Tariff Design Research Articles

Tariff option preferences for smart and bidirectional charging: Evidence from battery electric vehicle users in Germany

Smart charging of battery electric vehicles (BEVs) can contribute to flexibility in power grids and help integrate renewable electricity. Tapping into this potential requires high user acceptance for smart charging and corresponding tariffs. In this paper, we analyze the preferences of current BEV users, representing the potential near-term adopters of smart charging, for different smart charging tariff design elements by conducting a discrete choice experiment with 689 participants in Germany. In doing so, we (1) provide an overview of current BEV users' preferences, (2) identify and characterize BEV user groups with substantial differences in their preferences, and (3) identify barriers for smart charging implementation from the perspective of current BEV users. More specifically, we find that potential cost savings along with the pricing scheme and charging mode are the most important tariff elements, whereof a pre-defined price corridor with an emergency price for grid bottlenecks and charging a safety buffer before applying smart charging are most preferred. We identify three user groups, with a large share of innovative adopters. Moreover, driving range or reluctance regarding data sharing can represent barriers for smart charging adoption. Based on our findings, we derive implications for decision-makers in policy and industry.

On grid-serving grid tariff design in Local Energy Markets

Local Energy Markets (LEMs) are virtual market places that allow for energy sharing of prosumers in proximity to each other. In LEMs, participants’ offers and bids are coordinated by a pricing mechanism, which sets price signals for generation and loads. This impacts distribution grid utilization. Dynamic grid fees could be used by the distribution system operator to add price signals for market participants that are beneficial to the distribution grid operation. To develop such a grid-serving LEM design, we propose a framework for the design of alternative grid tariffs as a trade-off between cost-reflective and cost-recovering grid tariffs. We analyse three alternative grid tariff components in the context of LEMs: time-varying energy fees, critical peak pricing (CPP) and capacity fees for power not traded on the LEM. A case study on a synthetically generated grid region shows that while the alternative tariff components can reduce the aggregated peak load of the region by up to 31% with the CPP, the fees can also lead to increased line utilization. Also, while total local added value can be increased with all alternative grid tariffs, especially capacity-based grid tariffs are connected to a strong redistribution of grid costs from flexible to inflexible prosumers.

A deep learning approach for fairness-based time of use tariff design

Time of use (TOU) tariffs aim to shape demand by reducing peak demand. This is significant given the increasing pressure on peak energy supply, particularly in Europe, where peak gas prices have dramatically increased bills. This study defines fairness as a reform measure that carries both distributional and transitional effects on different income users and utilities. A flexible fairness-driven TOU tariff design is proposed, which (1) assigns households to different tariffs by income and estimated price responsiveness calculated from a long short-term memory (LSTM)-based counterfactual electricity consumption model and (2) filters potential tariffs to simultaneously lower total costs, distribute cost savings progressively, and maintain constant utility profitability. Using the 2009-10 Irish Smart Metering Trial based on more than 2000 users, under the proposed frameworks (b) and (c) with fairness constraints, no lower-income users have been observed to pay a higher bill than the higher-income users on average. Specifically, lower-income groups can achieve 8% and 10% bill reduction, respectively, under framework (b) partial bill protection and framework (c) full bill protection for the lower-income users. Our fairness-based TOU framework has successfully achieved fairness of distribution and transition, paving the way for a more equitable and sustainable electricity system.

Second-best electricity pricing in France: Effectiveness of existing rates in evolving power markets

The adoption of time-varying pricing in the French electricity market has been historically low despite their availability to consumers well before the deployment of smart metering. However, as the share of variable renewable electricity increases and carbon prices grow, the demand-side response will become increasingly important to achieve efficiency gains. Relying on the historical hourly consumption of French electricity consumers and multiple prospective weather years, we study the gain allowed by the broader adoption of time-varying electricity retail rates in low-carbon power systems. We develop a four-stage methodology to assess the efficiency and stability of the electricity supply cost component of the electricity bill in France for historical and prospective years. Our analysis demonstrates that peak pricing schemes have increasing interest in the context of further deployment of renewable capacity, capturing 25% to 50% of the welfare gain reached by real-time pricing schemes. The corresponding deadweight loss of peak pricing schemes represents 0.4 to 1 bn EUR per year in 2030 compared to real-time prices (RTP) schemes. Conversely, deadweight loss of current time-of-use (ToU) rates represents 1 to 1.2 bn EUR per year in 2025 and increases over time. The current design of TOU rates does not provide adequate incentives in future power systems. Besides the tariff efficiency, the results underlined an increasing price difference between on-peak and off-peak rates. This questions the social acceptance of time-varying retail rates for consumers unable to hedge against peak prices. By analysing the effectiveness and limitations of time-varying rates, our findings underline the importance and value of price-based DR in future power systems in general. Our results highlight the need to revise the end-user tariff design in the French power system, notably with regard to the conveyed price signals.

Strategic Behavior of Competitive Local Citizen Energy Communities in Liberalized Electricity Markets

The liberalization of energy retail markets empowered consumers with the ability to be part of new emerging entities, such as Citizen Energy Communities. With the increasing penetration of decentralized variable generation, communities have the advantage of incentive local carbon neutrality and sustainability. Local generation reduces transport grid usage and costs to consumers. Furthermore, worldwide legislation incentives energy communities by providing them discounts to other fee parts of the tariff apart from wholesale prices. This paper presents a model of strategic behavior, investment, and trading of energy communities. The model comprises the investment in local renewable generation, the design of competitive tariffs, and strategic bidding on wholesale markets. Consumers have an optimization model that selects the retail tariff that minimizes their costs with energy. These models are tested using data from Portuguese consumers and the Iberian electricity market. Results from the study indicate that inflexible consumers may reduce their costs by 29% by being part of the community. Furthermore, they have the potential to reduce their costs above 50% when using demand–response, adapting themselves to local production and wholesale prices.

Impact of tariff structures on energy community and grid operational parameters

This paper presents a mixed-integer linear programming optimization model of a renewable energy community comprised of members with local generators, battery energy storage systems, electric vehicles, and heat pumps and thermal energy storage, thus representing a local multi-energy system. The goal of the paper is to analyze the impact of different tariff structures for incentivizing energy sharing within energy communities on the distribution grid operational parameters. To achieve this, a comprehensive analysis of a hypothetical renewable energy community is conducted using the presented optimization model. Various tariff structures are evaluated, including flat and dynamic tariffs, reflecting the day-ahead market prices for energy and supply, as well as various network tariff designs, such as regulated charge reduction, time-of-use network tariffs and capacity payments. The results indicate that sharing energy within communities reduces energy losses in the grid, peak loads and reverse power flows and results in a smoother voltage profile. It is also found that tariffs that include dynamic electricity and supply costs can further increase these benefits, while including capacity payments as an incentive mechanism yields most favourable results for all analyzed criteria.

Automated tariff design for energy supply–demand matching based on Bayesian optimization: Technical framework and policy implications

With the emergence of renewable energy sources, designing tariffs becomes crucial to match unstable energy supply with varying energy demand. However, traditional tariff design frameworks depend on a certain type of tariff and the demand response of customers, which makes designing tariffs rigid and hard to automate. To overcome the challenges, in this paper, we propose an automated tariff design framework for energy supply–demand matching based on Bayesian optimization. The proposed framework allows us to design an optimized tariff for any given objective of energy matching automatically. This automation can provide flexibility in designing tariffs for energy management. Moreover, we propose a novel matching evaluation metric focusing on the mismatch of actual energy supply–demand patterns related to the stability and reliability of energy management systems. Adopting it along with traditional economic utility, actual energy pattern matching and economic values can be deliberately balanced in the automated tariff design. Through simulations with real datasets, we demonstrate that the proposed framework can effectively design tariffs in the real world such as time-of-use and demand charge tariffs, considering energy patterns and economic costs separately. Finally, we provide potential policy implications resulting from the automated tariff design.

Distribution network tariff design: Facilitate flexible resource under uncertain future energy scenarios

The uncertainty embedded in low-carbon transition pathways and future energy scenarios will reshape the way of distribution network planning and operation. Flexible solutions provided by flexible resources could help distribution network operators (DNOs) to reduce network costs and risks under uncertainties. However, the current tariff paradigm neglects such contributions, which cannot generate sufficient incentives to facilitate flexible resource development. To tackle this issue, this paper first introduces an improved price control method that caps DNOs' revenue while encouraging DNOs' sustainable investment under uncertainties. A bi-level planning model is then proposed to determine the optimal investment plan and evaluate the risk reduction effect by scheduling flexible loads. Finally, a novel pricing method is proposed to derive network tariffs for existing and potential flexible loads based on their contributions to risk reduction and demonstrated in a test system. The result shows a 24% network cost reduction given by 6% DNO risk compensation rate, and 0%–43% and 1%–22% network tariff reductions for existing and future flexible demands, respectively. This evidence proves the proposed method can incentivize DNOs to well manage risks, meanwhile promote the connection and engagement of flexible loads. Regulators and DNOs are recommended to review the existing pricing framework, enhance risk management in price regulation, and explore suitable tariff designs or business models for flexible loads to cope with future uncertainties through flexibility.

Design and Impact of Grid Tariffs

We propose a novel grid tariff design proportional to grid load and analyze its performance in comparison to other grid tariff designs with regards to (i) effectiveness, (ii) efficiency, (iii) profitability of technologies and (iv) equity. In the case of a large share of automated loads, time-of-use tariffs and critical peak prices create problematic new rebound peaks. Direct load control and capacity prices can reduce grid load without rebound peaks but are less effective at reducing both grid and energy costs. The novel tariff design proportional to the grid load can reduce both grid and energy costs but needs to be designed appropriately to avoid rebound peaks. Tariff impacts on the profitability of different technologies are more pronounced than equity impacts because households from all income brackets may be equipped with PV and flexible technologies.

The Impacts of Retail Tariff Design on Electric Vehicle Charging for Commercial Customers

The Impacts of Retail Tariff Design on Electric Vehicle Charging for Commercial Customers

Price-Based Demand Response: A Three-Stage Monthly Time-of-Use Tariff Optimization Model

In this research, we developed a three-stage monthly time-of-use (TOU) tariff optimization model to address the concerns of confusing time period division, illogical price setting, and incomplete seasonal element consideration in the previous TOU tariff design. The empirical investigation was conducted based on load, power generation, and electricity pricing data from a typical northwest region in China in 2022. The findings indicate the following: (1) In producing the typical net load curves, the employed K-means++ technique outperformed the standard models in terms of the clustering effect by 4.27–26.70%. (2) Following optimization, there was a decrease of 1900 MW in the maximum monthly abandonment of renewable energy, a decrease of 0.31–53.94% in the peak–valley difference, and a decrease of 2.03–17.27% in the monthly net load cost. (3) By taking extra critical peak and deep valley time periods into account, the average net load cost decreased by 10.36% compared with conventional peak–flat–valley time period division criteria.

Network tariff design with flexible customers: Ex-post pricing and a local network capacity market for customer response coordination

This paper assesses the performance of differently implemented forward-looking network tariff designs in a future with many flexible customers. Theoretically, forward-looking approaches provide more cost-reflective signals than historical accounting approaches; they reveal to customers the trade-off between consuming more flexibly and long-term network expansion costs. We test forward-looking network tariff designs with higher locational differentiation, potentially leading to increased cost-reflectivity. However, we observe that if large shares of customers synchronize their responses to ex-ante-determined highly time-varying and locational-specific network charges, the peak-shifting effect materializes. Consequently, reinforcements occur earlier than envisioned. This scenario is plausible in an electrified and automatized future. Regulators should acknowledge this potential issue and consider to what extent respecting the predictability principle can offset economic efficiency in such a context. Ex-post pricing aligns network charges with long-term incremental network costs. But ex-post pricing has downsides, such as lower predictability for customers. To increase predictability, we propose and test an innovative coordination mechanism in a case study. The coordination mechanism is a local network capacity market where customers book their expected network capacity usage in a competitive framework. Retailers/aggregators are foreseen as key actors translating complex tariffs into diverse products according to customers’ flexibility and risk aversion.

Analysis of Tariffs and the Impact on Voltage Variations in Low-Voltage Grids with Smart Charging and Renewable Energy

The rapid increase in electric vehicles (EVs) and installed photovoltaic systems (PV) has resulted in new challenges for electric systems, e.g., voltage variations in low-voltage grids. Grid owners cannot directly control the power consumption of the end consumers. However, by the design of transparent tariffs, economic incentives are introduced for the end consumers to adjust their EV charging patterns. In this work, the main objective is to design a time-of-use pricing tariff to reduce the voltage variations in a low-voltage grid when introducing PVs and EVs with smart charging. Data from an existing low-voltage grid and hourly data from household power consumption, together with models of PV and EV charging, are used to simulate the voltage fluctuations based on the modified electric consumption. The results show that a time-of-use pricing tariff taking into consideration maximum peak power is important to reduce grid voltage variations. Another observation is that the use of economic incentives, such as subsidies when selling power from the household, combined with V2G technology can be economical for households but increases the voltage variations in the grid.

Bi-level goal programming model for simultaneous microgrid investment and tariff design: A case study in the Volta Region of Ghana

The use of microgrids for remote rural electrification in developing countries requires solutions to technical and financial constraints involving variability in renewable generation and electricity demand, budget limitations, and underlying electricity tariffs. While previous research has evaluated a subset of these problems, to the best of the authors’ knowledge, no attempt has been made to design an optimal tariff structure that promotes an isolated microgrid’s business case for rural electrification. This work presents a new goal-oriented bilevel optimization model capable of simultaneously optimizing the microgrids tariff design–to offer full revenue recovery for the investor while also ensuring equitable electricity prices for low-income households–as well as optimizing the physical microgrid design. A case study of an off-grid island on Ghana’s Volta Lake demonstrates the efficiency of the new tariff design over the existing policy. For a remote rural village, a 6kW solar PV, 5kW wind and 11kW diesel microgrid provide the highest lifecycle electricity supply adequacy at the lowest investment cost. Through scenario analysis, we demonstrate the implementation of an optimal tariff design capable of offsetting a 39 % revenue shortfall compared to an existing tariff structure while ensuring equitable electricity prices for low-income households.

Future research of supply chain resilience: Network perspectives and incorporation of more stakeholders

In recent years, global supply chains face great challenges driven by events such as trade wars, the Covid-19 pandemic, and the Russia-Ukraine War. Since the disruptions caused by such shocks are more systematic and the duration is harder to predict compared to natural disasters or other operational risks, we highlight two features for future supply chain research: network perspectives and incorporation of more stakeholders such as governments that can influence overarching performances of industrial and supply chains. From the network perspective, we review the literature on vulnerable nodes & links identification, risk propagation modeling, network resilience measures, and disruption mitigation strategies. We suggest promising research directions such as deriving non-probabilistic resilience measures that can incorporate dynamics, extending existing methods for bottleneck detection to the network setting, balancing efficiency and resilience in a more sophisticated way, etc. In terms of incorporating more stakeholders’ roles in supply chain network decisions, we review the relevant literature and discuss research opportunities on governments’ roles in global supply chains (i.e., an external circulation perspective) and in incentivizing firms’ R&D and stimulating domestic demand (i.e., an internal circulation perspective). We suggest topics including global supply chain resilience evaluation using trade networks, technology networks, and firm ownership networks, the impact of trade policies with network perspectives, design of tariff and non-tariff measures under trade agreements, domestic industry relocation, evaluating the effects of government subsidies on firms’ long-term innovation, and design of subsidy strategies for supply chain stability and resilience. We believe research on supply chain resilience will thrive in the years ahead, better supporting firms’ and governments’ decisions.

Grid tariff designs to address electrification and their allocative impacts

This paper investigates volumetric grid tariff designs considering different pricing mechanisms and resulting in cost-allocative impacts across socio-techno-economic consumer categories. In a case study of 1.56 million Danish households divided into 90 socio-techno-economic categories, we compare three alternative grid tariffs and investigate their impact on annual electricity bills. The results of our design, consisting of a time-dependent threshold penalizing individual peak consumption and a system peak tariff, show (a) a range of different allocations that distribute the burden of additional grid costs across both technologies and (b) strong positive outcomes, including reduced expenses for lower-income groups and smaller households.

Contracts-for-Difference: An assessment of social equity considerations in the renewable energy transition

This research advances knowledge regarding social equity as it relates to electricity network charges, renewable investments, and household income. While much research has examined social equity issues related to electricity access, research has yet to fully explore how different network tariff designs used to recover the cost of renewable energy investments, such as those related to ‘Contracts-for-Difference’ (CfDs), impact low-income individuals. We accordingly examine CfDs in more detail, assessing if regressive effects emerge from levying CfD cost recovery via network charges. By analysing energy use and network design charges in Australia, we find that CfDs are a regressive form of taxation used by state governments to fund renewable energy commitments. We illustrate the impact that CfDs have on different energy users and provide recommendations to reform renewable energy policies to provide greater social equity.

SELECTION OF ELECTRICITY TARIFF DESIGNS FOR DISTRIBUTION NETWORKS USING ANALYTIC NETWORK PROCESS

In electricity distribution networks, tariff designs set the interface between the users and network operators or service providers. Tariff designs provide the reference base for tariff schedules covering multiple categories of network users. It is widely accepted that electric utility rate designs have subjective and objective multi-criteria dependencies. Based on utility rate making literature, subcriteria for selection of tariff designs can be listed under the economic, technological, and social criteria. In this study, two widely used electricity tariff designs, volumetric or energy charges and capacity or demand charges are considered. In addition, real-time pricing and a hybrid tariff design that combines energy and capacity charges with critical peak pricing are also included in the comparison. Performance evaluation of these tariff designs on quantifiable parameters relating to economic aspects is carried out using the Tariff Design and Analysis Tool. Literature on electricity tariff designs and pricing provides the metadata on performance relating to qualitative criteria covering mainly the technological and social aspects. A synthesis of the quantitative and qualitative criteria evaluations was done by developing a Benefits-Opportunities-Costs-Risks (BOCR) model in the Analytic Network Process (ANP), a Multi-Criteria Decision Making methodology. A quantitative assessment of inconsistencies in evaluation and synthesis of the model using a consistency index shows that the developed ANP framework for tariff design selection is a valid approach. The developed BOCR model in ANP shows that the hybrid tariff design of Energy and Capacity charges with Coincident Peak Pricing is the best alternative. A sensitivity analysis shows that the ranking is variable when the BOCR priorities change.

Optimal Retail Tariff Design With Prosumers: Pursuing Equity at the Expenses of Economic Efficiencies?

Optimal Retail Tariff Design With Prosumers: Pursuing Equity at the Expenses of Economic Efficiencies?

Forward-looking dynamic network charges for real-world electricity systems: A Slovenian case study

Electricity network charges are intended to recover network costs and adhere to economic efficiency and equity principles. Most network charges currently applied in real-world systems are merely focused on cost recovery, implicitly assuming inelastic customers. Although proposals for improved network tariff designs can be found in the literature, they are tested only for simplified small feeders. This paper reformulates a dynamic network tariff to implement it in a real-world electricity system. By adapting the proposed improved network tariff designs to manage large-scale layered networks and complex data sets, we address this gap in the literature. First, when considering the entire network, consumers and generators need to be clustered into subsystems by voltage levels, enabling to calculate the network utilization levels; this is the so-called cascade model. After, per voltage level, the network tariff needs to be computed. We focus on an advanced network tariff design that consists of forward-looking peak-coincident energy charges, which is symmetric for injections and withdrawals, a per-kWh component for energy losses, and fixed residual network charges. We illustrate that this network tariff incentivizes the shifting of flexible loads to off-peak hours and aligns individual customer incentives with expected system benefits, reducing future network investments. In addition, the symmetric nature of the proposed tariff enables a level playing field for distributed resources providing flexibility services. As demonstrated for Slovenia, the proposed formulation should be considered by regulators for implementation in real-world electricity systems.