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• Most sub-Saharan African countries have low and unequal access to electricity. • Multi-objective optimisation model integrates grid and off-grid electrification. • Solar energy is the cost-minimal strategy to minimise electrification inequality. • Off-grid electrification is a crucial component of all optimal solutions in Uganda. • Uganda's current official expansion plan is infeasible and would be sub-optimal. While electricity access is lowest in developing countries, the academic literature on generation expansion planning (GEP) has been informed almost exclusively by challenges in industrialised countries. This paper presents the first multi-objective, long-term energy planning optimisation model tailored towards national power systems with little existing power infrastructure. It determines the location, type, capacity and timing of power system infrastructure additions. Specifically, three novel generalisations of standard generation planning are introduced: (1) an expansion of the demand constraints to allow for industrial and household electrification rates below 100%, (2) a minimisation of sub-national energy access inequality in conjunction with minimising system costs considering environmental constraints, and (3) an integration of distribution infrastructure, explicitly including both on-grid and off-grid electrification. Using a specifically designed solution algorithm based on the ε-constraint method, the model was successfully applied to the previously unexplored Ugandan national power system case. The results suggest that while it is cost-optimal to maintain highly unequal sub-national access patterns to meet Uganda's official 80% electrification target in 2040, equal access rates across all districts can be achieved by increasing discounted system cost by only 3%. High optimal shares of locationally flexible on-grid and off-grid solar energy enable cheap sub-national shifts of generation capapcity. This paper strongly challenges the Ugandan government's nuclear energy and largely grid-based electrification expansion plans. Instead, it calls for solar concentrated power as a baseload option in the future and a focus on off-grid electrification which the model selects for the majority of household connections in 2040, even in a high-demand scenario. [ABSTRACT FROM AUTHOR]

Highlights • Study the role of electricity (demand and supply) for decarbonisation. • Multiple emission caps to identify shift-points in electricity deployment and costs. • Electrification is fundamental for decarbonisation targets above −50%. • Transport & industry more responsive to electrification to achieve decarbonisation. • Small changes in decarbonisation targets lead to high changes in electricity cost. Abstract The deep decarbonisation of the power sector coupled with electrification of end-use sectors will be crucial towards a carbon neutral economy, as required to achieve the Paris Agreement's goal. Several studies have highlighted the relevance of electrification under deep decarbonisation. However, previous work does not explore what would be the major shifts towards electrification, i.e., in what economic activities it will likely occur and when up to 2050 considering gradually stricter GHG emissions constraints. This is of upmost relevance since relatively small variations in emission caps may trigger substantial modifications in specific components of the energy system, namely the shift for the electrification of a particular energy end-use, with impacts on the power sector's portfolio. In this paper, we analyse the extension of the electrification of the energy system as a cost-effective strategy for deep decarbonisation. We set a large number of increasingly stringent mitigation caps to assess: (i) the degree of electrification of different energy end-uses across all economic activities, (ii) the impact in power sector portfolio and costs and (iii) investment needs. The novelty of this paper relies on the anticipation of electrification of activities traditionally supplied by non-electricity energy carriers, by exploring when and how such transformation may occur in the future, and how much it would cost. We assess the case of Portugal till 2050 by using the TIMES_PT model to generate 50 increasingly stricter decarbonisation scenarios. In the long term, incremental changes (+1%) in more aggressive decarbonisation targets (beyond −70% reduction) induce substantial increase in the share of electrification growth rates. Electric private vehicles, electricity-based steam and heat production in ceramic industrial sector and heat pumps in buildings are the most cost-effective electric technologies. We found that a decarbonisation up to near −80% of 1990′s levels of the Portuguese energy system does not have a significant impact on the power sector unit costs, and does not surpass historic values for some years. However, it should be noted that incremental changes (+1%) in more aggressive decarbonisation targets may increase sharply electricity costs in 2050 (+9%). Thus, focusing in only few scenarios may narrow the role of electrification (or other mitigation options) and its associated costs for deep decarbonisation. This paper allows researchers, planners and decision makers to enhance awareness regarding the relevance and cost-effectiveness of electrification under decarbonisation, namely its feasibility and affordability, providing fruitful insights. [ABSTRACT FROM AUTHOR]

The development of space and water heating combination heat pumps capable of generating water temperatures high enough for convective heat emitters will enable more cost-effective and equitable decarbonization solutions for electrifying multifamily buildings. In this paper, multifamily building models and a charge-sensitive mechanistic cycle model of a combination heat pump are developed, and the system performance is predicted based on the models. Unlike other state-of-the-art residential heat pumping equipment, the modeled combination heat pump using an economized, fluid-injected variable-speed compressor can achieve higher temperature lifts of 40° – 85°C, with lower installation costs and complexity. The model predicted heating coefficient of performance (COP h) is 2.1 at an ambient temperature of − 15°C with a high-temperature lift of nearly 85°C, and a seasonal coefficient of performance in heating mode (SCOP h) ranges from 2 – 4 for different locations. The system shows 30% – 90% lower CO 2 eq emissions over a condensing gas boiler and 9% – 13% lower projected installation costs than two separate space and water heat pumping appliances. • A drop-in boiler replacement high-temperature combination heat pump is introduced. • The system can achieve temperature lifts of 80°C for convective hydronic heating. • The seasonal coefficient of performance in heating mode ranges from 2 – 4. • Enabling lower CO 2 eq emissions by 30% – 90% compared to gas boilers. • Enabling lower installation costs by 10% compared to separate heating systems. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries have found their way into myriad sectors of industry to drive electrification, decarbonization, and sustainability. A crucial aspect in ensuring their safe and optimal performance is monitoring their energy levels. In this paper, we present the first study on predicting the remaining energy of a battery cell undergoing discharge over wide current ranges from low to high C-rates. The complexity of the challenge arises from the cell's C-rate-dependent energy availability as well as its intricate electro-thermal dynamics especially at high C-rates. To address this, we introduce a new definition of remaining discharge energy and then undertake a systematic effort in harnessing the power of machine learning to enable its prediction. Our effort includes two parts in cascade. First, we develop an accurate dynamic model based on integration of physics with machine learning to capture a battery's voltage and temperature behaviors. Second, based on the model, we propose a machine learning approach to predict the remaining discharge energy under arbitrary C-rates and pre-specified cut-off limits in voltage and temperature. The experimental validation shows that the proposed approach can predict the remaining discharge energy with a relative error of less than 3% when the current varies between 0 ∼ 8 C for an NCA cell and 0 ∼ 15 C for an LFP cell. The approach, by design, is amenable to training and computation. • A new C-rate-dependent definition of remaining discharge energy for lithium-ion batteries. • A new machine learning approach to predict the remaining discharge energy. • A novel way to train the machine learning approach using synthetic data generated by hybrid physics+machine learning models. • Experiments and simulations show the approach is highly accurate across broad C-rate ranges. [ABSTRACT FROM AUTHOR]

In the context of electrification for different sectors, demand-side management (DSM) strategies are acknowledged as primary strategies to ensure the stability and reliability of the utility grid. Urban building energy modelling (UBEM) emerges as a critical tool for utilities to assess the impact of these strategies on the building sector's energy consumption and flexibility. However, relying on the developed models for this task is applicable only when detailed occupant-related inputs are integrated into the model. To this end, this paper aims to develop a framework to integrate high-resolution occupancy schedules into UBEM and showcase the application of the developed models in evaluating DSM strategies with different scenarios. The developed framework is applied to a mixed-use district in Montreal, Canada with 112 buildings as a case study. The main objectives of this study are 1) developing an urban scale high-resolution occupancy profile generator representative of Canadian commercial buildings using mobile positioning data, 2) investigating the diversity between the generated profiles of buildings within the same type, 3) developing a method to integrate the generated profiles into the Canadian commercial archetypes, and 4) evaluating the applicability of the developed model in evaluating DSM strategies by investigating the effect of occupant-centric control and occupancy-based demand response strategies on the modelled district energy use. The results of this study serve as a preliminary investigation into the crucial role that occupancy patterns can play in maximizing building energy flexibility with an estimated reduction in district peak demand by up to 17%. This study also paves the way for future research incorporating occupant feedback and comfort requirements for a more precise exploration of the proposed strategy. • Generating high-resolution occupancy profiles for commercial buildings using mobile positioning data and validating the results with ground-truth data. • Analyzing the diversity of the occupancy schedules generated from over 10,000 buildings. • Developing a heuristic UBEM for 112 buildings to evaluate demand-side management strategies at an urban scale. • Quantifying the effect of occupant-centric control and occupancy-based demand response strategies on the energy use of buildings and the district. [ABSTRACT FROM AUTHOR]

Highlights • A novel PMSM demagnetization and efficiency estimation framework is established. • The efficiency reduction pattern under different demagnetization levels is analyzed. • The impact of demagnetization on the powertrains’ energy efficiencies is evaluated. • A comprehensive comparative study of the energy efficiency is conducted. Abstract Due to possible overheat, abrasion or mechanical vibrations, demagnetization fault is inevitable in permanent magnet synchronous motors (PMSMs), which could greatly decrease the motor’s efficiency and hence an electrified vehicle’s performance. This paper, from an energy efficiency point of view, proposes to analyze the tolerance ability of different electrified vehicles on motor demagnetization faults, via PMSM flux density degradation modeling, efficiency estimation and dynamic programming (DP) based powertrain energy management. The relationship between different demagnetization levels and resultant motor efficiencies is obtained, and analyzed according to the motor operation area. Demagnetized PMSM is adopted in a pure electric vehicle (PEV), a hybrid electric vehicle (HEV) and a plug-in hybrid electric vehicle (PHEV) for energy efficiency analysis. Tolerance analysis indicates that the powertrain efficiency decrease caused by motor demagnetization is more severe under urban driving conditions, especially with PEV and PHEV configurations compared with HEV. A demagnetization threshold investigation is also given in this paper. [ABSTRACT FROM AUTHOR]

In the transition from fossil fuel to electrified heating, a concerning trend is emerging in certain regions of the US. Owners of buildings with gas-based systems leave them in place after adding heat pumps (HPs). Existing control solutions for these hybrid (dual fuel) systems are rudimentary and fall short of realizing the full carbon reduction potential of these systems. Model predictive control (MPC) is often regarded as the benchmark for achieving optimal control in integrated systems. However, in the case of small-medium commercial buildings (SMCBs), the control and communication infrastructure required to facilitate the implementation of such advanced controls is often lacking. This paper presents a field implementation of easy-to-deploy MPC for a dual fuel heating system consisting of HPs and a gas-fired furnace (GF) for SMCBs. The control system is deployed on an open-source middleware platform and utilizes low-cost sensor devices to be used for real SMCBs without major retrofits. We demonstrated this MPC in a real office building with 5 HPs and 1 GF for 2 months. The test results showed that MPC reduced 27% of cost while completely eliminating GF usage by shifting 23% of the thermal load from occupied-peak time to non-occupied-non-peak times. • Implementation of a practical MPC for a heat pump-based dual fuel system in a small commercial building. • Experimental demonstration of a practical MPC with low-cost sensor retrofits for 2 winter months. • Achievement of 27% energy cost saving and 23% load shifting from occupied-peak time to non-occupied-non-peak time. • Elimination of natural gas usage with GHG emissions reduction of ∼ 52 kgCO2/month. [ABSTRACT FROM AUTHOR]

The electrification of residential energy demand for heating and transportation is expected to increase peak load and require additional generation and transmission capacities. Electrification also provides an opportunity to increase demand response. With a focus on household electricity consumption, we analyse the contribution of appliances and new services, such as individual heat pumps and electric vehicles, to peak consumption and the need for demand response incentives to reduce the peak. Initially, the paper presents a new model that represents the hourly electricity consumption profile of households in Denmark. The model considers hourly consumption profiles for different household appliances and their contribution to annual household electricity consumption. When applying the model to an official scenario for annual electricity consumption, assuming non-flexible consumption due to a considerable introduction of electric vehicles and individual heat pumps, household consumption is expected to increase considerably, especially peak hour consumption is expected to increase. Next the paper presents results from a new experiment where household customers are given economic and/or environmental incentives to shift consumption to or away from specified hours. The experiment focuses on the present classic consumption and shows that household customers do react to incentives, but today the flexibility of the classic consumption is limited. Considering electric vehicles and individual heat pumps, for an individual household, the consumption of each of these technologies roughly doubles the household’s consumption and considerably increases their potential for flexibility. Thus, in order to introduce incentives for demand flexibility, while considering reducing peak consumption, policy makers should initially focus on households that have a heat pump and/or an electric vehicle. [ABSTRACT FROM AUTHOR]

System inertia reduction, driven by the integration of renewables, imposes significant challenges on the primary frequency control. Electrification of road transport not only reduces carbon emission by shifting from fossil fuel consumption to cleaner electricity consumption, but also potentially provide flexibility to facilitate the integration of renewables, such as supporting primary frequency control. In this context, this paper develops a techno-economic evaluation framework to quantify the challenges on primary frequency control and assess the benefits of EVs in providing primary frequency response. A simplified GB power system dynamic model is used to analyze the impact of declining system inertia on the primary frequency control and the technical potential of primary frequency response provision from EVs. Furthermore, an advanced stochastic system scheduling tool with explicitly modeling of inertia reduction effect is applied to assess the cost and emission driven by primary frequency control as well as the benefits of EVs in providing primary frequency response under two representative GB 2030 system scenarios. This paper also identifies the synergy between PFR provision from EVs and “smart charging” strategy as well as the impact of synthetic inertia from wind turbines. [ABSTRACT FROM AUTHOR]

Vehicle electrification has to be addressed to reduce dependence on fossil fuels and meet future emission regulations. Pure electric vehicles still have many limitations, but hybrid vehicles are the optimum transference scheme. An over-expansion cycle (Atkinson or Miller) engine is the most suitable for hybrid vehicles. Compared with a conventional Otto cycle engine, an over-expansion cycle engine can realize a larger expansion ratio and thus, a higher thermal efficiency while maintaining a normal effective compression ratio to avoid the knock. Basics for the Atkinson and Miller cycles are introduced first. An in-depth survey on mechanical realizations for the over-expansion cycle is conducted. Challenges and general recommendations for real applications of these mechanical realizations are presented. After a comprehensive review of the advantages and applications of the “Atkinson cycle effect” in load control, reducing NOx formation and suppressing the knock, primary problems are discussed and some novel strategies are provided. Prospective technical solutions that handle the reduced effective compression ratio and power density for over-expansion cycle engines are studied and discussed. Finally, in the potential application fields of range-extended electric vehicles and cogeneration plants, a significant problem is presented; the efficiency optimum working points for the engine and generator do not match. A multi-disciplinary design and optimization methodology is provided to resolve the problem. The main objective of this paper is to explore the critical problems, challenges and prospective solutions that push forward broader applications for over-expansion cycle engines. This paper can be used as a critical review of the current state-of-the-art research for over-expansion cycle engines and also as guidance towards future research directions in this domain. [ABSTRACT FROM AUTHOR]

While in the developed countries electrification is paving the way for progress and prosperity, nowadays electricity is still not accessible for about 18% of the world’s population. Lack of power grids is the main reason that prevents millions in remote areas in developing countries from using electricity for the daily basic needs. PV systems provide an effective solution for these regions, but affordability remains an issue. This barrier can be widely overcome on the short term by limiting PV power supply to very high added value applications and by properly exploiting innovations, especially in energy efficiency and cost reductions. Additional to that, the long-term perspectives of off-grid PV are very favourable based on its ongoing technological improvements and cost reductions. This paper studies four off-grid PV cases of which each could cover a combination of basic energy needs regarding light, cooking, food conservation and electronic appliances. Case I considers a system that supplies power for LED lamps and electronic devices. Accordingly, Case II adds a fridge and Case III an electric rice cooker to Case I, while Case IV adds both. The paper elaborates on available technologies and future developments regarding all components in order to assess the long term evolution and potential of these applications, most specifically how their affordability would evolve over time. The modelling and optimization of the four cases are performed using the software iHOGA, which is an efficient tool to provide the lowest cost solution for off-grid PV systems. The use of iHOGA for the four cases and the installation years 2020, 2030 and 2040, taking thereby into account different developing regions, provide an evolutionary techno-economic assessment of these applications and a clear picture about the developments to be expected from off-grid PV in general. [ABSTRACT FROM AUTHOR]

The Electrical Vehicle (EV) has become a potential solution for enhancing the sustainability of our road transportation, in view of the environmental impacts traditional vehicles have regarding emissions and use of fossil fuel dependence. However, the widespread use of EVs is still restrained by the energy storage technologies, and the electrification of road transportation is still in its early stages. This paper focuses on the technical aspects related to the ‘electrification of roads’ (called ‘eRoads’) infrastructure that aims to diminish the limitations for using EVs. A historical overview of the technology development towards the electrification of road transportation is presented, along with an overview of prospective technologies for implementing an eRoad charging infrastructure. Of these, the Inductive Power Transfer (IPT) technology is examined in further details. The main objective of this paper is to explore the potential knowledge gaps that need to be filled for a successful integration of IPT technology within actual road infrastructure. As such, this paper can be used as an overview of the current state-of-the-art of eRoad infrastructure and also as guidance towards future research directions in this domain. [ABSTRACT FROM AUTHOR]

The transition to a deeply decarbonized energy system requires coordinated planning of infrastructure investments and operations serving multiple end-uses while considering technology and policy-enabled interactions across sectors. Electricity and natural gas (NG), which are vital vectors of today's energy system, are likely to be coupled in different ways in the future, resulting from increasing electrification, adoption of variable renewable energy (VRE) generation in the power sector and policy factors such as cross-sectoral emissions trading. This paper develops a least-cost investment and operations model for joint planning of electricity and NG infrastructures that considers a wide range of available and emerging technology options across the two vectors, including carbon capture and storage (CCS) equipped power generation, low-carbon drop-in fuels (LCDF) as well as long-duration energy storage (LDES). The model incorporates the main operational constraints of both systems and allows each system to operate under different temporal resolutions consistent with their typical scheduling timescales. We apply our modeling framework to evaluate power-NG system outcomes for the U.S. New England region under different technology, decarbonization goals, and demand scenarios. Under a global emissions constraint, ranging between 80%–95% emissions reduction compared to 1990 levels, the least-cost solution relies significantly on using the available emissions budget to serve non-power NG demand, with power sector using only 14%–23% of the emissions budget. Increasing electrification of heating in the buildings sector results in greater reliance on wind and NG-fired plants with CCS and results in similar or slightly lower total system costs as compared to the business-as-usual demand scenario with lower electrification of end-uses. Interestingly, although electrification reduces non-power NG demand, it leads to up to 24% increase in overall NG consumption (both power and non-power) compared to the business-as-usual scenarios, resulting from the increased role for CCS in the power sector. The availability of low-cost LDES systems reduces the extent of coupling of electricity and NG systems by significantly reducing fuel (both NG and LCDF) consumption in the power system compared to scenarios without LDES, while also reducing total systems costs by up to 4.6% for the evaluated set of scenarios. • A decarbonized joint electric power and natural gas model is developed. • Impact of electrification, coupling constraints, and new technologies are analyzed. • Joint planning with cross-sectoral emission limit is the most cost-effective pathway. • Increased use of carbon capture technologies can increase overall NG consumption. [ABSTRACT FROM AUTHOR]

• A comprehensive review of different willingness to pay (WTP) study methods In rural electrification studies presented. • The paper introduces a common measurement parameter, unit of service, that combines electricity and telecommunications in one service package to establish a link between customers' preferred price and profitability for the service provider. • The paper proposed a methodology for microgrid developers to adequately justify their investment plan and minimize the risks of non-payments. Despite the recent development, electrification of rural sub-Saharan Africa remains a major challenge because of remote locations and the low income of the population in those areas. There is a clear need for novel approaches for addressing this challenge. This paper proposes a new method for analysing the feasibility of microgrid projects based on consumers' expressed willingness to pay for the service in rural sub-Saharan Africa and microgrid project developers' willingness to accept. The paper also introduces a common measurement parameter, unit of service, that combines electricity and telecommunications in one service package to establish a link between customers' preferred price and profitability for the service provider. Further, the proposed methodology is used to analyse a study case located in rural Namibia. The results suggest that there is a price range for the service where a microgrid project can be feasible while satisfying local consumers' willingness to pay for the service. By using the proposed methodology, microgrid developers can adequately justify their investment plan and minimize the risks of non-payments. [ABSTRACT FROM AUTHOR]

• Optimal sizing methodology for solar home system (SHS) taking into account battery lifetime. • Genetic algorithm-based multi-objective optimization approach. • Optimal SHS sizing for each tier of the multi-tier framework (MTF) for household electricity access. • Limits of standalone SHS for electrification identified for higher tiers of MTF. With almost 1.1 billion people lacking access to electricity, solar-based off-grid products like Solar Home Systems (SHS) have become a promising solution to provide basic electricity needs in un(der)-electrified regions. Therefore, optimal system sizing is a vital task as both oversizing and undersizing a system can be detrimental to system cost and power availability, respectively. This paper presents an optimal SHS sizing methodology that minimizes the loss of load probability (LLP), excess energy dump, and battery size while maximizing the battery lifetime. A genetic algorithm-based multi-objective optimization approach is utilized to evaluate the optimal SHS sizes. The potential for SHS to cater to every tier of the Multi-tier framework (MTF) for measuring household electricity access is examined. The optimal system sizes for standalone SHS are found for different LLP thresholds. Results show that beyond tier 2, the present day SHS sizing needs to be expanded significantly to meet the load demand. Additionally, it is deemed untenable to meet the electricity needs of the higher tiers of MTF purely through standalone SHS without compromising one or more of the system metrics. A way forward is proposed to take the SHS concept all the way up the energy ladder such that load demand can also be satisfied at tier 4 and 5 levels. [ABSTRACT FROM AUTHOR]

Highlights • We provide a resilience framework for rural power systems in emerging economies. • We provide an overview of general, energy system and rural resilience frameworks. • This study identifies the key technological, social and economic resilience aspects. • We provide a tool for energy practitioners, NGOs, and policy in emerging economies. Abstract Infrastructure and services within urban areas of developed countries have established reliable definitions of resilience and its dependence on various factors as an important pathway for achieving sustainability in these energy systems. However, the assessment, design, building and maintenance of power systems situated in rural areas in emerging economies present further difficulties because there is no a clear framework for such circumstances. Aiming to address this issue, this paper combines different visions of energy-related resilience both in general and under rural conditions in order to provide a robust practical framework for local and international stakeholders to derive the right actions in the rural context of emerging economies. An in-depth review is implemented to recompile information of resilience in general, in energy systems and in rural areas in particular, and a number of existing frameworks is also consulted. In order to acknowledge the particular circumstances and identify the important factors influencing the resilience of rural electrification in emerging economies, a holistic rural power system resilience framework is developed and presented. This consists of twenty-one indicators for technical resilience, eight indicators for social resilience, and thirteen indicators for economic resilience. This framework can be used by system owners and operators, policy makers, NGOs and communities to ensure the longevity of power systems. This work also paves the way for the creation of appropriate and effective resilience standards specifically targeted for application in these regions - aiming to achieve the delivery of global and local sustainability goals. [ABSTRACT FROM AUTHOR]

Highlights • Microgrids can be a valid alternative to grid extension for rural electrification. • Advance dispatch strategies and proper system design can significantly reduce costs. • We propose a new catalog-based microgrid design optimization algorithm. • Yearly performance is assessed via a realistic Rolling Horizon simulation. • The algorithm is applied to optimal regional electrification planning. Abstract Off-grid microgrids constitute an increasingly viable alternative to grid extension for rural electrification. Thanks to significant reductions in hardware cost, energy cost for off-grid users in areas sparsely covered by the national grid has become competitive with grid-connection once infrastructural costs are accounted for. Microgrids offer advantages also from an environmental and social point of view, featuring generation systems with high renewable penetration and facilitating the involvement of local communities in development projects. To further enhance microgrids economic competitiveness, it is necessary to devise innovative control strategies and design algorithms, that can ensure an optimal operating performance at the lowest overall cost. In this paper we present a new predictive design and dispatch optimization algorithm based on Mixed Integer Linear Programming (MILP). The new method is compared to a previously developed heuristic methodology, applying both to the design and yearly performance estimation of local microgrids, characterized on the basis of real data relative to a sub-Saharan African region. The potential advantages of including the proposed microgrid design method in a regional electrification planning model are illustrated in a realistic case example. [ABSTRACT FROM AUTHOR]

• High-resolution map illustrates 16.8 TWh/year electricity demand for irrigation, refrigeration, incubation, and crop processing across Sub-Saharan Africa. • Irrigation pumping dominates end uses, with demand concentrated in semi-arid regions with high crop production. • Microgrid system sizing and costs compared for hundreds of off-grid sites across Kenya and Zambia, with and without inclusion of agricultural load. • Microgrid case study shows that serving agricultural end-uses along with community load has minimal impact on cost of energy. The use of advanced energy technologies for agricultural purposes—such as irrigation, refrigeration, crop processing, and egg incubation—has the potential to increase crop yield, reduce vulnerability to changing precipitation patterns, increase shelf life, strengthen income and employment opportunities in rural areas, and reduce emissions by displacing fossil fuel-based technologies. These productive uses of energy (PUE) in remote areas could potentially be powered by microgrids that additionally serve otherwise unelectrified communities, most of which are located in rural Sub-Saharan Africa. In this paper, we use high-resolution geospatial data to estimate the end-use electricity demand for a range of agricultural PUE across Sub-Saharan Africa, and we share these data in an open-access mapping tool. Next, we use REopt®, a techno-economic optimization model of energy systems, to determine the cost and system sizing implications of incorporating agricultural PUE into microgrid designs in Kenya and Zambia. We estimate the upper bound of agricultural PUE demand for irrigation, milling, shelling, refrigeration, and egg incubation across Sub-Saharan to be 16.8 TWh/yr. We find that incorporating local agricultural PUE into microgrid system designs increases the required system sizing while having minimal impact on the levelized cost of energy of these systems. Our analysis is the first to demonstrate the PUE potential in the agricultural sector at a 10x10-kilometer resolution across Sub-Saharan Africa and to show, at scale, how site-specific PUE can impact the cost and sizing of microgrids that are otherwise deployed to serve local household and community load. [ABSTRACT FROM AUTHOR]

Highlights • An architecture of microgrid energy management system was established. • The architecture was designed for China in the context of Energy Internet. • Two-stage robust optimization model and algorithm were proposed. • Case study was conducted using a real case of Beijing Jinfeng Industrial Park. Abstract In order to address the practical challenges posed by the increasing penetration of distributed energy resources and electric vehicles, the evolution from traditional power systems to Energy Internet and the rapidly changing market and policy environments in China, this paper proposes an Energy-Internet-oriented architecture of microgrid energy management system for China, considering specific technical requirements, markets and policies in China. Following the proposed architecture, the robust operation of a multi-energy microgrid with renewable energy and load uncertainties is formulated as a two-stage robust optimization problem and the column and constraints generation algorithm is used to solve it. A real-life case study in China, Beijing Goldwind Industrial Park, is conducted to demonstrate the proposed architecture and method. Simulation results show that by exporting surplus generation to the external customers and bulk power grid, as an important feature of the market dimension of the proposed architecture, the revenue of the microgrid is improved significantly compared to the traditional business mode. Moreover, as important features of technology and regulation dimensions of the proposed architecture, it is shown that smart charging of electric vehicles is able to improve the revenue of the microgrid as well. Finally, the results also show that the proposed robust operation method achieves robustness at the cost of lower revenue in non-worst scenarios. [ABSTRACT FROM AUTHOR]

Highlights • A novel data-driven load and wind power scenario generation framework is proposed. • A sufficient number of unseen but important scenarios are generated for TEP. • Interspatial dependencies between loads and wind power output are accurately captured. • The method can identify near optimal investment decisions with higher net benefits. Abstract Transmission expansion planning (TEP) is facing unprecedented challenges with the rise of integrated renewable energy resources (RES), flexible load elements, and the potential electrification of transport and heat sectors. Under this reality, the inadequate information of the stochastic parameters’ behavior may lead to inefficient expansion decisions, especially in the context of very high renewable penetration. This paper proposes a novel data-driven scenario generation framework for the TEP problem to generate unseen but important load and wind power scenarios while capturing inter-spatial dependencies between loads and wind generation units’ output in various locations, using a vine-copula based high-dimensional stochastic variable modeling approach. The superior performance of the proposed model is demonstrated through a case study on a modified IEEE 118-bus system. The expected result of using the expected value problem solution (EEV) and the net benefits of transmission expansion (NBTE) are used as the evaluation metrics to quantitatively illustrate the advantages of the proposed approach. In addition, the case of very high wind penetration is carried out to further highlight the importance of the multivariate stochastic dependence of load and wind power generation. The results demonstrate that the proposed scenario generation method can result in near-optimal investment decisions for the TEP problem that make more net benefits than using limited number of historical data. [ABSTRACT FROM AUTHOR]

This paper provides pathways of the carbon content of electricity extracted from the Intergovernmental Panel on Climate Change’s fifth Assessment Report scenarios database. It demonstrates three policy-relevant aspects of the carbon content of electricity that are implicit in most integrated assessment model results but under-discussed in academia and the policy debate. First, climate stabilization at any level from 1.5 °C to 3 °C requires the carbon content of electricity to decrease quickly and become almost carbon-free before the end of the century. As such, the question for policy makers is not whether to decarbonize electricity but when and how to do so. Second, decarbonization of electricity is still possible and required if some of the key zero-carbon technologies—such as nuclear power or carbon capture and storage—turn out to be unavailable. Third, progressive decarbonization of electricity is part of every country’s cost-effective means of contributing to climate stabilization. The pathways of the carbon content of electricity reported here can be used to benchmark existing decarbonization targets, such as those set by the European Energy Roadmap or inform new policies in other countries. They can also be used to assess the desirable uptake rates of electric and plug-in hybrid vehicles, electric stoves and heat pumps, industrial electric furnaces, or other electrification technologies. [ABSTRACT FROM AUTHOR]

Building sector accounts for nearly 30% of global final energy usage, and its energy demand is expected to keep growing with the increasing population and expanding tertiary industry in the coming decades. To achieve global climate mitigation goal, building sector will need to fully tap its potential on energy conservation and CO 2 reduction. This paper simulated the transformation pathways of global energy system under 2-degree and 1.5-degree climate targets with Global TIMES model, classified the 14 model regions into high, middle and low income region, and analyzed the main features and key challenges of each region’s building sector transition pathways. Modeling results show that: (1) For global building sector, 32 Gt of CO 2 emissions reduction is required between 2010 and 2050 by 2-degree target, additional 28 Gt of reduction would be essential to achieve 1.5-degree target; (2) High income region would take the lead in building sector’s CO 2 mitigation, while low income region may be increasingly important when the carbon constraint gets more stringent; (3) Electrification is a critical approach in building sector’s low carbon transition, by 2050, the share of electricity in final energy consumption is expected to reach 44%, 54% and 59% in reference, 2-degree and 1.5-degree scenarios respectively. [ABSTRACT FROM AUTHOR]

Current trends in both commercial and military ship development have focused on ship electrification. A challenge for electric-ship propulsion systems, however, is large propulsion-load fluctuations. To address this issue, this paper explores a new solution, namely a combined battery and flywheel (B/FW) hybrid energy storage system (HESS) as a buffer to isolate load fluctuations from the shipboard network. Our two main objectives, power-fluctuation compensation and energy saving under various operating constraints, are formulated as a multi-objective optimization problem. Pareto fronts, which illustrate the trade-offs between the main objectives, are obtained by using dynamic programming with the weighted sum method. To quantitatively analyze the performance of B/FW HESS, a comparative study is performed under different sea conditions, where a battery/ultra-capacitor (B/UC) HESS configuration is used as a reference in performance evaluation. Simulation results show the feasibility and effectiveness of B/FW to mitigate the load fluctuations for all-electric ships, especially at high sea states. Furthermore, a model predictive control (MPC) algorithm is developed to facilitate real-time implementation of the proposed solution. A performance comparison between the proposed MPC energy management strategy and the global dynamic programming is performed, and this comparison demonstrates the effectiveness of the proposed MPC strategy. [ABSTRACT FROM AUTHOR]

The constant need to reduce emissions in the automotive sector has driven the electrification of powertrain and chassis. To comply with this trend and decrease the bound even further, the present paper proposes the use of hydraulic regenerative shock absorbers for automotive suspension systems. The conversion of linear into angular motion and the suitable control of an integrated electric machine allow to transform part of the vibrational energy into electricity. In these damping devices, the key element is the motor-pump unit that is interfaced onto a conventional hydraulic cylinder architecture. Hence, the proposed research focuses on this component by investigating different design aspects in all the domains of interest. The objective is to optimize the energy conversion efficiency of the unit without affecting its damping control property. To give means of validation, a motor-pump prototype is built and experimentally characterized through a dedicated test rig. [ABSTRACT FROM AUTHOR]

The power sector in many developing countries face challenges of a fast-rising electricity demand in urban areas and an urgency of improved electricity access in rural areas. In the context of climate change, these development needs are challenged by the vital goal of CO 2 mitigation. This paper investigates plausible trade-offs between electrification and CO 2 mitigation in a developing country context, taking Indonesia as a case study. Aligned with the 2015 Paris Agreement, the Government of Indonesia has announced its voluntary pledge to reduce 29% of its GHGs emissions against the business as usual scenario by 2030. 11% of this should be attained by the energy sector. We incorporate the Indonesian Paris pledge into the modelling of capacity expansion of the Java-Bali power system, which is the largest power system in Indonesia. The LEAP model is used for the analysis in this study. Firstly, we validate the LEAP model using historical data of the national electricity system. Secondly, we develop and analyse four scenarios of the Java-Bali power system expansion from the base year 2015 through to 2030. These include a reference scenario (REF) to reflect a continuation of the present energy mix (REF), then a shift from coal to natural gas (NGS) (natural gas), followed by an expansion of renewable energy (REN) and, finally, the least-cost option (OPT). The shift to natural gas decreases future CO 2 emissions by 38.2 million ton, helping to achieve the CO 2 mitigation target committed to. Likewise, an escalation of renewable energy development in the Java-Bali islands cuts the projected CO 2 emissions by 38.9 million ton and, thus, assures meeting the target. The least-cost scenario attains the targeted emission reduction, but at 33% and 52% lower additional costs compared to NGS and REN, respectively. The cost-effectiveness of CO 2 mitigation scenarios range from 14.9 to 41.8 US$/tCO 2 e. [ABSTRACT FROM AUTHOR]

This paper explores the willingness of consumers to pay for three key electrification technologies – electric vehicles (EVs), rooftop solar (RPV), and air source heat pumps (HPs), using a nuanced two-step model. The first step focuses on whether or not a consumer would consider purchasing a technology, paying particular attention to thresholds and constraints. For those who are willing to pay, the second step focuses on the minimum return-on-investment (ROI) that they require, based on gradations of supporting circumstances such as sustainable lifestyle and climate concern. The second step conjoins respondents' willingness to pay if their energy savings are sufficient and those who are willing to pay if a supportive policy were available. We use the two-step Heckman model to test the validity of our framework using data from an original survey of 1800 adults living in the state of Georgia, USA in 2021. While various enablers and constraints play significant roles in the first step as thresholds, the internal motivators highlighted in the theories of innovation diffusion and planned behavior dominate the minimum ROI. The most consistently significant predictors of being willing to pay for EVs, RPVs, and HPs are having knowledge about energy systems, being risk-tolerant, not being a Republican, living a highly sustainable lifestyle, and believing that there is a climate urgency. Additionally, technology-specific infrastructure constraints are impactful, such as heating with natural gas when considering a conversion to electric heat pumps. Several common predictors affect ROI across all three technologies (e.g., having a sustainable lifestyle and being younger). Common predictors can be used to effectively guide policy design and deployment programs, and may help to promote other electric devices. • Consumer WTP for electrification technologies is tested using Heckman two-step model. • Attitudes, beliefs, knowledge of energy systems, and constraints affect consumer WTP. • Sustainable lifestyle, risk tolerance, and age affect valuation of most technologies. • Common attributes can guide policy design and promote further adoption. [ABSTRACT FROM AUTHOR]

The public transport market in most global cities is dominated by diesel buses, which can have significant adverse impacts on the environment and human health. To reach carbon neutrality, many public transport operators are attempting to replace their existing bus fleets with electric buses (EBs). This paper develops a generic bus replacement model to help public transit (PT) operators progressively renew their existing bus fleets so as to reach particular electrification goals within a given planning horizon. Unlike existing studies, our model more comprehensively considers the external costs, which include external climate costs, external health costs and used battery recycling costs/profits. Furthermore, by incorporating different incentives, variants on the model can be adopted to investigate how different government incentives can affect the replacement behaviors of PT operators. Finally, our proposed approach is applied to real-world data from a bus operator in Singapore which is aiming to achieve a 100% cleaner energy bus fleet within 20 years. The experiments indicate that an EB subsidy can effectively accelerate the electrification process, and a 25% EB subsidy could achieve full electrification eight years earlier than if no EB subsidy were applied. Moreover, we explore how the diesel tax and diesel bus (DB) tax affect EB replacement. Our experiments suggest a 10% diesel tax rate can achieve good results but imposing a DB tax will not encourage PT operators to purchase EBs earlier. • A novel model for the bus fleet replacement problem is proposed. • Various external costs that borne by the society and environment are considered. • Our approach evaluates different policy incentives. • Our real case study provides managerial insights for PT operators and policymakers. [ABSTRACT FROM AUTHOR]

Threats against power grids continue to increase, as extreme weather conditions and natural disasters (extreme events) become more frequent. Hence, there is a need for the simulation and modeling of power grids to reflect realistic conditions during extreme events conditions, especially distribution systems. This paper presents a modeling and simulation platform for electric distribution grids which can estimate overall power demand during extreme weather conditions. The presented platform's efficacy is shown by demonstrating estimation of electrical demand for (1) Electricity Reliability Council of Texas (ERCOT) during winter storm Uri in 2021, and (2) alternative hypothetical scenarios of integrating Distributed Energy Resources (DERs), weatherization, and load electrification. In comparing to the actual demand served by ERCOT during the winter storm Uri of 2021, the proposed platform estimates approximately 34 GW of peak capacity deficit. 1 1 These numbers are consistent with state-of-the-art prediction results published in the literature (Gruber et al., 2022). For the case of the future electrification of heating loads, peak capacity of 78 GW (124% increase) is estimated, which would be reduced to 47 GW (38% increase) with the adoption of efficient heating appliances and improved thermal insulation. Integrating distributed solar PV and storage into the grid causes improvement in the local energy utilization and hence reduces the potential unmet energy by 31% and 40%, respectively. • Modeling and simulation platform to estimate power demand during extreme weather conditions. • Demonstration of the platform on 2021 winter storm Uri conditions. • 34 GW of peak capacity deficit estimated for the current Electricity Reliability Council of Texas (ERCOT) grid. • 78 GW peak capacity deficit for load electrification, reduced to 47 GW with technology efficiency • Integrating distributed solar PV and storage reduces the potential unmet energy by 31% and 40%, respectively. [ABSTRACT FROM AUTHOR]

• Isolating the impact of heat pumps on national electricity under historic weather. • Validations with measured data indicate the best heat demand generation method. • 41 % heat pumps would increase Great Britain's winter electricity demand by 30 TWh. • Year-to-year variability of electricity demand increases by 37 %. • 25 % inaccuracy of future peak electricity demand estimates. Amongst all future developments it is the electrification of heat that is anticipated to have the largest impact on seasonal and interannual electricity demand. There is therefore a need to accurately quantify and assess this impact. This paper uses a combination of existing advanced techniques to modify the historic electricity demand to incorporate the impact of heat pumps alone for long-term historic weather data using Great Britain as an example. The methods for generating time series were compared and extensively validated. This includes comparisons with measured data that have not been used previously for this purpose. The research reveals that for predicted 2050 heat pump penetration levels the monthly demand for electricity doubles in winter. This leads to an increase of approximately 30 TWh for each winter month and a 37 % increase in year-to-year variability of electricity demand due to weather. Peak electricity demand is very sensitive to the method of generating heat demand and the assumptions on hourly heat pump operating profiles, suggesting inaccuracies of 25 % in estimates of future peak demand. This work, rather than just assessing the impact of projected changes provides a reference case for policy makers to guide the decision process and planning for future scenarios. [ABSTRACT FROM AUTHOR]

Heating electrification powered by distributed renewable energy generation is considered among potential solutions towards mitigation of greenhouse gas emissions. Roadmaps propose a wide deployment of heat pumps and photovoltaics in the residential sector. Since current distribution grids are not designed to accommodate these loads, potential benefits of such policies might be compromised. However, in large-scale analyses, often grid constraints are neglected. On the other hand, grid impact of heat pumps and photovoltaics has been investigated without considering the influence of building characteristics. This paper aims to assess and quantify in a probabilistic way the impact of these technologies on the low-voltage distribution grid, as a function of building and district properties. The Monte Carlo approach is used to simulate an assortment of Belgian residential feeders, with varying size, cable type, heat pump and PV penetration rates, and buildings of different geometry and insulation quality. Modelica-based models simulate the dynamic behavior of both buildings and heating systems, as well as three-phase unbalanced loading of the network. Additionally, stochastic occupant behavior is taken into account. Analysis of neighborhood load profiles puts into perspective the importance of demand diversity in terms of building characteristics and load simultaneity, highlighting the crucial role of back-up electrical loads. It is shown that air-source heat pumps have a greater impact on the studied feeders than PV, in terms of loading and voltage magnitude. Furthermore, rural feeders are more prone to overloading and under-voltage problems than urban ones. For large rural feeders, cable overloading can be expected already from 30% heat pump penetration, depending on the cable, while voltage problems start usually at slightly higher percentages. Additionally, building characteristics show high correlations with the examined grid performance indicators, revealing promising potential for statistical modeling of the studied indicators. Further work will be directed to the assessment of meta-modeling techniques for this purpose. The presented models and methodology can easily incorporate other technologies or scenarios and could be used in support of policy making or network design. [ABSTRACT FROM AUTHOR]

This paper develops a scenario-based model to identify and provide an array of electricity demand in Africa, and to derive them from the African power system of development. A system-based approach is performed by applying the scenario methodology developed by Schwartz in the context of the energy-economic modeling platform ‘Long-range Energy Alternative Planning’. Four scenarios are investigated. The Business as Usual scenario (BAU) replicates the regional and national Master Plans. The renewable-promotion scenario increases the share of renewable energy in the electricity mix. The demand and supply side efficiency scenarios investigate the impact of energy efficiency measures on the power system. The results show an increase in electricity demand by 4% by 2040, supply shortages and high emissions of Greenhouse Gases. Contrary to expectations, the renewable energy scenario did not emerge as the best solution to a sustainable electrification of the region. The energy efficiency scenarios have allowed us to draw a sustainable pathway for electrification. [ABSTRACT FROM AUTHOR]

Challenges that smart grids aim to address include the increasing fraction of supply by renewable energy sources, as well as plain rise of demand, e.g., by increased electrification of transportation. Part of the solution to these challenges lies in exploiting the opportunity to steer residential electricity consumption (e.g., for flattening the peak load or balancing the supply and demand in presence of the renewable energy production). To optimally exploit this opportunity, it is crucial to have insights on how flexible the residential demand is. Load flexibility is characterized by the amount of power, time of availability and duration of deferrable consumption. Residential flexibility however, is challenging to exploit due to the variation in types of customer loads and differences in appliance usage habits from one household to the other. Existing analyses of individual customer flexibility behavior in terms of timing are often based on inferences from surveys or customer load patterns (e.g., as observed through smart meter data): there is a high level of uncertainty about customer habits in offering the flexibility. Even though some of these studies rely on real world data, only few of them have quantitative data on actual flexible appliance usage, and none of them characterizes individual user behavior. In this paper, we address this gap and contribute with: (1) a new quantitative specification of flexibility, (2) two systematic methodologies for modeling individual customer behavior, (3) evaluation of the proposed models in terms of how accurately the data they generate corresponds with real world customer behavior, and (4) a basic analysis of factors influencing the flexibility behavior based on statistical tests. Experimental results for (2)–(4) are based on a unique data set from a real-life field trial. [ABSTRACT FROM AUTHOR]

• Reviewing flexibility and robustness in local, integrated energy system models. • Flexibility is introduced by storage and sector coupling, DSM is rare. • Transport integration and the flexibility potential of EVs receive little attention. • Reporting metrics beyond cost and emissions provides additional decision-support. • Uncertainty assessments are overwhelmingly deterministic, mainly using scenarios. The electrification of heating, cooling, and transportation to reach decarbonization targets calls for a rapid expansion of renewable technologies. Due to their decentral and intermittent nature, these technologies require robust planning that considers non-technical constraints and flexibility options to be integrated effectively. Energy system models (ESMs) are frequently used to support decision-makers in this planning process. In this study, 116 case studies of local, integrated ESMs are systematically reviewed to identify best-practice approaches to model flexibility and address non-technical constraints. Within the sample, storage systems and sector coupling are the most common types of flexibility. Sector coupling with the transportation sector is rarely considered, specifically with electric vehicles even though they could be used for smart charging or vehicle-to-grid operation. Social aspects are generally either completely neglected or modeled exogenously. Lacking actor heterogeneity, which can lead to unstable results in optimization models, can be addressed through building-level information. A strong emphasis on cost is found and while emissions are also frequently reported, additional metrics such as imports or the share of renewable generation are nearly entirely absent. To guide future modeling, the paper concludes with a roadmap highlighting flexibility and robustness options that either represent low-hanging fruit or have a large impact on results. [ABSTRACT FROM AUTHOR]

• Assessment of demand and investments for rechargers to decarbonise the EU car fleet. • Distribution, usage ratio, unit costs and lifespan of rechargers influence investment needs. • The analysis points to 3.2–4 and 13.6–20.3 million chargers in 2030 and 2050 respectively. • Investments range between 8.7 and 16.2 bn€ in 2021–2030 and an extra 44.3–80.3 bn€ in 2031–2050. • The number of fast chargers influences investments the most. • Fast chargers may nonetheless help overcome barriers and promote the uptake of EVs. The electrification of passenger cars is a cornerstone of the EU strategy to decarbonising European transport by 2050. A key step for the uptake of electric vehicles (EVs) is to scale up the rollout of rechargers. The paper assesses which factors influence the investments in EU recharging infrastructure in a context of ambitious EV deployment projected under the "Fitfor55" policy package of the European Commission. We use an energy-demand method that includes assumptions on charging requirements of different EV types, the structure of the charging fleet, the survival probability and learning rates of the rechargers and conduct a sensitivity analysis to quantify their impact on investments. The projected electricity share in passenger cars is about 4.5% (65 TWh) and the required infrastructure ranges between 3.2 and 4 million chargers in 2030, while investments range between 8.7 and 16.2 bn € in 2021–2030. By mid-century, the electrification share of passenger cars increases to 62% (285 TWh) for which we estimate a total demand of 13.6 to 20.3 million chargers and an additional 44.3 to 80.3 bn € of investments in 2031–2050. A regression analysis depicts a strong correlation between fast chargers and investment expenditures, which is particularly relevant for 2021–2030, a period, when the deployment of a fast charging network will be needed to help reduce charging times and enable the roll-out of EVs. The energy output per charging point is the second most influential factor. Other factors, while important, are found not to significantly affect system costs. [ABSTRACT FROM AUTHOR]

This paper introduces the design of the CSIRO variant of the Global Trade and Environment model (GTEM-C). GTEM-C is a hybrid model that combines the top-down macroeconomic representation of a computable general equilibrium model with the bottom-up engineering details of energy production. The model features detailed accounting for global energy flows that are embedded in traded energy goods, and it offers a unified framework to analyse the energy-carbon-environment nexus. As an illustrative example, we present simulation results on global energy transformation under the Intergovernmental Panel on Climate Change’s representative carbon pathways 4.5 and 8.5. By testing the model’s sensitivity to the relevant parameter, we find that the pace of electrification will significantly contain the spiking of carbon prices because electricity can be produced from carbon-free or less carbon-intensive technologies. The decoupling of energy use and carbon footprint, due to the uptake of clean electricity technologies, such as nuclear, wind, solar, and carbon capture and storage, allows the world to maintain high level of energy consumption, which is essential to economic growth. [ABSTRACT FROM AUTHOR]

A recent worldwide uptake of electric vehicles (EVs) has led to an increasing interest for the EV charging situation. A proper understanding of the former is required to understand charging needs and to dimension the corresponding infrastructure. In the paper, we develop models that allow us to approximate the steady-state distribution of State-of-Charge (SoC) levels for EVs at the beginning of the day and infer its dependence regarding the daily relative range, r defined as the ratio of mean daily-driven distance to the maximum range. The framework combines: (i) a generic parametric model for decision to charge in terms of the SoC and r ; and (ii) a simulation model in which we utilize longitudinal log-data for a fleet of cars to track charging events and SoC over time. The model brings about several interesting use cases, of which two stand out. Firstly, it provides a simple parametric way of circumventing transient behaviour in SoC distributions when applied to simulation frameworks. Secondly, it offers a clear method to infer crucial information regarding EV fleets and the total energy storage potential. Such information is useful for vehicle-to-grid (V2G) applications in that it provides expected lower and upper bounds for the energy that can be stored and charged. The model is applied in a Danish context, and it is suggested that while a full electrification of the vehicle fleet could lead to significant stress on the power grid, it will at the same time hold a large potential for deploying V2G as an important mechanism to dampen the effect of temporal high-demand. The potential for V2G stems from a low battery utilization between charging events of approximately 40%, which in turn provides a large storage buffer that could be harnessed with little to no impact on EV utilization. Results also indicate that tapping into just half of the available buffer could serve the EV demand and also meet close to 50% of the household consumption. • Estimation of steady-state distribution for decision to charge and level of SoC for EVs. • Models have simple parametric form and can be readily applied to different scenarios. • Assessment of general battery utilization and the interval between charging events. • Probabilistic assessment of power demand for different scenarios. • Estimation of storage capacity available for vehicle-to-grid systems. [ABSTRACT FROM AUTHOR]

• A bottom-up estimation of CO 2 mitigation potential of combined options for a case-study refinery. • A dynamic CO 2 mitigation potential over time is based on measure interactions for each pathway. • Co-processing account for 60% of the portfolio followed by CCS with 23%, GE with 7%, and H 2 with 6% • The oil and gas industry could reach carbon neutral operation at factory gate. • The methodological approach brings insights to define optimal transition routes of the industry. The oil and gas industry is responsible for 6% of total global CO 2 emissions, from exploration to downstream petrochemical production and account for another 50% when including the use of its products. Thus, this industry has a significant role in realising the target of net "zero" CO 2 emissions by 2070, essential to limit global warming to 1.8 °C [2], as introduced under the Paris agreement. Currently, the interactions of an extensive set of individual and combined CO 2 mitigation measures along the value chain and over time are poorly assessed. This paper aims to assess a bottom-up CO 2 mitigation potential for a complex refinery, including portfolios of combined mitigation options, considering synergies, overlap, and interactions over time for more realistic insight into the costs and constraints of the mitigation portfolio. A total of 40 measures were identified, covering a wide range of technologies such as energy efficiency measures (EEM), carbon capture and storage (CCS), bio-oil co-processing, blue and green hydrogen (BH 2 , GH 2), green electricity import, and electrification of refining processes linked to the transition of the Colombian energy systems. Five deployment pathways were assessed to achieve different specific targets: 1-base case scenario, 2-less effort, 3-maximum CO 2 avoidance, 4-INDC, and 5-measures below 200 €/t CO 2. Two scenarios (3 and 5) gave the highest GHG emission reduction potentials of 106% and 98% of refining process emissions, respectively. Although significant, it represent only around 13% of the life-cycle emissions when including upstream and final-use emissions of the produced fuels. Bio-oil co-processing options account for around 60% of the mitigation options portfolio, followed by CCS (23%), green electricity (7%) and green H 2 (6%). The devised methodological approach in this study can also be applied to assess other energy-intensive industrial complexes and shed light on the bias for estimating CO 2 mitigation potentials, especially when combining different mitigation options. This is turn is vital to define optimal transition pathways of industrial complexes. [ABSTRACT FROM AUTHOR]

Abstract: Renewable energy is one of the possible solutions when addressing climate change. Today, large-scale renewable energy integration needs to include the experience to balance the discrepancy between electricity demand and supply. The electrification of transportation may have the potential to deal with this imbalance and to reduce its high dependence on oil production. For this reason, it is interesting to analyse the extent to which transport electrification can further the renewable energy integration. This paper quantifies this issue in Inner Mongolia, where the share of wind power in the electricity supply was 6.5% in 2009 and which has the plan to develop large-scale wind power. The results show that electric vehicles (EVs) have the ability to balance the electricity demand and supply and to further the wind power integration. In the best case, the energy system with EV can increase wind power integration by 8%. The application of EVs benefits from saving both energy system cost and fuel cost. However, the negative consequences of decreasing energy system efficiency and increasing the CO2 emission should be noted when applying the hydrogen fuel cell vehicle (HFCV). The results also indicate that developing renewable energy is crucial for transportation electrification. [Copyright &y& Elsevier]

Microgrids are a key technology in granting universal access to affordable energy. To compare the efficacy of these systems, a quantitative approach is required to evaluate implemented solutions and energy sharing benefits from interconnected units. In this paper, real data from an off-grid microgrid in the Philippines were analyzed and used for simulating different sharing scenarios. The microgrid consisted of 62 solar home system units arranged in 10 clusters, each unit paying a daily fee for a given allowance of energy usage. An agent-based model was built from the parameters and topology of this microgrid, closely approximating recorded energy flows and enabling the simulation of different sharing schemes. Key performance indicators were then introduced to evaluate the actual sharing performances, against scenarios with no interconnections (standalone) and with all units connected (ideal). As the microgrid panels and batteries were over-dimensioned to sustain streaks of bad weather days, sharing seemed to provide moderate benefits. A further analysis on the unit types showed that peer-to-peer exchange promoted economic development, since productive units, which consumed more energy, could exploit energy excesses that would otherwise be curtailed. To improve microgrid performance, two solutions were proposed: the diversification of energy sources; the addition of flexible load. The latter was validated by simulations, showing that the setup analyzed could sustain a supplemental flexible load of about 250 W, that could power communal appliances or agro-processing machines, increasing the grid utility by 10%. These results confirmed that additional grid heterogeneity enhances the benefits of energy sharing. • A case study of a 62 household off-grid microgrid in the Philippines was analyzed. • Different microgrid sharing scenarios were compared via agent-based modeling. • Necessary grid over-dimensioning led to apparently modest benefits from sharing. • Heterogeneity, from flexible load and diverse energy sources, potentiates sharing. • Sharing sustained local economy, yet its potential should be further exploited. [ABSTRACT FROM AUTHOR]

This paper explores the impacts of decarbonization of heat on demand and subsequently on the generation capacity required to secure against system adequacy standards. Gas demand is explored as a proxy variable for modelling the electrification of heating demand in existing housing stock, with a focus on impacts on timescales of capacity markets (up to four years ahead). The work considers the systemic changes that electrification of heating could introduce, including biases that could be introduced if legacy modelling approaches continue to prevail. Covariates from gas and electrical regression models are combined to form a novel, time-collapsed system model, with demand-weather sensitivities determined using lasso-regularized linear regression. It is shown, using a Great Britain-based case study with one million domestic heat pump installations per year, that the sensitivity of electrical system demand to temperature (and subsequently sensitivities to cold/warm winter seasons) could increase by 50% following four years of heat demand electrification. A central estimate of 1.75 kW additional peak demand per heat pump is estimated, with variability across three published heat demand profiles leading to a range of more than 14 GW in the most extreme cases. It is shown that the legacy approach of scaling historic demand, as compared to the explicit modelling of heat, could lead to over-procurement of 0.79 GW due to bias in estimates of additional capacity to secure. Failure to address this issue could lead to £100m overspend on capacity over ten years. • System adequacy considered in context of transition to electrified heat. • 30-year net demand hindcast based on Lasso regression and weather reanalysis data. • GB temperature-demand sensitivity could increase by 54% in just 4 years. • Electric heat demand growth uncertainty doubles variability in capacity to secure. • Legacy modelling approach could lead to £100m over-procurement over 10 years. [ABSTRACT FROM AUTHOR]

• A MILP-based formulation for optimal planning of electrification systems. • Different demand-side strategies can be incorporated. • A case study with different layouts and demand-side management strategies. • The cost can be reduced up to 41.5% The emergence of novel demand-side management strategies is provoking that residential consumers cannot be longer categorized as inflexible loads. In that sense, new paradigms such as flexible demand and vehicle-to-home capabilities have appeared on pursuing a more efficient management of the different domestic assets such as smart appliances, small renewable generators and storage facilities. Consideration of such kind of management strategies plays a vital role on designing stages of electrifications systems for isolated homes. In this regard, a proper evaluation of possible demand-side capabilities enables a more accurate evaluation of the electrification project. This paper tackles this issue by developing a Mixed Integer Linear Programming formulation for optimal planning of electrification systems for off-grid dwellings. The developed framework allows to incorporate and analyse advanced demand-side strategies such as deferrable loads and vehicle-to-home processes by evaluating different project costs over various time horizons. The proposed approach is applied to a benchmark off-grid residential case study and various results are provided and analysed. As the most relevant result, it is worth remarking that total project cost can be reduced by ~21.2% and ~25.73% by considering flexible demand and vehicle-to-home capabilities, respectively. Nevertheless, the highest monetary savings were achieved when both strategies are jointly adopted, reducing the total project cost up to ~41.5%. The reported results demonstrate the importance of considering the different demand-side strategies on planning stages of electrification systems for off-grid dwellings. [ABSTRACT FROM AUTHOR]

• Hydrogen-solar-storage integrated microgrid for airport electrification. • Airport energy dispatch strategy incorporated with flight schedules. • Five energy supply scenarios with techno-economic lifecycle assessment. • Sensitivity analysis of key parameters on future energy system design. • Airport microgrid presents economic and environmental benefits. Can aviation really become less polluting? The electrification of airport energy system as a micro-grid is a promising solution to achieve zero emission airport operation, however such electrification approach presents the engineering challenge of integrating new energy resources, such as hydrogen supply and solar energy as attractive options to decarbonize the present system. This paper explores the techno-economic benefits of integrating hydrogen supply, electric auxiliary power unit (APU) of aircraft, electric vehicles, photovoltaic energy (PV), and battery storage system into electrified airport energy system. The hydrogen fuel cell generation provides great flexibility to supply aircraft at remote stands, and reduces the carbon emissions caused by traditional fuel-powered APU. A mixed integer linear programming optimization method based on life cycle theory is developed for capacity sizing of hydrogen energy system, PV and battery storage, with optimization objective of minimizing the total economic costs as well as considering environmental benefits of the proposed airport microgrid system. Case studies are conducted by five different energy integration scenarios with techno-economic and environmental assessments to quantify the benefits of integrating hydrogen and renewable energy into airport. Compared with the benchmark scenarios, the integration of hydrogen energy system reduced the total annual costs and carbon emissions of airport energy system by 41.6% and 67.29%, respectively. Finally, sensitivity analysis of key system parameters such as solar irradiance, grid emission factor, elctricity price, carbon tax, unit investment cost of hydrogen energy system have been investigated to inform the design of hydrogen-solar-storage integrated energy system for future airport electrification. [ABSTRACT FROM AUTHOR]

• Electricity and electrification is a viable solution for the energy transition. • Electricity triangle assures a consistent framework for the energy transition. • Multifaceted benefits: energetic, environmental and socio-economic. • Conceptualization of a set of metrics for impact assessment. • A real case study (Italy) considering its evolution up to 2050. Greenhouse gases along with pollution generated by the present energy paradigm widely based on fossil fuels are providing large impacts in terms of climate change and the health of human and biological systems at large. A shift in the energy paradigm, from fossil fuels to renewable energy, is urgently needed for nature and society. This is what we refer to as the energy transition. An old commodity – electricity – can play a new key role in the energy transition. That can happen through what we call the electricity triangle involving electricity generation from Renewable Energy Sources, exploitation of electricity as the main energy vector, and electrification of the final energy uses in all sectors. The possible deployment of the electricity triangle must be carefully assessed in all its possible implications, from technological, economic, societal and environmental perspectives. In this paper, we conceptualize the electricity triangle as a viable approach to the energy transition, and we propose a set of holistic metrics to assess its possible impacts. We apply the electricity triangle framework to the case of Italy based on sectorial studies on RES generation and electrification in building, industry and transport sectors. Our results indicate that Italy in 2050 has the potential to achieve 85.6% penetration of RES in its electricity supply, while 41%, 53% and 42% of the energy consumptions in transport, residential, and industry sectors will be electrified. Ultimately, this would lead to a 68% reduction in CO 2 emissions compared to current levels. [ABSTRACT FROM AUTHOR]