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An unreliable supply of grid electricity has a strong negative impact on industrial and commercial profitability as well as on household activities and government services that rely on electricity supply. This unreliable grid electricity could be a result of technical and security factors affecting the grid network. Therefore, this study aims to investigate the effects of technical and security factors on the transmission and distribution of grid electricity in Uganda.

This study used the ordinary least squares (OLS) and autoregressive distributed lag (ARDL) models to examine the effects of technical and security factors on grid electricity reliability in Uganda. The study draws upon secondary time series monthly data sourced from the Uganda Electricity Transmission Company Limited (UETCL) government utility, which transmits electricity to both distributors and grid users. Additionally, data from Umeme Limited, the largest power distribution utility in Uganda, were incorporated into the analysis.

The findings revealed that technical faults, failed grid equipment, system overload and theft and vandalism affected grid electricity reliability in the transmission and distribution subsystems of the Ugandan power grid network. The effect was computed both in terms of frequency and duration of power outages. For instance, the number of power outages was 116 and 2,307 for transmission and distribution subsystems, respectively. In terms of duration, the power outages reported on average were 1,248 h and 5,826 h, respectively, for transmission and distribution subsystems.

This paper investigates the effects of technical and security factors on the transmission and distribution grid electricity reliability, specifically focusing on frequency and duration of power outages, in the Ugandan context. It combines both OLS and ARDL models for analysis and adopts the systems reliability theory in the area of grid electricity reliability research.

Despite South Africa’s ailing electrical grid, substantial renewable energy (RE) integration is planned for the country. As grid-integrated RE affects all grids differently, this study aims to develop an adaptable grid code-guided renewable power plant (RPP) control real-time simulation testbed, tailored to South African grid code requirements to study grid-integrated RE’s behaviour concerning South Africa’s unique conditions.

The testbed is designed using MATLAB’s Simulink and live script environments, to create an adaptable model where grid, RPP and RPP guiding grid codes are tailorable. This model is integrated with OPAL-RT’s RT-LAB and brought to real-time simulation using OPAL-RT’s OP4510 simulator. Voltage, frequency and short-circuit event case studies are performed through which the testbed’s abilities and performance are assessed.

Case study results show the following. The testbed accurately represents grid code voltage and frequency requirements. RPP point of connection (POC) conditions are consistently recognized and tracked, according to which the testbed then operates simulated RPPs, validating its design. Short-circuit event simulations show the simulated wind farm supports POC conditions relative to short-circuit intensity by curtailing active power in favour of reactive power, in line with local grid code requirements.

To the best of the authors’ knowledge, this is the first design of an adaptable grid code-guided RPP control testbed, tailored to South African grid code requirements in line with which RPP behavioural and grid integration studies can be performed.

The intended nationally determined contributions (INDCs) is a major outcome of the Paris Agreement on international cooperation to reduce emissions, and is likely to be the future scenario for carbon emissions. This paper aims to obtain the fine spatial pattern of carbon emissions in 2030, identify hot spots and analyze changes of carbon emissions with a spatial grid method.

Based on the integrated quantified INDCs of each economy in 2030, the authors predict the population density pattern in 2030 by using the statistics of current population density, natural growth rates and differences in population growth resulting from urbanization within countries. Then the authors regard population density as a comprehensive socioeconomic indicator for the top-bottom allocation of the INDC data to a 0.1° × 0.1° grid. Then, the grid spatial pattern of carbon emissions in 2030 is compared with that in 2016.

Under the unconditional and conditional scenarios, the global carbon emission grid values in 2030 will be within [0, 59,200.911] ktCO₂ and [0, 51,800.942] ktCO_2, respectively; eastern China, northern India, Western Europe and North America will continue to be the major emitters; grid carbon emissions will increase in most parts of the world compared to 2016, especially in densely populated areas.

While many studies have explored the overall global carbon emissions or warming under the INDC scenario, attention to spatial details is also required to help us make better emissions attributions and policy decisions from the perspective of the grid unit rather than the administrative unit.

Gridded management in the public service supply is still in the experience exploratory stage, and this paper aims to analyze the inherent logic and operation mode of the gridding mechanism of the public supply based on the existing theory study and practices, and verify its efficiency so as to come to the conclusion whether it could be promoted to a wider range.

The methodology applied in this paper was case study/deductive induction.

The grid model in the public service supply needs to be demonstrated completely in theoretical logic and operation principles before it is promoted across the country. Meanwhile, full support of the government is required in terms of service concept, function distribution, technical parameters and infrastructure.

The inherent logic and operation mode of the gridding mechanism of the public service supply needs enough practice tests. The practical test of efficiency analysis of the gridding mechanism of the public service supply is not enough.

This paper validated whether the gridding mechanism that originated from China’s urban management can be promoted to all over the country in the public service supply. It provides references for government policy.

This paper constructs a gridded management model for public service provision in urban and rural areas on the basis of an analysis of the plight of traditional model of public service provision, thus delivering the same standard of public service for both urban and rural areas through optimization of resource allocation without requiring more supply and fundamental change to the content of service.

Green energy as a transportation supply trend is irreversible. In this paper, a highway energy supply system (HESS) evolution model is proposed to provide highway transportation vehicles and service facilities with a clean electricity supply and form a new model of a source-grid-load-storage-charge synergistic highway-PV-WT integrated system (HPWIS). This paper aims to improve the flexibility index of highways and increase CO₂ emission reduction of highways.

To maximize the integration potential, a new energy-generation, storage and information-integration station is established with a dynamic master–slave game model. The flexibility index is defined to evaluate the system ability to manage random fluctuations in power generation and load levels. Moreover, CO₂ emission reduction is also quantified. Finally, the Lianhuo Expressway is taken as an example to calculate emission reduction and flexibility.

The results show that through the application of the scheduling strategy to the HPWIS, the flexibility index of the Lianhuo Expressway increased by 29.17%, promoting a corresponding decrease in CO₂ emissions.

This paper proposed a new model to capture the evolution of the HESS, which provides highway transportation vehicles and service facilities with a clean electricity supply and achieves energy transfer aided by an energy storage system, thus forming a new model of a transportation energy system with source-grid-load-storage-charge synergy. An evaluation method is proposed to improve the air quality index through the coordination of new energy generation and environmental conditions, and dynamic configuration and dispatch are achieved with the master–slave game model.

Giant orthogonal grid barrel vault is generated by deleting members in the inessential force transfer path of the two-layer lattice barrel vault. Consisting of members in the essential transfer path only, giant orthogonal grid barrel vault is a new type of structure with clear mechanical behavior and efficient material utilization. The paper aims to discuss this issue.

The geometrical configuration of this structure is analyzed, and the geometrical modeling method is proposed. When necessary parameters are determined, such as the structural span, length, vault rise, longitudinal and lateral giant grid number and section height to top chord length ratio of the lattice member, the structure geometrical model can be generated.

Numerical models of giant orthogonal grid barrel vaults with different rise–span ratios are built using the member model that can simulate the pre-buckling and post-buckling behavior. So the possible member buckle-straighten process and the plastic hinge form–disappear process of the structure under strong earthquake can be simulated.

Seismic analysis results indicate that when the structure damages under strong earthquake there are a large number of buckling members and few endpoint plastic hinges in the structure. Dynamic damage of giant orthogonal grid barrel vault under strong earthquake is caused by buckling members that weaken the structural bearing capacity.

The paper aims to present a grid-connected multi-inverter for solar photovoltaic (PV) systems to enhance reliability indices after selected the placement and level of PV solar.

In this study, the associated probability is calculated based on the solar power generation capacity levels and outages conditions. Then, based on this probability, dependability indices like average energy not supplied (AENS), expected energy not supplied and loss of load expectations (LOLE) are computed, also, another indices have been computed such as (customer average interruption duration index (CAIDI), system average interruption frequency index (SAIFI) and system average interruption duration index (SAIDI)) addressing by affected customers with distribution networks reliability assessment, including PV. On the basis of their dependability indices and active power flow, several PV solar modules installed in several places are analyzed. A mechanism for assessing the performance of the grid's integration of renewable energy sources is also under investigation.

The findings of this study based on data extracted form a PV power plant connected to the power network system in Diyala, Iraq 132 kV, attempts to identify the system's weakest points in order to improve the system's overall dependability. In addition, enhanced reliability indices are given for measuring solar PV systems performance connected to the grid and reviewed for the benefit of the customers.

The main contributions of this study are two methods for determining the reliability of PV generators taking into consideration the system component failure rates and the power electronic component defect rates in a PV system which depend on the power input and the power loss using electrical transient analysis program (ETAP) program.

This study aims to investigate the intricate relationships between a community energy system, water resources and biodiversity conservation, with a specific focus on augmenting community energy resilience in Bondo. The primary objective is to gain an in-depth understanding of how community members perceive and experience the challenges related to balancing the often-conflicting demands of energy, water and biodiversity conservation within this context.

The research uses a qualitative approach to unravel the multifaceted dynamics of community energy systems, water resources and biodiversity conservation in Bondo. Data were collected through focus groups and direct observations, enabling a nuanced exploration of community perspectives and lived experiences. The subsequent analysis of this qualitative data follows established thematic analysis procedures.

The study's findings shed light on the formidable barriers that impede rural communities in Malawi from accessing electricity effectively. Even in communities fortunate enough to have electricity connections, the lack of knowledge regarding productive electricity use results in community energy systems operating at significantly reduced load factors. Furthermore, the intricate challenge of managing a biodiversity hotspot persists, exacerbated by the densely populated peripheral communities' continued reliance on forest, land and water resources. These activities, in turn, contribute to ecosystem degradation.

In a context where government-led management of forest reserves and game reserves has not yielded the expected results due to a multitude of factors, there arises a compelling need for innovative approaches. One such innovation involves fostering partnerships between the government and experienced trusts as lead organisations, providing a fresh perspective on addressing the complex interplay between community energy systems, water resources and biodiversity conservation. This novel approach opens doors to explore alternative pathways for achieving the delicate balance between human energy needs and the preservation of vital ecosystems.

Quantitative and spatial-explicit flood risk information is of great importance for strengthening climate change adaptation and flood resilience. Shanghai is a coastal megacity at large estuary delta with rising flood risks. This study aims to quantify the overall economic-societal risks of storm flooding and their spatial patterns in Shanghai.

Based on multiple storm flood scenarios at different return periods, as well as fine-scale data sets including gridded GDP, gridded population and vector land-use, a probabilistic risk model incorporating geographic information system is used to assess the economic-societal risks of flooding and their spatial distributions.

Our results show that, from 1/200 to 1/5,000-year floods, the exposed assets will increase from USD 85.4bn to USD 657.6bn, and the direct economic losses will increase from USD 3.06bn to USD 52bn. The expected annual damage (EAD) of assets is around USD 84.36m. Hotpots of EAD are mainly distributed in the city center, the depressions along the upper Huangpu River in the southwest, the north coast of Hangzhou Bay, and the confluence of the Huangpu River and Yangtze River in the northeast. From 1/200 to 1/5,000-year floods, the exposed population will rise from 280 thousand to 2,420 thousand, and the estimated casualties will rise from 299 to 1,045. The expected annual casualties (EAC) are around 2.28. Hotspots of casualties are generally consistent with those of EAD.

In contrast to previous studies that focus on a single flood scenario or a particular type of flood exposure/risk in Shanghai, the findings contribute to an understanding of overall flood risks and their spatial patterns, which have significant implications for cost-benefit analysis of flood resilience strategies.

Large/stochastic spatiotemporal demand data sets can prove intractable for location optimization problems, motivating the need for aggregation. However, demand aggregation induces errors. Significant theoretical research has been performed related to the modifiable areal unit problem and the zone definition problem. Minimal research has been accomplished related to the specific issues inherent to spatiotemporal demand data, such as search and rescue (SAR) data. This study provides a quantitative comparison of various aggregation methodologies and their relation to distance and volume based aggregation errors.

This paper introduces and applies a framework for comparing both deterministic and stochastic aggregation methods using distance- and volume-based aggregation error metrics. This paper additionally applies weighted versions of these metrics to account for the reality that demand events are nonhomogeneous. These metrics are applied to a large, highly variable, spatiotemporal demand data set of SAR events in the Pacific Ocean. Comparisons using these metrics are conducted between six quadrat aggregations of varying scales and two zonal distribution models using hierarchical clustering.

As quadrat fidelity increases the distance-based aggregation error decreases, while the two deliberate zonal approaches further reduce this error while using fewer zones. However, the higher fidelity aggregations detrimentally affect volume error. Additionally, by splitting the SAR data set into training and test sets this paper shows the stochastic zonal distribution aggregation method is effective at simulating actual future demands.

This study indicates no singular best aggregation method exists, by quantifying trade-offs in aggregation-induced errors practitioners can utilize the method that minimizes errors most relevant to their study. Study also quantifies the ability of a stochastic zonal distribution method to effectively simulate future demand data.