning. We use an energy justice decision-making (Sovacool et al., 2014) tool to evaluate the process of designing SaskPower’s solar energy strategy and the content of recommendations made by participants to answer the question, can due process help to achieve energy justice? Participants in our deliberative dialogue suggested guiding principles that were similar to the dimensions of the energy justice decision-making tool. The deliberative process also highlighted tensions between dimensions of the energy justice decision-making tool. In this paper, we suggest avenues to improve the deliberative dialogue process and conclude that centering due process as a core element of the energy justice decision-making tool can help to achieve energy justice. Our results contribute to the growing field of study on how deliberative dialogue can allow for better decisions in complex fields such as energy policy. [ABSTRACT FROM AUTHOR]

In renewable power systems for remote islands, a significant amount of electricity is curtailed for power balance. This study examines the effect of reducing electricity curtailment on optimal renewable power system design for remote islands. A Mixed-Integer Linear Programming problem for obtaining the optimal design of remote renewable power systems is formulated with an additional constraint which limits the total electricity curtailment. For a parametric study, the problem is solved with various values of electricity curtailment limit, for four cases; two islands - one with a higher and the other with a lower capacity factor of wind turbines - in South Korea, and two renewable power penetrations - 60% and 90% - in the two islands. Variations in the optimal capacities of photovoltaic panels, wind turbines, and batteries for limited total electricity curtailment are examined for every case. Reasons of the variations are explained by an analysis on the temporal operation profiles of the remote renewable power systems. In addition, the appropriate upper limit of total electricity curtailment considering cost increment, normalized by the total electricity demand, is newly suggested as a function of renewable power penetration to guide policy design. [ABSTRACT FROM AUTHOR]

Lack of long-term global solar radiation data has often been a significant challenge to the solar power sector development primarily in developing countries. The choice of a solar radiation database is projected to have a considerable impact on the predicted performance of a solar power project and consequently on its techno-commercial viability. Therefore, use of reliable and well- characterized solar radiation data source is important for bankability of solar power projects. This study presents the technical and economic performance evaluation of grid-interactive solar photovoltaic (PV) projects implemented under the first phase of India’s national solar mission. For performance assessment, we compare annual energy yield predictions using several solar radiation databases and monitored data of 39 solar PV power plants located across the country. Technical simulations have been carried out for each project location using static and dynamic solar irradiance data obtained from various databases available in the Indian context. PV SYST software has been used for energy yield assessment of solar PV projects after taking into account the key design and technical parameters and associated energy losses during solar energy conversion. The inter-comparability of capacity utilization factor and levelized cost of electricity of operational solar PV projects have also been analyzed with the estimates obtained through different solar radiation databases. Mutual deviation for the techno-economic performance of solar PV projects varied from −12% to 31% for the projects under the first phase of India’s solar mission. Our study indicates that the long-term measured or high-resolution time series databases should be preferred for the bankability of solar power projects. Further, solar power policies of the country must provide clear guidelines for selection of solar radiation databases to enhance their bankability. [ABSTRACT FROM AUTHOR]

Price elasticities play an important role in energy demand forecasting, which in turn shapes energy policy and investment decisions. However, there is still considerable debate around how responsive customers are to energy prices, and whether investments in metering and consumer education have made them more responsive in recent decades. Using a Generalized Leontief (GL) demand system and a rich panel of monthly data covering 2001–2016 for the lower 48 United States, we estimate own- and cross-price elasticities of retail demand for electricity, natural gas and fuel oil for the three major customer classes: residential, commercial and industrial. These estimates indicate that retail energy demand in the United States was highly price-inelastic over 2001–2016, consistent with historical and current estimates used by many utility planners. Our findings suggest that, with current technologies and behavior, higher energy prices will not induce significant reductions in demand. Hence, energy efficiency standards and utility demand-side programs are still an important strategy for managing energy demand growth, mitigating energy price risk, and reducing the environmental impacts associated with energy use. Finally, while our analysis uses data from the United States, our approach is general and can be readily extended to other countries that have similar data available. [ABSTRACT FROM AUTHOR]