[en] Highlights: • The impacts of photovoltaic (PV) policies on distribution networks are assessed. • PV and batteries are optimally sized via mixed integer linear programming. • Self-consumption policy enables larger PV penetration than the net-metering policy. • Sizing/managing batteries from customers’ perspective cannot solve the PV issues. • Distribution network operators leading the uptake of batteries mitigate PV impacts. The transition towards low-carbon energy systems requires increasing the contribution of residential Photovoltaic (PV) in the energy consumption needs (i.e., PV self-consumption). For this purpose, the adoption of PV self-consumption policies as alternatives to the current net-metering policy may support harnessing batteries to improve PV self-consumption. However, the technical impacts of PV policies on distribution networks have to be adequately assessed and mitigated. To do so, a two-stage planning framework is proposed. The first stage is an optimization approach that determines the best sizes of PV and batteries based on the adopted PV policy. The second stage assesses the impacts of the resulting sizes on distribution networks using Monte-Carlo simulations to cope with uncertainties in demand and generation. The framework is applied on real medium and low voltage distribution networks from the south of Jordan. For the net-metering, the results show that the uptake of residential PV penetration above 40% will result in voltage issues. It is also found that the adoption of batteries for the benefits of customers (i.e., reduce electricity bills) will not mitigate the PV impacts for PV penetration above 60%. Further, the results demonstrate the important role of distribution network operators to manage the uptake of batteries for the benefits of customers and distribution networks. Network operators can support customers to adopt larger sizes of batteries to achieve the desired PV self-consumption in return of controlling the batteries to solve network issues. This facilitates the uptake of 100% PV penetration and improves PV self-consumption to 50%.

[en] Highlights: • Direct and indirect nexus accounting framework is proposed for urban agglomeration. • The multi-regional input-output analysis and structural path analysis are combined to investigate the energy-water nexus paths in economic system. • The top 50 ranking paths of water-related energy induced by rural household, urban household, and government consumption are investigated. • Sectoral flows within Beijing-Tianjin-Hebei region account for 87% of the total while cross-regional only accounts for 13%. Energy and water are closely intertwined within economic sectors in urban systems. The direct and indirect linkages between them in economic systems, termed the energy–water nexus, have been widely studied from production and consumption perspectives. However, the step-by-step linkages from initial production to final consumption remain unclear, particularly the indirect linkages. In this paper, we develop a multiregional energy–water nexus path model based on multiregional input–output analysis and structural path analysis. The results show that the top 50 ranking paths of water-related energy induced by rural household, urban household, and government consumption account for 84%, 82%, and 90%, respectively, of total flows, whereas the corresponding figures for energy-related water are 57%, 58% and 76%, respectively. The proportion of the top 50 paths within total paths is much lower for energy-related water than is the case for water-related energy. Sectoral flows within Beijing–Tianjin–Hebei account for 87% of the total, whereas cross-regional flows only account for 13%. By comparing the energy–water nexus from production, consumption, and supply chain perspectives, we aim to identify the critical, yet often overlooked, energy–water nexus paths and transmission sectors to enhance coordinated energy–water management in urban systems.

[en] Highlights: • A framework is proposed for restoration optimization of coupled energy systems. • Skeleton-network reconfiguration is developed to obtain critical components to repair. • Restoration sequence of critical components is optimized to maximize resilience. • Resilience is enhanced by the coordinated restoration of power and gas systems. • Impacts of resources, crews, and repair modes on resilience are illustrated. The ever-increasing interdependencies of gas and power transmission networks have made it possible for disruptions in one subnetwork to permeate to the connected ones and trigger widespread blackouts. One of the most critical issues is to restore the interdependent gas and power networks (IGPNs) to normal operation as soon as possible after complete blackouts. The interdependencies of gas and power networks, together with different operating characteristics bring about additional complexities to the formulation of restoration schemes. As a necessary and challenging task, restoration strategy optimization for IGPNs is tackled in this paper. Firstly, system functionality metrics are defined to characterize the real-time performance level, then resilience metrics are developed by capturing recovery features of system functionality. Secondly, the restoration sequence optimization model is developed to determine the restoration sequence of failed components to maximize resilience, in which restoration characteristics in terms of repair modes, repair time, and recovery costs are considered. To relieve the computational burden, the skeleton-network reconfiguration model is proposed to determine critical components to restore within limited resources. Moreover, linearization methods are utilized to transform models into mixed-integer linear programming problems. The results in test cases not only illustrate the effectiveness of the proposed approach to enhance the system resilience, but also illustrate the impacts of resources, crews, and repair modes on resilience, which help system operators to constitute restoration strategies quickly and develop resilience enhancement measures.

[en] Highlights: • The optimized dispatch of city-scale integrated energy system with various functional zones is presented. • An energy hub model with gas gate station and combined heating and power device is established. • Application prospect of pipeline storage and gas storage tanks is evaluated. • Effects of gas-electricity price ratio coefficient on heating methods and energy consumption are analyzed. Integrated Energy System (IES) has been proved to be able to effectively improve energy utilization and economic benefits. In the day-ahead dispatch of city-scale IES, electricity-gas interconnection is widely used to realize energy complementarity among regions, where the pressure regulation and electricity-heat-gas coupling in the gas gate station powered by Combined Heating and Power (CHP) device are usually excluded. Therefore, with the gas gate station centered, CHP fired energy hub linearization model, the optimized dispatching model of city-scale IES considering the flexibilities of city gas gate station and line packing is established in this paper where an Integrated Electricity and Gas System (IEGS) is running among regional Combined Cooling, Heating and Power (CCHP) systems, applying power and natural gas coupled energy flow algorithm. Results of case studies verify the potential of pipeline storage compared with gas storage tanks up to an 8.391% reduction on overall operation cost, a 14.366% improvement on renewable energy utilization and charging/discharging cycles reaching only 0.197 times of those of tanks, especially in areas with rich renewable energy. Besides, regulated by CHP fired energy hub which is worth constructing for its economic benefits and alleviation of peak power supply pressure, the average pipeline pressure proportional to stored gas volume increases by 5.885%, enhancing the flexibility of line packing. Moreover, this paper demonstrates that heating economy of gas boilers and electric boilers in comparison to gas turbines rises in turn with the increase of Gas-Electricity Price Ratio (GEPR). Lastly, Quasi Elastic Coefficient (QEC) is defined to describe multi-energy consumption sensitivity to Time-Of-Use (TOU) energy tariff, which is helpful in energy pricing and equipment scheduling.

[en] Highlights: • An operation interval is proposed considering economy, flexibility and reliability. • The interval can mitigate emergencies in the hydro-photovoltaic system operation. • The interval reduced 4.32% loss of annual power generation in a plague emergency. The joint operation of multiple renewable energies has become a promising approach to promote the penetration of renewables into power systems, where multiple uncertainties are unavoidably involved. Uncertainties caused by emergencies can significantly affect power system operation. However, the traditional single operation trajectory, which only considers pre-scribed uncertainties, is not enough to cope with emergencies. This study aims to propose a robust operation interval to deal with uncertainties caused by emergencies. First, a multi-objective model is developed to derive the robust operation interval considering the economy (power generation), flexibility (robust operation interval width) as well as reliability (portion of feasible solutions). Second, a two-layered nested framework is used by coupling non-dominated sorting genetic algorithm II and discrete differential dynamic programming in a hierarchical structure to improve calculation efficiency. Finally, the stochastic simulations are used to validate the effectiveness of the robust operation interval. Results for a case study using China’s Longyangxia hydro–photovoltaic power plant indicated that the proposed method could derive the robust operation interval effectively. The multi-objective optimization revealed that the interval’s economy was in conflict with both the flexibility and reliability. The robust operation interval reduced 4.32% loss of annual power generation by adjusting 0.14% annual reservoir discharge compared with the pre-scribed trajectory in a plague emergency, and decreased 8.99% loss of quarterly power generation by adjusting 1.95% quarterly discharge in an earthquake emergency. The proposed robust operation interval effectively deals with uncertainties caused by emergencies.

[en] Highlights: • A zero-energy community with battery and hydrogen vehicle storage is developed. • A time-of-use grid penalty cost model is proposed for grid flexibility and economy. • Grid penalty cost is reduced by 145.36% in zero-energy community with battery storage. • Carbon emissions are declined by 71.23% in zero-energy community with battery storage. • Net present value is lowered in zero-energy campus and residence without batteries. This study presents hybrid renewable energy systems integrated with stationary battery and mobile hydrogen vehicle storage for a zero-energy community consisting of campus, office and residential buildings based on practical energy use data and simulations. A time-of-use grid penalty cost model evaluating grid import and export during on-peak and off-peak periods is proposed to achieve the power grid flexibility and economy. Multi-objective optimizations are conducted to size zero-energy buildings and the community considering the renewable energy self-consumption, on-site load coverage and grid penalty cost in the coupled platform of TRNSYS and jEplus+EA. The study results indicate that battery storage improves the renewable energy self-consumption, load coverage, hydrogen system efficiency and grid integration of the zero-energy community. Grid penalty cost reductions of 145.36% − 158.92% and 135.05% − 164.41% are achieved in zero-energy scenarios with and without battery storage compared with baseline scenarios without renewable energy. The lifetime net present value of four zero-energy scenarios with battery storage is increased by 22.39% − 96.17% compared with baseline scenarios, while it is reduced by 6.45% of US$ 7.62M and 1.90% of US$ 2.16M in zero-energy campus and residential buildings without battery storage. Substantial environmental benefits are also achieved in zero-energy scenarios with and without battery storage for reducing carbon emissions by 71.23% − 90.93% and 67.57% − 91.36%, respectively. Such a comprehensive techno-economic-environmental feasibility study can offer significant guidance for relative stakeholders to develop renewable energy applications for zero-energy buildings and communities in urban areas.

[en] Highlights: • Impact of natural disasters on the electricity grid is studied. • Grid resilience and reliability metrics and enhancement strategies are described. • Grid resilience and reliability of the United States are assessed. • The United States map is categorized based on grid resilience and reliability. • Resilience risk factor and grid infrastructure density are newly coined terms. The rise in power shutdowns triggered by severe weather due to deteriorating climate change has expedited the research in enhancing community resilience. Several researchers and policy-makers have contributed to the characterization and parameterization of energy resilience and reliability in particular, which requires accumulated and coordinated studies to underline the outcomes and reflect those in future works on grid resilience and reliability enhancement. The concept of both the resilience and reliability of the grid systems should be defined and distinguished so that the systems can be clearly comprehended, assessed, and operated to maintain flawless operation and ensure environmental sustainability. This paper meets the mentioned objectives to discuss grid resilience and reliability, their quantification metrics, and their enhancement techniques in detail. The paper also categorizes the United States into four tiers based on grid reliability and grid resilience using Monte Carlo Simulations and the discussed metrics. Two novel terminologies named resilience risk factor and grid infrastructure density are propounded in this work, which will serve as vital parameters to determine grid resilience.

[en] Highlights: • Several temporal and operational model structures are systematically evaluated. • Weighted representative periods can better capture solar and short-term storage. • Aggregated, continuous representation is suitable with wind and long-term storage. • Adequate technology modelling is crucial for capturing energy sector interactions. • Testing several model structures for each system and purpose is important. Planning of future energy systems with higher prevalence of wind and solar energy requires a careful representation of the temporal and operational characteristics of the system in the investment planning model. This study aims to identify the aspects that should be considered when selecting the representation for a particular system. To demonstrate the impacts that various model representations have in terms of model accuracy and computational effort, we carry out case studies on two test systems implemented within the Backbone energy systems modelling framework. The results show that the temporal and operational representations have different benefits and weaknesses in different system types. The findings provide general guidelines on the relative importance of different model details, depending on the characteristics of the system under study. For example, some temporal sampling strategies can better capture long-term storage needs, while others are more suitable for short-term storage modelling. Likewise, solar-dominated and wind-dominated systems differ in their methodological requirements. Furthermore, the interactions between energy sectors and the operational limits of the technologies for sector coupling should be correctly captured, as they significantly impact on the value of different technologies and their flexibility. Finally, we recommend testing several temporal and technical representations for each particular system in order to ensure the feasibility of the selected method for that purpose. The findings and recommendations inform energy system modellers about improvements that will facilitate higher quality planning results.

[en] Highlights: • An improved index is derived to quantify the STVS of different network structures. • A BiLSTM-based model is developed to identify resilient network structures. • A transfer learning approach is proposed to adapt to new systems for expansion. • Network structure is described by sequences to extract the structural features. • Numerical tests show the high reliability and efficiency of the proposed approach. With increasing dynamic loads, short-term voltage stability (STVS) problems are emerging in sub-transmission expansion planning (SEP), which threats the stable operation of energy systems. However, it is computationally intensive to evaluate all possible network structures in SEP, since STVS is traditionally analyzed for a fixed network structure at a certain operating condition using time-domain simulations. Taking advantage of big data analytics, a deep transfer learning approach based on bi-directional long short-term memory (BiLSTM) is proposed to identify resilient network structures with better STVS performance efficiently. First, an improved voltage recovery index (IVRI) is introduced to quantify the STVS of different network structures with a higher degree of distinguishment. Then, a BiLSTM-based STVS evaluation machine is devised to identify resilient network structures with better STVS performances with high efficiency, which predicts the STVS of various network structures without resorting to time-consuming time-domain simulations. Finally, the STVS evaluation machine is transferred to adapt to new systems with different numbers of buses in the context of SEP. Numerical tests on the IEEE benchmarks and the real Guangdong Power Grid have verified the effectiveness of the proposed approach. An illustrative application example indicates the potential of the proposed approach in tackling STVS-based SEP for the stable operation of energy systems.

[en] Highlights: • We present a methodology for managing renewable farm outputs using storage devices. • A key feature of the algorithm is its lookahead decision framework where: costs are quantified over a rolling horizon; both predictions and decisions are updated on the fly as new data is acquired. • The algorithm is able to incorporate any optimization or forecasting approach given limitations on the time between real-time decision updates. • The presented approach is easily implementable in real-time settings where storage decisions may be updated regularly. This paper presents an integrative methodology for managing and stabilizing the output of a wind/solar farm using storage devices in a cost effective and real-time manner. We consider the problem where a renewable farm should decide the amount of energy charged into, or withdrawn from, the battery given the stochastic and time-varying nature in the renewable energy power output. Our methodology features a seamless integration of a non-myopic decision framework and a sequential non-parametric predictive model based on functional principal component analysis. A key feature of our algorithm is that it quantifies costs over a rolling horizon where both predictions and decisions are updated on the fly as new data is acquired. Our technology is tested on the California ISO dataset. The case study provides a proof-of-concept that highlights both the benefits and ease of implementation of our forward looking framework.