Large consumers in liberalized electricity markets rarely choose a supplier on price alone, yet electricity procurement research has concentrated on pricing, contract design and market structure rather than on how organizations evaluate competing suppliers. Drawing on Organizational Buying Behavior (OBB) and Buying Center Theory, this study examines supplier selection among thirteen large non-regulated consumers in Colombia's hydro-dominated electricity market. Semi-structured interviews with procurement managers were analyzed thematically. Managers described five groups of supplier attributes: price and cost predictability, generation backing and financial robustness, reputation and track record, renewable attributes, and technical support and value-added services. Competitive pricing operated as a threshold condition; the choice among acceptable offers depended on whether a supplier appeared able to honor a long-term contract under stressed market conditions, and reputation served as a proxy for capabilities that buyers cannot easily verify in advance. On this basis, the study proposes a market-conditioned framework in which wholesale-price volatility, hydrological variability, generation adequacy and counterparty exposure shape organizational priorities, which procurement managers reconcile when evaluating supplier attributes. The framework separates supplier attributes from the organizational, interpersonal and individual processes through which they are judged, and it distinguishes direct interview evidence, managers' reported accounts of organizational processes, interpretation grounded in the Colombian market context, and theoretical propositions. The study contributes to electricity procurement research by explaining supplier choice as a risk-allocation decision rather than a price comparison and by specifying which of its mechanisms may transfer to other liberalized markets.
Electric power disruptions impose substantial economic and societal costs, particularly in islanded power systems exposed to extreme weather, geographic isolation, and limited opportunities for grid support. Existing outage cost estimates are largely derived from mainland U.S. data and short-duration interruptions, limiting their applicability to prolonged outages in islanded systems. In addition, conventional approaches implicitly assume customer coping strategies, such as geographic relocation and spatial substitution, that are often unavailable in island environments, where constrained import capability, limited reserve sharing, and slower restoration may amplify the impacts of extended disruptions. This study presents the first empirical assessment of customer interruption costs in Puerto Rico for outages lasting one to 30 days. Using large-scale survey data and non-linear econometric modeling, we estimate duration-dependent customer damage functions for residential, non-residential, and public-sector customers. Results show that outage costs increase non-linearly with duration. Expressed as average daily costs, impacts initially decline as customers rely on available mitigation measures, but increase again during extended outages as mitigation resources become increasingly constrained and cumulative losses grow. Territory-wide economic impacts are estimated at approximately $1 billion, $5 billion, and $29 billion for 1-, 14-, and 30-day outages, respectively. The findings demonstrate that linear valuation approaches, including FEMA's Value of Unit Service assumptions, may substantially misrepresent the economic consequences of prolonged outages. While the estimated customer damage functions are specific to Puerto Rico, the methodological framework provides a foundation for improving outage valuation and resilience planning in other islanded systems and, with appropriate adaptation, interconnected mainland systems.
This paper explores how European natural gas disturbances are transmitted to the Italian economy and distinguishes energy-market pass-through from macroeconomic effects. The analysis uses monthly data from 2015M1 to 2026M6 and quarterly real GDP from 2015Q1 to 2026Q1. The empirical approach relies on error-correction analysis for the price block, a stationary short-run multivariate model, the structural gas-supply shock series by Alessandri and Gazzani (2025), quarterly local GDP projections, and historical plausibility of the 2022 energy crisis. The results clearly highlight a hierarchy in the transmission mechanism. A 10% gas-price innovation pushes wholesale electricity prices up by about 6.5% at impact, and the gas-to-electricity channel is the strongest and most robust. The pass-through to consumer-price inflation is smaller and delayed, whereas industrial-production responses are imprecise estimates. At the aggregate level, the direct quarterly GDP response to the identified gas-shortage shocks seems weak, statistically fragile, and sensitive across time horizons, so the data do not suggest a large mechanical gas-to-GDP elasticity. As such, the 2022 historical exercise is a plausibility test and not an accounting decomposition of the data. The 2026 episode is seen as the result of renewed energy-market stress rather than a nonlinear regime. In general, the findings support diversification, storage, grid investment, renewables, and system flexibility as pillars of macroeconomic resilience.
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.
