[en] Highlights: • Real-world performance comparison between compressed natural gas and diesel buses. • Higher THC emissions for the compressed natural gas bus compared to the diesel bus. • Increase of 6–55% of fuel consumption with variations in the operating conditions. • Less NO_x emissions for the natural gas bus at high congestion and road grade levels. • Vehicle specific power predicts CO₂ and NO_x emissions with good accuracy. This study investigated the effects of passenger load, road grade, and congestion level on the fuel consumption and emissions from a Euro VI compressed natural gas (CNG) urban bus and a Euro V diesel urban bus. Testing was performed under real-traffic conditions in Madrid, Spain, using a portable emission measurement system (PEMS). The PEMS data also were combined with the vehicle specific power (VSP) methodology to analyse the differences between the performance of the two types of buses and develop an energy-based emission model. Between the empty and 4000 kg passenger load cases, the fuel consumption and CO₂ emissions for the diesel bus showed a significant increase by approximately 25%. With an increase in the road grade, and congestion level, the fuel consumption and CO₂ emissions of both types of buses increased, by 6–55%. Unlike in the case of the diesel bus, the NO_x emissions of the CNG bus decreased by 40–50% as the level of road grade and congestion increased. At intervals of VSP ≥ 2 kW/t, NO_x emission rates for the CNG bus were approximately 60% lower than those of the diesel bus. Finally, the proposed VSP-based model estimated the fuel consumption and the CO₂ and NO_x emission factors with relative total errors of less than 13%.

[en] Highlights: • PV + storage can reduce CO₂ emissions while lowering cost of abatement. • Storage optimal power rating seems to be lower than 25% of PV capacity. • Storage with low energy-to-power ratio is cost effective with lower PV shares. • PV + storage systems would achieve breakeven point with expected costs projections. • Optimal configurations can reduce carbon emissions in the DEC/DEP system up to ~57%. This study explores the performance of the Duke Energy Carolinas/Progress (DEC/DEP) electric power system under one hundred forty-one configurations combining different capacities of utility-scale photovoltaics (PV) and battery energy storage (lithium-ion batteries or BES). The different configurations include PV installations capable of providing 5–25% of the systems energy and batteries with varying duration (energy-to-power ratio) of 2, 4, and 6 h. A production cost model comprised of a day-ahead unit commitment and a real-time economic dispatch simulates the optimal operation of all the generation resources necessary to supply hourly demand and reserve requirements during the year 2016. The model represents in detail the generation fleet of the system, including 221 nuclear, natural gas, coal and hydro power generators with a combined installed capacity of 37.8 GW. Results indicate that: 1) adding BES to a power system that includes PV further reduces carbon dioxide emissions while also lowering the cost of carbon abatement. 2) The optimal power rating of a BES system that supports PV seems to be lower than 25% of the capacity of the PV. 3) BES of short duration (2-h) are more cost-effective (i.e., result in a lower cost of abatement) when the level of PV penetration is low (lower than ~12.5%), while BES of longer duration (6-h) are more cost-effective when there are larger shares of PV. 4) The installation of optimal configurations of PV + BES to reduce carbon emissions in the DEC/DEP system by ~14–57% would increase the levelized cost of electricity (LCOE) ~8–65%. 5) If projections of declining costs for the next decade materialize, the installation of up to 15 GW of PV + 1.88 GW / 3.76 GWh of BES would reduce the LCOE while achieving up to 33% reduction in carbon emissions.

[en] Highlights: • A novel method proposed for flexible load forecasting represented by EVs • Flexible load data from similar EV charging stations are learned jointly • The existing multi-task learning algorithm is improved for time-series data • Clear discussions and guidelines for implementing of the proposed method are provided Forecasting the available flexible load provided by electric vehicles would enable electric utilities to make informed decision in utilizing these loads for enhancing the operational efficiency of distribution systems. To overcome the lack of historical loads data at newly-installed EV charging stations, this paper proposes a clustered multi-node learning with Gaussian Process (CMNL-GP) method to fuse data from multiple charging stations and to learn them simultaneously. The proposed method improves the forecasting accuracy in each node by transferring meaningful information among multiple nodes. The proposed method also performs a clustering algorithm within its objective function to obtain within-cluster similarity, since all the nodes may not be related equally, and the nodes within a cluster may have a stronger correlation. To characterize the clustered structures and to transfer the shared information among multiple nodes, different regularization terms are imposed in the objective function of the proposed method. The proposed clustered multi-node learning also utilizes the Gaussian Process for statistical attributes of the residual stochastic process, which refers to the information that may not be shared among multiple nodes and can be node-specific. The proposed method is validated by real-world EV charging stations data in State of Utah, USA, to demonstrate the effectiveness of the proposed algorithm.

[en] Highlights: • We propose an engine-speed-dependent model of a hybrid electric race car. • We jointly optimize the energy management and gear strategies for minimum lap time. • Our iterative algorithm combines convex optimization and dynamic programming. • An entire lap can be optimized with a computation time of around 90 s. • Deviating by 4% from best upshift threshold can increase lap time by more than 100 ms. Modern Formula 1 race cars are hybrid electric vehicles equipped with an internal combustion engine and an electric energy recovery system. In order to achieve the fastest possible lap time, the components’ operation must be carefully optimized, and the energy management must account for the impact of the gearshift strategy on the overall performance. This paper presents an algorithm to calculate the time-optimal energy management and gearshift strategies for the Formula 1 race car. First, we leverage a convex modeling approach to formulate a mathematical description of the powertrain including the gearbox, preserving convexity for a given engine speed trajectory. Second, we devise a computationally efficient algorithm to compute the energy management and gearshift strategies for minimum lap time, under consideration of given fuel and battery consumption targets. In particular, we combine convex optimization, dynamic programming and Pontryagin’s minimum principle in an iterative scheme to solve the arising mixed-integer optimization problem. We showcase our algorithm with a case study for the Bahrain racetrack, underlining the interactions between energy management and gear selection. Finally, we use our approach as a benchmark to evaluate the sub-optimality of a heuristic gearshift rule. Our results show that using an optimized engine speed threshold for upshifts can yield close-to-optimal results. However, already deviations smaller than 4% from the best possible threshold can increase lap time by more than 100 ms, highlighting the importance of jointly optimizing energy management and gearshift strategies.

[en] Highlights: • Few studies evaluate energy flexibility from an integrated energy systems perspective. • A novel unified framework is proposed for capturing the DR potential of thermal and electrical systems. • A series of novel indicators are introduced to acquire daily energy flexibility mappings. • Flexibility maps are used to quantify energy flexibility concisely and consistently. • Weather, occupant schedules and use of appliances shape the flexibility potential of each system. To date, the energy flexibility assessment of multicomponent electrical and thermal systems in residential buildings is hindered by the lack of adequate indicators due to the different interpretations, properties, and requirements that characterise an energy flexible building. This paper addresses this knowledge gap by presenting a fundamental energy flexibility quantification framework applicable to various energy systems commonly found in residential buildings (i.e., heat pumps, renewables, thermal and electrical storage systems). Using this framework, the interactions between these systems are analysed, as well as assessing the net energy cost of providing flexibility arising from demand response actions where onsite electricity production is present. A calibrated white-box model of a residential building developed using EnergyPlus (including inter alia a ground source heat pump, a battery storage system, and an electric vehicle) is utilised. To acquire daily energy flexibility mappings, hourly independent, and consecutive demand response actions are imposed for each energy system, using the proposed indicators. The obtained flexibility maps give insights into both the energy volumes associated with demand response actions and qualitative characteristics of the modulated electricity consumption curves. The flexibility potential of each studied energy system is determined by weather and occupant thermal comfort preferences as well as the use of appliances, lighting, etc. Finally, simulations show that zone and water tank thermostat modulations can be suitably combined to shift rebound occurrences away from peak demand periods. These insights can be used by electricity aggregators to evaluate a portfolio of buildings or optimally harness the flexibility of each energy system to shift peak demand consumption to off-peak periods or periods of excess onsite electricity generation.

[en] Highlights: • A two-stage method is applied for solving the DGs and capacitors placement problem. • Two VSFs based on VD and VSI are used to reduce the search space. • The CBA is used to find the optimal locations and sizes of DGs and capacitors. • The CBA is used to optimize single and multi-objective functions. • The proposed CBA is applied to small and large-scale systems to show its robustness. This paper proposes a two-stage procedure to enhance the distribution system performance by determining the optimal sizes and locations of distributed generations (DGs) and capacitors considering single and multi-objective functions. In stage-1, two voltage sensitivity factors (VSFs) based on voltage deviation (VD), and voltage stability index (VSI) are proposed to reduce the search space (SS) by selecting the candidate buses for DGs and capacitors placement. In stage-2, the chaotic bat algorithm (CBA) is applied to find the optimal sizes and locations of DGs and capacitors, according to different objective functions (OFs) and system constraints. The considered OFs are real power loss reduction, total VD minimization, and total VSI maximization. The multi-OF, which aims to optimize these objectives simultaneously, is also considered. The load flow calculations are carried out using the backward/forward sweep (BFS) algorithm. The proposed methodology is evaluated and tested on small and large-scale standard test systems, namely 34-bus and 118-bus radial distribution systems, through different case studies. The numerical results obtained by the proposed procedure are compared with other methods in the literature to show the superiority of the proposed procedure for reducing the total real power loss and improving the voltage profile, especially at increasing the power system sizing.

[en] Highlights: • A three-step methodology to develop a HE for harnessing GW heat is put forward. • A PCM-HE with finned-corrugated pipes can be developed for versatile applications. • A technique simulating a unit volume determines performance of the holistic PCM-HE. • A developed PCM-HE saves 20–40% heat in residential buildings, as per a case study. • This novel methodology and technique can be applied in numerous energy applications. Waste greywater (GW) from non-industrial buildings has considerable exergy that must be exploited for a sustainable future. Scavenging this low-grade heat from GW into cold water (CW) before storage in a phase change material (PCM) is a novel approach to decouple demand and supply along with integrating storage and transfer in a single heat exchanger (HE). A methodology to optimally select an appropriate PCM along with the heat transfer enhancement procedures both internal and external to the flow pipes is presented. This procedure can be extended to reduce the hot water heating demand for applications in both residential and larger commercial buildings. Furthermore, a design technique to numerically assess the performance of a full-scale HE based on empirical formulations of a unit-volume are also put forward. As a case study, the performance of a counter-flow PCM-HE with vertically cascaded GW and CW finned-corrugated pipes is assessed for heat recovery in household appliances of a residential building. Such HE with 9 m piping length can increment the incoming CW temperature by 9.5 K with complete phase change of 30 kg of PCM in 900 s. A transfer of about 5,100 kJ of heat for PCM melting with GW outflow and freezing with CW inflow occurs within this time frame. Furthermore, to fully exploit the temperature differences between the fluids a three-cascaded PCM arrangement enhances the CW outlet temperature by 64%. The installation of this PCM-HE in a four-member UK household, can save 4,687 kWh of energy annually with a payback time of 4.44 years.

[en] Highlights: • The effect of China’s feed-in tariff (FIT) on installed PV capacity is estimated. • China’s zonal FIT offers a quasi-natural experiment to analyze its policy effect. • Subsidies have a much higher effect on installed PV capacity than was believed. • Without the FIT policy, China’s PV deployment could virtually disappear. • The abatement cost of China’s FIT is higher than the carbon-trading market. Governments across countries often offer dynamic subsidies to clean technologies that decrease over time. However, the impact of government subsidies on technology deployment is difficult to gauge due to many confounding factors and the selection bias problem caused by the phenomenon of rushing for subsidies. This study takes China’s solar photovoltaic (PV) as an example, and uses a difference-in-difference framework that leverages China’s zonal feed-in tariff (FIT) policy design and its multiple changes over time. The parallel rushing for subsidies by two neighboring FIT zones provides a unique opportunity to identify the causal effect of FIT policy on newly installed PV capacity. Results show that an increase of 0.1 yuan/kWh (~$0.014/kWh) in PV subsidies adds about 18 GW/year of installed capacity to the national PV market, right in the middle of previous estimates in the literature. From a different perspective, if China did not have any PV subsidies, the PV deployment market would virtually disappear. The cost of carbon mitigation through PV feed-in tariffs is estimated at around 120 yuan (~$17) per ton of CO₂. Our estimate of the impact of FIT on PV capacity is useful for the government to design policies that help the PV industry transit to a subsidy-free era.

[en] Highlights: • Hybrid renewable energy with battery and hydrogen vehicle systems are developed. • Two energy management strategies with different energy storage priority are compared. • Multi-objective optimizations on supply, grid and system cost are conducted. • Four decision-making strategies are studied for stakeholders with different concerns. • Techno-economic-environmental feasibility is analyzed applied in high-rise buildings. This study presents a robust energy planning approach for hybrid photovoltaic and wind energy systems with battery and hydrogen vehicle storage technologies in a typical high-rise residential building considering different vehicle-to-building schedules. Multiple design criteria including the supply performance, grid integration and lifetime net present value are adopted to size the hybrid system and select the optimal energy management strategy. Four decision-making strategies are further applied to search the final optimum solution for major stakeholders with different preferences. The study result indicates that the energy management strategy with battery storage prior to hydrogen storage is suitable for hybrid systems with large photovoltaic, wind and battery installation capacities to achieve the optimum supply-grid integration-economy performance. The energy management strategy with hydrogen storage prior to battery storage has a wider applicability, and this strategy should be selected when focusing on the supply-grid integration or supply-economy performance. The annual average self-consumption ratio, load cover ratio and hydrogen system efficiency are about 84.79%, 76.11% and 77.06% respectively in the end-user priority case. The annual absolute net grid exchange is about 4.55 MWh in the transmission system operator priority case. The lifetime net present value of the investor priority case is about 3.64 million US$, 29.88% less than the equivalent priority case. Final optimum solutions show positive environmental impacts with negative annual carbon emissions. Such a techno-economic-environmental feasibility analysis of the hybrid system provides major stakeholders with valuable energy planning references to promote renewable applications in urban areas.

[en] Highlights: • The multi-party stochastic energy scheduling in IIES is studied. • A decentralized decision support system with stochastic utility model is built for IUs. • A stochastic game is designed to formulate the interaction among uncertain IUs. • A solution algorithm with Markov decision process and iterative method is designed. • A comparison between multiple stochastic and deterministic scenarios is provided. Multi-dimensional stochastic factors challenge the interactive energy scheduling of the industrial integrated energy system (IIES). Previous research focuses on either deterministic energy scheduling or individual stochastic scheduling while neglecting complicated interactions among uncertain parties, which brings the research gaps about stochastic multi-party’s interaction. In this regard, a multi-party stochastic energy scheduling approach in IIES is proposed based on the stochastic game. A decentralized decision support system is considered, and a stochastic utility model is designed for decentralized IUs with multi-dimensional stochastic factors from photovoltaic (PV) production and IIES parameters, enabling them to participate in the multi-energy scheduling with their own strategies. A stochastic game model is developed considering the thermoelectric coupling and the IUs’ interaction. The co-decision mechanism, recognizing different transfer times of electrical and thermal energy, is built based on the state transition within the game. Moreover, a distributed solution algorithm that includes the Markov decision process and iterative method is designed to address the problem of the “curse of dimensionality” arising from multiple stochastic factors. Finally, case studies with realistic data from an industrial park in Guangdong Province, China, are designed to show the effectiveness of the proposed approach, which enhances IUs’ profits by 9.4% and fits flexible load strategies and price strategies. The decentralized system can also reduce the computation time by 70.1% compared to the centralized system. Through analyzing different number of scenarios and intervals for PV generation, electrical and thermal load, the conclusion has obtained that increase the number of scenarios has a negative effect on IUs’ decision, but increase the number of load intervals contributes to more specific results and higher utility.