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Resilience of power systems is already a key issue that is getting frequent attention all over the world. It is useful to analyze resilience issues not only for bulk supply, but at all levels including at a customer level. This is because distributed energy resources can play a prominent role in enhancing resilience. Although the literature on planning models, tools and data for bulk supply and distribution systems have expanded in recent years, customer-centric planning, e.g., for an individual household, is yet to receive adequate attention. Although solar PV and battery storage at a household level have been analyzed, how these resources can be optimally combined, together with grid supply, from a resilience perspective is the focus of this study. The study demonstrates how a conceptual framework can be developed to show the trade-off between system costs and resilience including its dimensions such as duration, depth and frequency of service outages. A planning model is developed that incorporates multiple facets of resilience and individual customer preferences. The model considers power system resilience explicitly as a constraint. The model is implemented for a household level case study in Miami, Florida. The results show there are complex trade-offs among different dimensions of resilience. The study demonstrates how combined resilience metrics can be formulated and evaluated using the proposed least-cost planning model at a household level to optimize grid supply together with solar, battery storage and diesel generators. The model allows a planner to directly embed a resilience standard to drive the optimal supply mix. These concepts and the modeling construct can also be applied at other levels of planning, including community level and bulk supply system planning.

Mining and mineral processing continues to be a source of lasting environmental problems in many developing economies. Phytoremediation has proven to be a viable strategy to remediate contaminated lands and limit environmental damage, but it has not been widely implemented partially due to social and economic challenges. However, by encouraging phytoremediation with a focus on phytoextraction, it may be possible to rehabilitate contaminated lands while simultaneously providing economic support to local communities. This can be achieved by the sale of phytoextracted metals to fund large-scale phytoremediation, particularly in Sub-Saharan Africa. To this end, this paper provides a conceptual approach for phytoremediation-based mineral recovery and explores the social and economic challenges related to large-scale deployment. The viability of the approach is explored and future work on phytoremediation implementation is defined with the goal of advancing research and collaboration.

Open-source energy models are becoming more widely used for electric power systems planning. The solutions for these models are often computed using commercial optimization solvers, which require licensing fees that can be a potential barrier for certain organizations and researchers. This study explores the ability of the open-source COIN-OR linear programming (CLP) solver to compute solutions for the Regional Energy Deployment System (ReEDS) model—a large-scale, open-access electricity system planning model for the United States developed by the National Renewable Energy Laboratory (NREL). We find that open-source solvers, such as CLP, require some reduction of model size and detail. Although the solutions for reduced-form models may differ from full-featured models, we demonstrate that reduced-form solutions for ReEDS can still provide useful insight about drivers of power sector evolution. This research can help the modeling community better understand how open-source solvers can be applied to large-scale planning tools, and the potential steps that may be required to implement them.

Small-scale battery energy storage systems (BESS), especially for behind-the-meter applications, are still relatively expensive, but we show that it can be a potent solution to render homes resilient to storm related power outages. We present a stochastic programming model formulation to optimize PV/BESS explicitly accounting for resilience benefits these investments entail, over and above their ability to reduce cost of supply. The stochastic optimization considers uncertainties around storm related grid supply failures as well as variability of solar PV. The model includes an embedded Monte Carlo simulation module that considers storm related outage risks using climate model reanalysis data. It is a least-cost planning framework that optimizes selection of BESS, solar PV, grid supply, and diesel generator, from a home-owner's perspective. We present two case studies with low and high storm risks that demonstrate how different risk exposures can impact on the selection of alternative options to build resilience. Duals, or shadow prices, of demand-supply constraints from the model for both normal days and for storm related contingencies, provide interesting insights into the marginal cost of supply that can inform innovative pricing schemes to promote customer level resilience measures. The case study results reveal significant merits of BESS, in combination with PV, to enhance resilience. We find that in low-risk areas like Bethesda, MD, incremental PV and BESS required for a more resilient system can add $79 (4%) to annual electricity costs for a typical household, and a considerably higher $208 (10.6%) in Miami, FL which is at a much greater risk. These options are, however, 27% (in Bethesda) and 20% (in Florida) less expensive than the conventional solution of installing a diesel generator. These results provide insights into the value of BESS as part of a resilient and clean energy solution for households.

This paper presents the design and results of a model that uses household smart meter data, electric vehicle (EV) travel load and charging options, and multiple solar resource profiles, to make decisions on optimal combinations of photovoltaics (PV), battery energy storage systems (BESS) and EV charging strategies. The least-cost planning model is formulated as a stochastic mixed integer programming (MIP) problem that makes first stage decisions on PV/BESS investments, and recourse decisions on purchase/sell from/to the grid to minimize expected household electricity costs. The model undertakes a customer-centric optimization taking into consideration net metering policy, time-of-use grid pricing, and uncertainties around inter-annual variability of solar irradiance. The model adds to the existing literature in terms of stochastic representation of inter-annual variability of solar irradiance, together with BESS capacity optimization, and EV charging mode selection. Three case studies are presented: two for a residential house with and without EV load, and a third for a larger community facility. Results from the model for the first residential house case study are compared with commercially available software to show the impacts of an accurate load profile and different policy parameters. The stochastic feature of the model proves useful in understanding the impact of variability in solar resource profiles on PV sizing. Finally, simulations of alternative EV travel patterns and tariff policies that discourage charging during the evening peak show the efficacy of `super off-peak' pricing being introduced in the state of Maryland.

This paper explores the technical and economic characteristics of an accelerated energy transition to 2050, using new datasets for renewable energy. The analysis indicates that energy efficiency and renewable energy technologies are the core elements of that transition, and their synergies are likewise important. Favourable economics, ubiquitous resources, scalable technology, and significant socio-economic benefits underpin such a transition. Renewable energy can supply two-thirds of the total global energy demand, and contribute to the bulk of the greenhouse gas emissions reduction that is needed between now and 2050 for limiting average global surface temperature increase below 2 °C. Enabling policy and regulatory frameworks will need to be adjusted to mobilise the six-fold acceleration of renewables growth that is needed, with the highest growth estimated for wind and solar PV technologies, complemented by a high level of energy efficiency. Still, to ensure the eventual elimination of carbon dioxide emissions will require new technology and innovation, notably for the transport and manufacturing sectors, which remain largely ignored in the international debate. More attention is needed for emerging infrastructure issues such as charging infrastructure and other sector coupling implications. Keywords: Energy transition, Sustainable development, Energy policy

The COVID-19 pandemic and Russia’s war on Ukraine have impacted the global economy, including the energy sector. The pandemic caused drastic fluctuations in energy demand, oil price shocks, disruptions in energy supply chains, and hampered energy investments, while the war left the world with energy price hikes and energy security challenges. The long-term impacts of these crises on low-carbon energy transitions and mitigation of climate change are still uncertain but are slowly emerging. This paper analyzes the impacts throughout the energy system, including upstream fuel supply, renewable energy investments, demand for energy services, and implications for energy equity, by reviewing recent studies and consulting experts in the field. We find that both crises initially appeared as opportunities for low-carbon energy transitions: the pandemic by showing the extent of lifestyle and behavioral change in a short period and the role of science-based policy advice, and the war by highlighting the need for greater energy diversification and reliance on local, renewable energy sources. However, the early evidence suggests that policymaking worldwide is focused on short-term, seemingly quicker solutions, such as supporting the incumbent energy industry in the post-pandemic era to save the economy and looking for new fossil fuel supply routes for enhancing energy security following the war. As such, the fossil fuel industry may emerge even stronger after these energy crises creating new lock-ins. This implies that the public sentiment against dependency on fossil fuels may end as a lost opportunity to translate into actions toward climate-friendly energy transitions, without ambitious plans for phasing out such fuels altogether. We propose policy recommendations to overcome these challenges toward achieving resilient and sustainable energy systems, mostly driven by energy services.

Direct risks and indirect effects from the COVID-19 pandemic have impacted the operations and resilience of global energy markets. This article considers several aspects of the impacts and responses of these markets as well as energy sector resilience. All major energy markets have been affected, with unprecedented decreases in oil and natural gas demand, alongside operational stress for electric systems. Construction, maintenance, and operations in the industry face personnel constraints and limitations. Meanwhile, the disruptions from public health measures have tested the market’s abilities to reliably manage severe demand destruction. Moving forward, there is a need to include demand shocks within energy resilience planning frameworks, and to prepare for multiple concurrent crises.

In this paper, we compare 2015 satellite-derived natural gas (gas) flaring data with the greenhouse gas reduction targets presented by those countries in their nationally determined contributions (NDC) under the United Nations Framework Convention on Climate Change (UNFCCC) Paris Agreement. Converting from flaring to utilization is an attractive option for reducing emissions. The analysis rates the potential role of reduction of gas flaring in meeting country-specific NDC targets. The analysis includes three categories of flaring: upstream in oil and gas production areas, downstream at refineries and transport facilities, and industrial (e.g., coal mines, landfills, water treatment plants, etc.). Upstream flaring dominates with 90.6% of all flaring. Global flaring represents less than 2% of the NDC reduction target. However, most gas flaring is concentrated in a limited set of countries, leaving the possibility that flaring reduction could contribute a sizeable portion of the NDC targets for specific countries. States that could fully meet their NDC targets through gas flaring reductions include: Yemen (240%), Algeria (197%), and Iraq (136%). Countries which could meet a substantial portion of their NDC targets with gas flaring reductions include: Gabon (94%), Algeria (48%), Venezuela (47%), Iran (34%), and Sudan (33%). On the other hand, several countries with large flared gas volumes could only meet a small portion of their NDC targets from gas flaring reductions, including the Russian Federation (2.4%) and the USA (0.1%). These findings may be useful in guiding national level efforts to meet NDC greenhouse gas reduction targets. Keywords: VIIRS, Gas flaring, Nightfire, Nationally determined contributions, UN climate agreement