Network control is a key process for distribution network operations, where electricity supply is restored through a combination of remote switching and field crew activities. Although analytical methods have been widely applied to problems such as network expansion and restoration sequencing, broader operational decisions, such as workforce availability and the extent of cooperation between adjacent depots, are often based on subjective management judgement, partly because evaluating their business-wide effects requires substantial computational resources. The emergence of cloud computing and high-performance processors allow network operations, and the temporal and spatial uncertainty of faults, to be modelled across an entire distribution network within a practical timeframe and at an acceptable computational cost. In this paper, we explain how an Australian Distribution Network Service Provider (DNSP) has developed an integrated model utilising discrete-time simulation, fuzzy logic, and design of experiments (DOE) to evaluate strategic network operations decisions and optimise the trade-off between regulatory penalties and operations costs. The selected case study showed that partially relaxing the boundaries between depots can reduce average outage duration and regulatory penalties, although it may also lead to higher operations costs. Managerial Relevance Statement One of the challenges facing distribution network service providers (DNSPs) is assessing how changes in network control and operations processes will affect operations costs and regulatory penalties. The uncertainty involved in estimating how such changes affect the company's bottom line can make management reluctant to implement proposed changes, as their effects on operational costs, regulatory penalties, and employees’ work-life balance are difficult to quantify. Consequently, potentially beneficial operational changes may not be pursued. To help evaluate these decisions, we developed a decision-support tool that combines discrete-time simulation with fuzzy logic. The tool is currently being used to support decision-making by a DNSP serving more than one million customers in Australia, across Central and Western Victoria.
This paper presents a geospatially resolved modeling framework for ex-ante assessment of electricity subsidy instruments applied to least-cost electrification plans. The framework integrates subsidy modeling directly into the planning stage, enabling evaluation of how alternative subsidy structures alter private developer incentives, mini-grid system sizing decisions, and cost outcomes — before programs are deployed. We apply the framework to Sierra Leone, examining three instruments selected for their prevalence in electrification programs: direct capital subsidies, concessional finance, and supply-side tariff subsidies. The analysis yields three principal findings. First, investment subsidies introduce efficiency distortions: capital grants and concessional finance both incentivize system over-sizing, generating excess capital expenditure for developers and financiers alike, while tariff subsidies create a recurring fiscal burden without inducing the same investment distortion. Second, under a constrained public budget, the instruments produce divergent electrification trajectories: capital subsidies accelerate the rate of new connections, concessional finance may slow it, and tariff subsidies have a negligible effect on connection pace. Third, subsidy allocation strategies that prioritize developer financial sustainability tend to favor high-demand, densely populated communities, deferring connections to lower-demand settlements and thereby reinforcing existing spatial inequalities in access. These results demonstrate that the efficiency and distributional consequences of subsidy design are inseparable: instrument choice determines not only how public resources are spent but also who benefits, when, and at what long-run cost. The framework provides a practical tool for policymakers to evaluate these trade-offs before committing to a subsidy program.
Nigeria’s electricity system has long struggled to deliver reliable, affordable, and sustainable power despite the country’s substantial energy endowments. Mounting climate obligations and a rapidly expanding population underscore the inadequacy of continuing dependence on fossil-fuel-based energy generation. The sector remains hindered by chronic instability, constrained generation capacity, gas supply interruptions to thermal plants, hydrological volatility affecting hydropower output, high technical and commercial losses across the transmission network, low metering devices, and ageing infrastructure that leaves millions outside the formal grid. The Electricity Act 2023 (as amended 2024) represents Nigeria’s most ambitious legislative effort to restructure the power sector since liberalisation. This article undertakes a doctrinal and policy-oriented legal analysis to interrogate the statute’s innovations, operational constraints, and alignment with global best practices. Drawing on statutory interpretation, judicial authorities, and contemporary scholarly discourse, the paper demonstrates that although the Act signals a significant shift toward decentralisation and renewable integration, several structural deficiencies persist. These include ambiguous regulatory mandates, an over-centralised tariff-setting framework, weak coordination between federal and state regulatory spheres, and imprecise terminologies that risk administrative uncertainty in areas such as mini-grids and embedded generation. The article proposes targeted amendments to strengthen regulatory clarity, streamline institutional responsibilities, and introduce enforceable compliance mechanisms capable of supporting investment, enhancing grid resilience, and accelerating Nigeria’s transition to a diversified and sustainable electricity system. Addressing these gaps is essential for transforming the Act from an aspirational reform instrument into a functional catalyst for long-term energy security and economic development in Nigeria.
China’s distributed photovoltaic (PV) expansion has accelerated through policy support, promotional activity, and diverse ownership models. While this growth has increased deployment, it has also created user-side risks that are not always visible in aggregate installation statistics. Using Expectation-Disconfirmation Theory as an analytical lens, this study examines post-installation experiences among residential PV users in China. It draws on semi-structured interviews with 50 self-selected but information-rich users who shared installation experiences on Chinese social media platforms. Rather than estimating population-level satisfaction or problem prevalence, the study identifies recurring sources of expectation confirmation and disconfirmation. Dissatisfaction often arose from after-sales service failures, contractual or rights-protection disputes, misleading promotion, involuntary installation, and fraud-related risks. Business models shaped risk exposure by allocating ownership, maintenance responsibilities, financial obligations, and property-rights constraints in different ways. The findings show that actual performance in residential PV includes not only technical and economic outcomes, but also service, contractual, and governance-related performance. The study provides diagnostic evidence for improving consumer protection, contract transparency, and post-installation governance in China’s distributed PV sector.
Accurate estimation of peak electricity loads is important for planning infrastructure and system costs. Prospective energy system models often emphasize total generation rather than instantaneous power draw, in part for computational tractability, and thus necessarily underestimate true peak loads with coarse timesteps and low-resolution representation of demand. As the structure of electricity demand changes (e.g., due to beneficial electrification), historical heuristics about acceptable timesteps for estimating peak loads might no longer hold. Here, we use a validated agent-based model of households in Aotearoa New Zealand to estimate peak errors under electrification and other conditions by generating 1-minute residential electricity demand profiles, then resampling with timesteps from 2 min to 24 h. Coarser timesteps increase peak load errors: 1-hour timesteps underestimate true peaks by 44% for one household (with implications for e.g., electric panel sizing), and 9% for 100 households (with implications for e.g., total dispatchable generation requirements). Error is sensitive to large insulation increases, solar PV, and electric vehicles. We recommend modelers should avoid coarse timesteps for applications like capacity expansion or battery sizing, where absolute peaks matter. We encourage modelers to carefully consider how changing system structure changes assumptions about acceptable simplifications, especially in safety-relevant contexts.
