Electricity Tariffs Research Articles

Electricity subsidies targeting performance indicators in selected Latin American countries

ABSTRACT This document calculates targeting performance indicators of electricity subsidies in Mexico, Panama, the Dominican Republic, and Uruguay. To do this, beneficiary and benefit incidence indicators are calculated using official household survey data and administrative records on electricity tariffs. In addition, this paper calculates inclusion and exclusion errors in the targeting of electricity subsidies. The results show that the share of poor households that receive the subsidy is very large in Mexico, Panama, and the Dominican Republic. However, the distribution of the subsidy is regressive in absolute terms, which implies that most of every dollar spent on keeping electricity prices low will benefit the wealthiest households. This document also finds large inclusion errors suggesting that an important share of the non-poor is also benefiting from the subsidy. These results shed light on the evaluation of electricity subsidies in the region to improve targeting and optimisation of resources.

Optimal planning of renewable energy park for green hydrogen production using detailed cost and efficiency curves of PEM electrolyzer

Installing multi-renewable energy (RE) power plants at designated locations, known as RE parks, is a promising solution to address their intermittent power. This research focuses on optimizing RE parks for three scenarios: photovoltaic (PV)-only, wind-only, and hybrid PV-wind, with the aim of generating green hydrogen in locations with different RE potentials. To ensure rapid response to RE fluctuations, a Proton Exchange Membrane (PEM) electrolyzer is employed. Furthermore, this research proposes detailed models for manufacturer-provided wind power curves, electrolyzer efficiency against its operating power, and electrolyzer cost towards its capacity. Two optimization cases are conducted in MATLAB, evaluating the optimum sizes of the plants in minimizing levelized cost of hydrogen (LCOH) using classical discrete combinatorial method and determining the ideal PV-to-wind capacity ratio for operating PEM electrolyzer within hybrid PV-wind parks using particle swarm optimization. Numerical simulations show that wind power-based hydrogen production is more cost-effective than PV-only RE parks. The lowest LCOH, $4.26/kg H2, and the highest LCOH, $14.378/kg H2, are obtained from wind-only and PV-only configurations, respectively. Both occurred in Adum-Kirkeby, Denmark, as it has highest average wind speed and lowest irradiance level. Notably, LCOH is reduced with the hybrid PV-wind configuration. The results suggest the optimum PV-to-wind capacity ratio is 65:35 on average and indicate that LCOH is more sensitive to electrolyzer's cost than to electricity tariff variation. This study highlights two important factors, i.e., selecting the suitable location based on the available RE resources and determining the optimum size ratio between the plants within the RE park.

Optimal retail contracts with contractible uncertainty

Around the world policymakers and regulators are struggling with the question of how to design retail electricity tariffs in the face of increasing penetration of local generation (e.g., solar PV), smart appliances, local storage, and electric vehicles. There is a widespread recognition that retail tariffs should vary dynamically across time and space, reflecting the changing conditions (congestion and losses) on the underlying networks. But, at the same time, there is recognition that such tariffs potentially expose retail customers to substantial risk. Risk averse retail customers desire protection against price spikes and volatile wholesale spot prices. This paper seeks to derive the optimal retail contract in the special case in which the uncertainty in the market is contractible (in the sense defined here). We show that the optimal retail contract exposes the prosumer to the wholesale spot price at the margin, but also perfectly insulates the customer from risk, achieving the first-best outcome. We show how the hedge component of this retail contract can be constructed from standard-form hedge contracts. We draw out several lessons for policymakers.

Decarbonization through Active Participation of the Demand Side in Relatively Isolated Power Systems

In the context of power system decarbonization, the demand-side strategy for increasing the share of renewable energy is studied for two constrained energy systems. This strategy, which is currently widely suggested in policies on the energy transition, would impact consumer behavior. Despite the importance of studying the latter, the focus here is on decisions regarding the type, location, and timeframe of implementing the related measures. As such, solutions must be assessed in terms of cost and feasibility, technological learning, and by considering geographical and environmental constraints. Based on techno-economic optimization, in this paper we analyze the evolution of the power system and elaborate plausible long-term trajectories in the energy systems of two European islands. The case studies, Procida in Italy and Hinnøya in Norway, are both electrically connected to the mainland by submarine cables and present issues in their power systems, which are here understood as relatively isolated power systems. Renewable energy integration is encouraged by legislative measures in Italy. Although not modeled here, they serve as a backbone for the assumptions of increasing these investments. For Procida, rooftop photovoltaics (PV) coupled with energy storage are integrated in the residential, public, and tertiary sectors. A price-based strategy is also applied reflecting the Italian electricity tariff structure. At a certain price difference between peak and off-peak, the electricity supply mix changes, favoring storage technologies and hence decreasing imports by up to 10% during peak times in the year 2050. In Norway, renewable energy resources are abundant. The analysis for Hinnøya showcases possible cross-sectoral flexibilities through electrification, leading to decarbonization. By fine-tuning electric vehicle charging tactics and leveraging Norway’s electricity pricing model, excess electricity demand peaks can be averted. The conclusions of this double-prospective study provide a comparative analysis that presents the lessons learnt and makes replicability recommendations for other territories.

Effective communication of time–of–use electricity tariffs: Plain and simple

Time-of-use (ToU) tariffs bring important benefits to consumers and society, but some individuals may find it harder to benefit from them than others. Communication around ToU tariffs needs to explain both benefits and challenges successfully. This article presents results from a pre-registered experiment with N = 1300 consumers in Ireland that pre-tested behaviourally-informed communications on ToU tariffs. Consumers were generally positive towards ToU tariffs. Emphasising environmental benefits enhanced positivity among younger participants. Comprehension and choice quality were modest, but improved when tariff examples were presented in a plain table, compared to 24hour clock formats that are commonly used in the market internationally.

Remarkable photodegradation breakdown cost, antimicrobial activity, photocatalytic efficiency, and recycling of SnO2 quantum dots throughout industrial hazardous pollutants treatment

In the conducted research, a one-step hydrothermal synthesis of pure and titanium-doped tin dioxide quantum dots is elaborated upon, with a thorough analysis of their structural, optical, morphological, and photocatalytic properties undertaken using advanced analytical techniques. Through X-ray Diffraction XRD, the crystalline nature and phase purity of the tetragonal structures of SnDs were confirmed, with the crystallite sizes measured at 3.0 nm for SnD1 and 7.66 nm for SnD2, following treatments at 240 °C and 300 °C, respectively. The structural integrity of SnO2 was maintained despite titanium doping. FTIR spectroscopy verified the existence of specific vibrational modes indicative of surface hydroxyl groups. HRTEM images revealed the spherical morphology of particles, with diameters of 3.5 nm for SnD1 and 9.1 nm for SnD2. Optical band gaps, determined through UV-DRS, ranged from 3.33 eV in SnD1 to 3.47 eV in SnDTi2. The photocatalytic degradation of Congo Red dye under xenon lamp irradiation was quantitatively assessed; notably, SnD1 exhibited a 23 % higher rate constant compared to SnD2, attributed to its smaller particle size and a 31 % greater surface area. Doping with 4 % Ti in Sn0.96Ti0.04O2 more than doubled the degradation rate compared to a 6 % Ti doping in Sn0.94Ti0.06O2. Furthermore, the generation of hydroxyl radicals was significantly enhanced, showing an increase of approximately 220 % for SnD1 and 80 % for SnD2. The capability of these nanomaterials to reduce the chemical oxygen demand of industrial organic pollutants to within regulatory limits under solar irradiation was documented, with SnD1 maintaining its photocatalytic efficiency over seven cycles of reuse. In the photocatalytic degradation rate of Congo Red dye, which was 23 % higher for SnD1 compared to SnD2, and the threefold increase in the degradation rate for SnDTi1 compared to SnDTi2. An economic assessment, based on electricity tariffs in Saudi Arabia, highlighted the cost-effectiveness of SnD1, which ranged from 26.93 to 30.36 USD per breakdown cost of the photodegradation process, showing it to be less costly than SnD2. Conversely, SnDTi1 was found to be more economical than SnDTi2, with costs ranging from 26.67 to 33.09 USD. Collectively, the results emphasize the outstanding photocatalytic performance and cost-efficiency of SnDs, reinforcing their potential as sustainable solutions for the treatment of industrial wastewater. Additionally, the antibacterial efficacy of these materials against a range of bacteria, yeast, and fungi was investigated and substantiated.

System Optimization and Operating Strategy of Single-Stage Air Source Heat Pump with Thermal Storage to Reduce Wind Power Curtailment

Wind power generation has increased in China to achieve the target of decreasing CO2 emissions by 2050, but there are high levels of wind curtailment due to the mismatch between electricity supply and demand. This paper proposes a single-stage air source heat pump coupled with thermal storage for building heating purposes. The main objective is to find the proper system designs and operating strategy, which can help to avoid peak demand periods while obtaining minimized running costs and reduced wind energy curtailment. Dynamic simulations were performed using TRNSYS to investigate its use in a typical office building based on an actual electricity tariff, wind power, and meteorological data. The proper system designs, including the tank size and thermal storage temperature, were determined to maximize the system’s performance. It was found that a proper combination of the two parameters exists for a specific application. Further, results showed that the use of auxiliary electric heating is necessary for single-stage air source heat pumps to participate in a wind curtailment reduction. The operating strategy of the system was also studied. Results indicate that by implementing a proper operating strategy, non-renewable power consumption can be reduced by 11% for the studied building, with a total wind power utilization of 3348 kWh during the heating season while still satisfying the heating demands of users. These findings can contribute to the green and low-carbon development of the building industry and further enhance the grid’s accommodation capacity for renewable energy sources.

A general sizing methodology of grid-connected PV systems to meet the zero-energy goal in buildings

This work expresses the most general definition of zero-energy building through a set of equations based on measured energy consumption and electricity generation predictions. This helps determining the optimal size of grid-connected photovoltaic systems for upgrading any highly efficient or conventional building into a zero-energy building. Opposed to previous works, which consider only electrical consumptions, this article includes consumption from fossil fuels and district heat and cooling networks. The study also covers environmental parameters and an economic study adaptable to any electricity market structure, contributing to deciding the best electricity tariff to hire.The methodology was applied to passive and conventional single-family houses in Spain. Results showed that the performance of a grid-connected photovoltaic system depends critically on the energy sources imported by the building. In particular, the performance improves in buildings where the electricity represents most of the imported energy. The required minimum size of the photovoltaic system in the conventional house (14 kWp) is far greater than in the passive house (9 kWp). After the recent global increase in electricity prices, annual economic savings of 70.12 % and 49.71 %, and payback periods of 6 and 13 years for the passive and conventional houses, respectively, make the investment profitable.

Navigating the transition: The impacts of electricity tariff regulation on distributed generation in Brazil

Energy transition presents new challenges in electricity regulation, notably Distributed Generation (DG) tariffs. In Brazil, DG capacity surged 18 times between 2021 and 2023. The impact of recent Brazilian DG legislation (Law 14300/2022; Normative Resolution 1059/2023) on the market remains uncertain. Our analysis, from technical, social, and financial perspectives, focusing on units with up to 5 MW of installed capacity, reveals persisting and increasingly unsustainable cross-subsidy schemes. While new rules mitigate the risk of a “utility death spiral,” misguided regulations may spur DG adoption and hike electricity tariffs. Revising subsidy regulations can mitigate adverse social and financial repercussions.

How explain on-grid PV systems diffusion? Review and application in Brazil

This paper examines the remarkable growth of photovoltaic (PV) solar energy technology in Brazil, which stands out among the leading countries in photovoltaic installations. The recent implementation of Law 14300 on grid-connected PV systems has caused a stir in the Brazilian market, with investors anticipating installations to avoid reductions in benefits. This phenomenon, previously observed in Europe, instigates an understanding of the variables underlying the exponential growth of the Brazilian market. This study seeks to identify the predictors that explain the PV systems diffusion in Brazil, in two stages: a systematic review of the literature to map approaches and methods, followed by the construction of a database and the application of predictive models. The literature revealed 24 methods in six approaches, with 44 predictors. For Brazil, the database contains 90,129 instances, 22 predictors, and covers all 5570 municipalities. The results highlight the crucial importance of the electricity tariff as the main predictor, followed by irradiation and municipal Gross Domestic Product. Predictors such as the number of companies, minimum wage, HDI education, demographic density, and vehicle fleet prove to be relevant. In conclusion, the spatially explicit analysis highlights the complexity of the market, offering valuable implications for energy planners and stakeholders.

Commercial electric vehicle fleets in U.S. ancillary services markets: A stochastic analysis to inform utility rate design

For commercial fleets in the United States, traditional electricity tariffs can disincentivize vehicle-to-grid (V2G) participation and render electrification less attractive. First, we show that absent a rate redesign, opportunities for both fleets and the grid are missed. We propose a rate design modification: demand charge relaxation during off-peak (overnight) hours. This approach would enable fleets to shift additional load share to overnight hours and increase ancillary service revenues without the expectation of impacting local grid cost drivers (i.e., coincident peak load). Applied to a California case study, operational savings spanning 7.5% and 20.6% are realized via increased capacity revenues and deferred demand charges.

Community-based transactive energy market concept for 5th generation district heating and cooling through distributed optimization

Fifth-generation district heating and cooling (5GDHC) is an emerging concept that exploits heat sharing among prosumers through a low-temperature bidirectional loop. Coordinating the energy interactions in such a system is a complex problem, typically solved through centralized optimization. In addition to privacy issues for prosumers, centralized optimization can prove too hard to solve for large 5GDHC systems and cannot establish a profit distribution between prosumers. To overcome these drawbacks, this paper develops a community-based transactive energy market framework based on distributed optimization of 5GDHC. The optimization is based on the Jacobi-proximal alternating direction method of multipliers. The approach relies on iterative interactions between the network coordinator and the prosumers, the former adjusting the internal price of thermal energy and the latter adapting their heating and cooling demand. A series of test-case 5GDHC networks involving houses, commercial buildings, data centers and central boilers and chillers were numerically simulated to demonstrate how the proposed framework performs, under three electricity tariff structures (constant, time-of-use or pay for peak). Results show that, for the cases tested, the gap between the proposed approach and global optimization is below 5% and that electricity savings compared to a situation without 5GDHC could reach 40%. The proposed method allowed to dynamically adjust the internal price of thermal energy and the prosumers' demand while considering their thermal dissatisfaction. By facilitating 5GDHC operation, this work could help their practical implementation in the future.

Incorporating energy storage system into grid connected photovoltaic system with the application of wireless sensor network

The growing use of residential photovoltaics (PV) poses several challenges for distribution system operators. Technical challenges arise when excess PV energy is integrated into the low-voltage utility grid. This leads to current and voltage increase. On the other hand, economic challenges show up when the energy storage system (ESS) is fully charged without a mechanism in place to store excess energy. Residential ESS not only mitigates these challenges but also assists in mitigating irregular PV power issues. Different methodologies have been developed to construct models assessing the techno-economic benefits of integrating ESS with PV systems. Nevertheless, these approaches do not account for PV-ESS operation with feed-in-tariff (FiT) incentives, different electricity rate structures, and the lifetime model of ESS. This paper proposes an energy management strategy (EMS) suitable for rooftop PV installations. The EMS incorporates a temperature model and optimizes scheduling to regulate the power output of the PV/ESS in response to changes/needs in electricity demand. In addition, tactical information exchange between the ESS and the utility grid is facilitated by Link 16 wireless sensor networks. Furthermore, hybrid gated recurrent unit (GRU) and graph convolutional network (GCN) model is introduced for predicting PV power and electricity load. Consumer electricity data, PV data, and tariffs are utilized in the proposed study to examine the impact of MESS on electricity charges. The impact of multi-ESS (MESS) on the objective function is evaluated using the optimization model. Simulation results on a typical system show that the proposed approach significantly enhances battery performance. By the end of the year, the SOC reaches 84.78 % for the proposed MESS and 84.64 % for the DESS. The results reveal that the proposed approach yields 22 % cost savings compared to a dual ESS system and 24 % cost reduction compared to a single ESS. Moreover, the elevated packet delivery ratio values corresponding to higher signal-to-noise ratio levels signify enhanced reliability and superior communication performance. The impact of jamming attacks on battery consumption employing additive white Gaussian noise (AWGN) is compared with AWGN and single-tone, and AWGN and multi-tone. The results show that when dealing solely with AWGN, less power is needed to filter out noise.

Real options analysis of seawater desalination with solar photovoltaic energy systems: A least-squares stochastic perspective

Real Options Analysis (ROA) offers a modern theoretical framework to evaluate the impact of managerial flexibilities and uncertainties on the intrinsic investment value of renewable-powered desalination projects. This study analyzes Photovoltaic-powered Reverse Osmosis (PV-RO) desalination plants using a novel Least Square Monte-Carlo Real Options Analysis (LSMC-ROA) to assess the deferral of investment decisions. Three scenarios for powering RO are explored: reliance on the national grid with a subsidized electricity tariff for of $0.085/kWh, which applicable only to governmental sections in Saudi Arabia, full PV power with grid balancing ($0.083/kWh), and PV with a battery storage system (average electricity cost of $0.138/kWh). The presented modeling results reveal that considering the flexibility to defer and wait for new information, the total investment values would be $43.9 million, $59.6 million, and $30.5 million, respectively. Therefore, the optimal investment timing might be to defer investment to the second year. Additionally, the analysis reveals an unexpected outcome: a threshold salinity level for feedwater exists that can affect the economic viability of renewable-powered desalination. The proposed LSMC-ROA framework emerges as a robust tool for addressing uncertainties and assessing economic feasibility in the context of PV-RO desalination projects.

Towards sustainable smart cities: Integration of home energy management system for efficient energy utilization

Smart Homes (SHs) play a crucial role in the broader context of smart cities, contributing to the overall efficiency, sustainability, and quality of life for residents. This work presents a computationally efficient mathematical model for Home Energy Management Systems (HEMS) to activate the Demand Side Management (DSM) while minimizing the energy consumption costs of SHs. The proposed model is represented as a standard Mixed-Integer Linear Programming (MILP) optimization problem. The proposed HEMS provides the optimal scheduling of home appliances and plugging time for charging Electric Vehicles (EV) at home. The main objective of this optimization problem is to minimize the electricity bills while improving the load profile of the distribution system in the context of smart cities. In this study, load management for a typical household has been studied considering several electricity tariffs in Portugal. The simulation results confirm that in the presence of the proposed HEMS, the electricity bill will be effectively reduced while consumer behavior is changed. The cost savings that can be attained through only shifting loads is 5.7 %, while it can reach 67.65 % by installing a hybrid energy system at the studied smart home. The weekly cost reduction percentage due to charging the EV at home is around 16.24 % compared to charging the EV using public charging stations regardless of the traveling time and waiting at the public charging stations.

Spatial and Temporal Variability of Ocean Thermal Energy Resource of the Pacific Islands

A lack of natural resources drives the oil dependency in Pacific Island Countries and Territories (PICTs), hampering energy security and imposing high electricity tariffs in the region. Nevertheless, the Western Equatorial Pacific is known for its large Sea Surface Temperature (SST) and deep-sea water (DSW) temperature difference favorable for harvesting thermal energy. In this study, we selected 18 PICTs in the western Equatorial Pacific to estimate Annual Energy Production (AEP) for a 1 MW class Ocean Thermal Energy Conversion (OTEC) plant. We combined the DSW temperature from the mean in situ Argo profiles and 1 km resolution satellite SST data to estimate the thermal energy resource resolving the fine features of the island coastline. Furthermore, the twenty-year-long SST dataset was used to analyze the SST variability. The analysis showed that Equatorial islands and Southern islands have the highest inter-annual variability due to El Nino Southern Oscillation (ENSO). The power density varied from 0.26 to 0.32 W/m2 among the islands, with the lowest values found for the southernmost islands near the South Equatorial Countercurrent. Islands within the South Equatorial Current, Equatorial Undercurrent, and North Equatorial Countercurrent showed the highest values for both power density and gross power. Considering a 1 MW class OTEC plant, Annual Energy Production (AEP) in 2022 varied from 7 GWh to 8 GWh, with relatively low variability among islands near the Equator and in low latitudes. Considering the three variables, AEP, SST variability, and distance from the shore, Nauru is a potential candidate for OTEC, with a net power of 1.14 MW within 1 km from the shore.

Costs versus revenues of flexibility enhancement techniques for thermal power units in electricity-carbon joint markets

Thermal power units (TPUs) are required to enhance operational flexibility to ensure power grids accommodate the increasing penetration of intermittent renewables. The flexibility enhancement includes increasing peak shaving depth, increasing ramp rate, or both. In electricity-carbon joint markets, increasing peak shaving depth means TPUs can generate less electricity during low-tariff periods while increasing ramp rate means TPUs can respond faster to changes in electricity tariffs. Flexibility enhancement brings additional revenues, but also additional investments. The tradeoff between costs and revenues is important for the construction of sustainable power systems mainly based on renewables, but limited literature models them. To address this gap, variable costs, fixed costs, revenues, and an optimization model for maximum profits are proposed and modeled. Then the economic applicability of different flexibility enhancement aspects is evaluated by comparing the maximum profit increase for a day before and after the flexibility enhancement of TPUs. Furthermore, the profitability of applying different flexibility technologies under the same investments is analyzed, obtaining the optimal flexibility enhancement route. The proposed method is applied to TPUs with different live steam parameters, installed capacities, and reheat stages. Numerical simulations provide detailed economic analysis and optimal flexibility enhancement routes for TPUs in electricity-carbon joint markets.

Integrated optimization of charging infrastructure, fleet size and vehicle operation in shared autonomous electric vehicle system considering vehicle-to-grid

Shared autonomous electric vehicles (SAEVs) are predicted to become a significant solution to reduce global emissions and energy consumption resulting from urban transportation. The centralized operation of SAEVs not only allows large-scale travel demand response but also can provide essential ancillary services to the smart grid through the concept of vehicle-to-grid (V2G). With V2G technology, unused electric vehicles can work as a distributed energy storage facility for the electricity grid to smoothen the intermittent demand. Designing and operating a V2G-enabled SAEV system is challenging. This problem involves complicated planning and operational decisions, as well as time-varying electric tariffs. In this work, a flow-based Integer Linear Programming (ILP) model is formulated for determining the optimal configurations (charging infrastructure and fleet size) and daily operation strategies (serving passengers, relocation, charging/discharging). The developed mathematical model allows for maximizing the total profit, comprising investment cost, revenue from serving passengers, and V2G profit through charging/discharging schedules. A two-stage Benders decomposition-based algorithm is proposed to address the sophisticated ILP problem. Via testing instances in the Manhattan network based on real-world and synthetic data, we have demonstrated the feasibility of our approaches and studied the benefits of integrating V2G in the SAEV system.

Deployment of Lifetime Cost Analysis to Reassess Economic Current Density in the Presence of Vietnam’s Market Economy

A decent choice of conductor size for overhead power transmission lines can provide economic advantages throughout the project’s lifetime. One of the crucial criteria for sizing conductors is economic current density. In Vietnam, the electricity industry has extensively utilized the 1950s-era economic current density that ignores the value of cash flow at diverse points in time and applies the assumption of the constant electricity tariff and wire cost. These costs, nonetheless, are likely to greatly impact the figures of economic current density; as a result, these figures must be modified. This paper puts forward novel figures of economic current density that are systematically determined on the basis of lifetime cost (LTC) analysis in the presence of Vietnam’s market economy. The LTC is made up of the initial capital expenditure (ICE) and the overall operating cost (OPC), which includes the expense incurred for maintenance and electricity energy loss. An analytical formulation of ICE related to conductor size and system voltage is established based on information from recently built overhead transmission lines and a regression-based method. The electricity energy loss is computed based on equivalent hours of maximum power loss contingent upon equivalent hours of maximum power usage throughout an analytical expression obtained on the basis of the regression technique from load consumption patterns in the past. Finally, the feasibility and efficacy of the proposed economic current density values are validated by practical case studies of overhead transmission lines in Vietnam. The deployment of the suggested strategy demonstrates a decrease in lifetime cost ranging from approximately 5% to around 20% in comparison to the methodology prescribed by the existing Vietnam standard.

The economics of public transport electrification: The charging dilemma

The electrification of public transport has the potential to mitigate greenhouse gas emissions and support the electricity system, however this potential depends on how intraday charging interfaces with grid emissions and wholesale prices. Our paper explores these trade-offs by using boundary analysis to evaluate a least-cost tariff-based aggregate bus fleet charging strategy and comparing it to charging strategies that either minimise indirect greenhouse gas emissions or wholesale price exposure, based on the metropolitan Melbourne bus network. We develop a novel two-state fleet charging optimisation model, that allows us to perform a counterfactual analysis across 5 years of historical (2017–2021) and 5 years of modelled (2022–2026) changes to wholesale electricity prices and emission intensities in Australia's National Electricity Market, in response to the three charging strategies. We find that current Australian commercial tariff structures with high daytime volumetric usage and demand charges mute climate and wholesale market signals. By charging to minimise greenhouse gas emissions over bill savings, the fleet can achieve (on average) a further 33.4% reduction in indirect emissions but incurs a 103% increase in annual electricity bills. Alternatively, by charging to minimise wholesale price exposure, the fleet can achieve (on average) a 13.4% reduction in indirect emissions while incurring a 69% increase in electricity bills. Improving the temporal alignment of commercial electricity tariffs with wholesale prices should thus simultaneously reduce indirect emissions and wholesale electricity costs. This necessitates urgent tariff reforms to allow relevant wholesale price signals to pass through to commercial EV operators.