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Adoption of residential rooftop photovoltaic (PV) systems is increasingly widespread. However, empirical evidence shows that households who adopt rooftop PV increase total electricity consumption, a response known as the 'solar rebound effect' (SRE). The SRE implies that rooftop PV generation displaces conventional, grid-supplied electricity—still overwhelmingly generated by fossil fuel combustion—on a less than one-for-one basis. This article argues that utility-scale solar avoids a SRE because the SRE emerges as a household's response to the self-generation of electricity by its rooftop PV system, which changes its electricity consumption incentives in ways that utility-scale solar does not. By avoiding a SRE, utility-scale solar allows the carbon reduction potential of increased PV generation capacity to be more fully realized, which has important implications for U.S. solar policy. [ABSTRACT FROM AUTHOR]

The Electricity Supply Chain is a system of enabling procedures to optimize processes ranging from production to transportation and consumption of electricity. The proportion of distributed energy sources within the electricity system increases steadily, which necessitates an improved monitoring capability to ensure the overall reliability and quality of the Electricity Supply Chain. Automation is strongly required to process the growing amount of data. Thus, it is inevitable to handle large amounts of heterogeneous data and process the information using forecasting and optimization techniques. Artificial Intelligence techniques are crucial for extending human cognitive abilities in these tasks. In our work, we synthesize the main impacts of the Artificial Intelligence paradigm on the automation of the Electricity Supply Chain. We describe the emerging automation through Artificial Intelligence in every layer of the Smart Grid Architecture Model and highlight state-of-the-art approaches. In the review, we focus on the following Electricity Supply Chain functionalities: generation, maintenance, pre-processing, analysis, forecasting, optimization, and trading within energy systems. After investigating the individual perspectives, we examine the potential implementation of a fully automated Electricity Supply Chain. Lastly, we discuss perspectives and limitations for the transformation from conventional to automated Electricity Supply Chains, specifically in terms of human interaction, Artificial Intelligence adaptation, energy transition, and sustainability. • Assessment of AI impact on key functions for Electricity Supply Chain automation. • Electrical automation sectors are arranged by Smart Grid interoperability layers. • Identification of a clear need for AI standardization in electrical applications. • Challenges and opportunities of AI for the electricity sector are presented. [ABSTRACT FROM AUTHOR]

Globally, electricity systems are undergoing transitions from robust, carbon-intensive, and firm power conventional systems to uncertain, intermittent, and variable renewable energy integrated low carbon systems. These transitioning electricity systems have moved from a situation of "matching available supply with dynamic demand" to "matching dynamic supply with dynamic demand." These transformations have led to several new challenges - significant mismatch in periods of high supply and high demand, the shift in the method of accessing energy resources for electricity generation from "procure, store and generate when needed" to "generate when available," continuous struggle to match variable supply with variable demand, and installed capacity redundancy, temporal as well as permanent leading to low plant load factors. Actions on the supply-side alone will not be enough to address these challenges and achieve optimal functioning of the electricity system. We need effective demand-side solutions, too, to manage variations in both supply and demand. In this paper, it is proposed to study the effectiveness of demand-side interventions as potential solutions for managing the variabilities introduced by renewable energy mainstreaming. Towards this, we develop a mixed-integer linear programming model to implement and validate emergency and economic demand response (DR) programs. DR options like load curtailment, short-, medium- and long-term load shifting are considered with both incentive-based and penalty-based pricing strategies to influence consumer participation. The Karnataka electricity system is used as a case study for model implementation and validation. The findings suggest that DR interventions are very effective in moderating variability in electricity demand by chopping the peak loads and topping the valleys. Further, benefits include postponement of installed capacity additions, enhanced utilization of available capacity, and minimization of demand variability. • Demand response for managing supply variations due to renewable energy integration. • Studying effectiveness of DR in supply-demand matching using a modelling approach. • Addressing supply variabilities using strategies from demand-side. • Model is validated with a case study of renewable energy dominated system. • Demand response is a win-win intervention for all the stakeholders. [ABSTRACT FROM AUTHOR]

A model is presented which covers the global greenhouse gas emissions (GHG emissions) and the energy consumption (fuels, electricity) in five sectors of end users, industry, transport, buildings, agriculture, and fugitive emissions. The electricity sector is also considered, but the associated GHG emissions are reallocated to the five end users. Different GHG reduction measures were calculated ranging from substitution of coal for electricity generation by renewables, electrification of road transport and buildings, restructuring of the sector industry to finally a 50% reduction of both food waste and meat consumption. To elucidate the consequences of global warming, future emission scenarios were also incorporated. One major conclusion is that the world can only reach the 2-degree climate target if electricity is only produced by renewables, and if transportation, buildings, and the industry are completely electrified by 2050. Compared to today, the electricity production by renewables will then rise by a factor of 11, and the total electricity demand by a factor of 2.4. [ABSTRACT FROM AUTHOR]

• Electricity, hydrogen and methane system will be more integrated in the future. • The developed integrated model considers their interactions and interdependencies. • As proof-of-concept, the model is applied to the Dutch Infrastructure Outlook 2050. • Interweaving electricity, hydrogen and methane system will provide much needed flexibility. The future energy system is widely expected to show increasing levels of integration across differing energy carriers. Electricity, hydrogen, methane and heat systems may become increasingly interdependent due to coupling through conversion and hybrid energy technologies. Market parties, network operators, policy makers and regulators require tools to capture implications of possible techno-economic and institutional developments in one system for the operation of others. In this article, we provide an integrated electricity, hydrogen and methane systems modelling framework focusing on interdependencies between them. The proposed integrated electricity and (renewable) gas system model is a market equilibrium model with hourly price and volume interactions, considering ramp rates of conventional units, variability of intermittent renewables, conversion, transport as well as storage of electricity, hydrogen and methane. The integrated model is formulated as a linear program under the assumption of perfect competition. As proof-of-concept, the model has been applied to a test case consisting of 34 electricity nodes, 19 hydrogen nodes and 22 methane nodes, reflecting the regional governance scenario in the Dutch Infrastructure Outlook 2050 study. The case study also includes different sensitivity analyses with regard to variable renewable capacity, energy demand and biomass prices to illustrate model response to perturbations of its main drivers. This article demonstrates that the interweaving of electricity, hydrogen and methane systems can provide the required flexibility in the future energy system. [ABSTRACT FROM AUTHOR]

• Energy yield simulation of vertical bifacial PV array in 12 European locations. • Comparison with a conventional PV array (monofacial, equator-oriented, tilted). • Evaluation of market revenues based on historic day-ahead market prices. • Evaluation of LCOE and net present value. • Evaluation of system cost reduction from a macroeconomic perspective for Germany. Thanks to the two diurnal generation peaks, vertical bifacial photovoltaic power plants (VBPV) with a north-south axis represent an option to meet the challenges of a mismatch between electricity demand and the generation profile of conventional photovoltaic systems (C-PV). Despite this promising characteristic, it is hardly possible to assess the technical and economic properties of VBPV on the basis of existing studies. The present work is a contribution to close this gap. Among other things, the conducted analyses included electricity generation, market revenues, levelized costs of electricity (LCOE) and the cost-reducing effect of VBPV in a national electricity system. C-PV was used as a benchmark in all analyses. Electricity generation was simulated at twelve European sites in four countries. It has been found that above a latitude of 50° VBPV generates more electricity than C-PV. Due to the higher investments, VBPV had higher LCOE at all locations investigated. The results also showed that VBPV can generate higher revenues in electricity markets with a high share of C-PV capacity, but that these additional revenues are not yet sufficient to compensate for the higher investments. With an optimising electricity market model, it was also found that VBPV can contribute to reducing system costs in an electricity system with high shares of C-PV capacity. Against the background of the increasing importance of PV for the electricity supply of most European countries, these findings are of particular importance. In summary, it can be said that VBPV has significant potential at both the business and macro-economic level and that decision makers should promote the market entry of this technology. [ABSTRACT FROM AUTHOR]

• Integrated stakeholder preferences towards equality into electricity planning. • Framework for opportunity-focused approach for electricity planning. • Novel combination of economics, utility maximization, and least-cost frameworks. • Co-optimized generation and transmission investments under equality preferences. Access to electricity can lead to enhanced education, business, and healthcare opportunities. Governments in emerging economies are often faced with the challenge of increasing access to electricity and reducing system inequality, while operating under severe budget constraints. This paper develops a methodology for finding the optimal expansion of a power system under the objective of maximizing social benefit, as it relates to distributional equality for electricity access, subject to a budget constraint. This contrasts with traditional models, which minimize the cost of satisfying projected electricity demand. We formulate a generation expansion planning problem as a utility-maximization mixed-integer linear program and apply it to a case study analysis of a low-income country with limited electricity infrastructure. We focus our analysis on understanding how the optimal allocation of generation between centralized and distributed resources is impacted by stakeholder preferences toward equality and different budget levels. We find that a high preference for equality leads to lower overall electricity consumption levels, but improved electrification rates due to greater investment (300–750 km increase) in transmission infrastructure. If stakeholders move from a low to a high equality preference then they could see a 72–87% increase in energy access equality rating depending on the budget. Conversely, indifference to equality leads to higher overall consumption levels in urban areas but reduced electrification rates. This methodology can help decision makers evaluate the social trade-offs between improving energy access, reducing energy inequality and poverty, and increasing total electricity consumption when operating under budget constraints in their countries. [ABSTRACT FROM AUTHOR]

• Probabilistic simulation of security of electricity supply in central Europe. • Quasi-absolute levels of security of supply are unlikely to be maintained in future scenarios. • Sensitivity of security of supply towards meteorological influences is increasing. • Dependency on contributions from neighboring countries is growing. • Relevance to coordinate capacity planning on an international level is demonstrated. Against the backdrop of expansions of intermittent renewable energy capacity, planned nuclear phase-outs, and current debates on the additional mothballing of coal-fired power plants in central Europe, there is a substantial increase in uncertainty regarding security of electricity supply. In this context, we present a probabilistic and scenario-based analysis of impacts on security of supply for Germany as a case-study in the medium-term perspective (years 2020, 2022, and 2023). For our analysis, we introduce the energy security assessment module of the JERICHO energy system model from RWTH Aachen University. Our model allows for an hourly probabilistic simulation of security of supply and accounts for stochastic characteristics of (non-)availability of conventional generators, renewable feed-in, electricity demand, and import potentials from central-European countries. To increase the robustness of results, we use a comprehensive dataset of temperature, wind speeds, solar radiation and water levels from weather years 1986 to 2015 as input for our simulations. Our results indicate that Germany is unlikely to maintain its quasi-absolute levels of security of supply in the near future with a mean Loss of Load Expectation (LoLE) of up to 2.6 h in scenario year 2023. However, this does not necessarily imply that future levels of security of supply are insufficient. There is need for further assessments on identifying threshold levels for security of supply, which are economically efficient and acceptable for society. Our results also indicate that the sensitivity of security of supply for meteorological changes increases with reductions of conventional power plants. Finally, we demonstrate that the dependency of security of supply on imports from neighboring countries increases substantially calling for increased international coordination of national energy policies. [ABSTRACT FROM AUTHOR]