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The success of the energy transition relies on effectively utilizing flexibility in the power system. Dynamic tariffs are a highly discussed and promising innovation for incentivizing the use of residential flexibility. However, their full potential can only be realized if households achieve significant benefits. This paper specifically addresses this topic. We examine the leverage of household flexibility and the financial benefits of using dynamic tariffs, considering household heterogeneity, the costs of home energy management systems and smart meters, the impact of higher electricity prices and price spreads and the differences between types of prosumers. To comprehensively address this topic, we use the EVaTar-building model, a simulation framework that includes embedded optimization designed to simulate household electricity consumption patterns under the influence of a home energy management system or in response to dynamic tariffs. The study's main finding is that households can achieve significant cost savings and increase flexibility utilization by using a home energy management system and dynamic electricity tariffs, provided that electricity prices and price spreads reach higher levels. When comparing price levels in a low and high electricity price scenario, with an increase of the average electricity price by 15.2 €ct/kWh (67 % higher than the average for the year 2019) and an increase of the price spread by 8.9 €ct/kWh (494 % higher), the percentage of households achieving cost savings increases from 3.9 % to 62.5 %. Households with both an electric vehicle and a heat pump observed the highest cost benefits. Sufficiently high price incentives or sufficiently low costs for home energy management systems and metering point operation are required to enable households to mitigate rising electricity costs and ensure residential flexibility for the energy system through electric vehicles and heat pumps.

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the ongoing expansion of renewable energies and the increasing energy demand of household consumers, e.g., due to the electrification of the mobility and heating sectors, grid bottlenecks and voltage band violations in low-voltage grids in Germany are expected to occur more frequently in the future. Dynamic pricing, implemented through digitization, is seen as one option to incentivize flexible and responsive electricity use of household consumers. Flexible consumption of household consumers could make grid utilization more efficient. So far, most studies on the effects of dynamic pricing focus on only one tariff for all household consumers within one low-voltage grid, whereas, in reality, households are free to choose between many different static and dynamic electricity tariffs. As previous studies have shown, the choice of tariff plays a major role in how flexibility is used. To address this research gap, we developed a model which integrates households and their static and flexible consumption and generation units into a home energy management system (HEMS) and minimizes the households’ power purchase costs. The model considers a range of dynamic tariffs that are already available today and includes the decision-making of heterogeneous household customers regarding the choice of tariff. Load flow calculations are then used to determine and analyze the effects on a low-voltage grid area. With this methodology including the free choice of tariff we go beyond the state of the art and paint a more realistic picture of the effects of dynamic tariffs on low-voltage grids. Our results show that, even though dynamic tariffs lead to increased peak demand at the level of individual households, peak loads are spread more widely within a grid area as the result of households choosing different tariffs based on economic considerations. Due to these effects, dynamic pricing has positive effects on grid utilization and could reduce the need for grid expansion.

Demand side management focuses on flattening the demand profile, reducing network losses, costly investments in network infrastructure and generation capacity. The supplier pursues a profit for the service it provides. In addition, in demand-side management, the supplier modifies its pricing scheme in order to charge a fair price to each user. In this sense, the price is expected to increase during peak demand. For this reason, the supplier needs to know his supply cost to propose a reasonable price scheme. However, there has been scarce interest in finding a supply cost function that enables to develop of a real and scalable model of the supplier-user interaction. In the literature, a bi-level optimization has been proposed to model the supplier-user interaction and some supply cost functions were proposed without any analysis. In this paper, a new supply cost is proposed, and it is compared with the supply cost functions found in the literature analyzed. The proposed supply cost shows good performance, and it can be scalable. [ABSTRACT FROM AUTHOR]

Dynamic energy tariffs facilitate engaging domestic consumers on demand management, contributing to grid’s stability, but requires of informed decision enabling tools. This paper presents a domestic heating costs calculation method for different heating technologies (gas boiler, heat-pumps) and a range of energy tariffs. Based on physical modeling, effect of outdoor temperature in the COP of heat-pumps is assessed. The methodology is applied to the 2018/19 heating season in Madrid (Spain), calculating the heating costs under four diverse energy tariffs (static gas tariff, static electricity tariff, real-time-price electricity tariff, dynamic time-of-use electricity tariff) for a typical home demand. The hourly results for two representative days are detailed, along with the aggregated results for the whole season. Along the season, the continuous changes in energy wholesale market prices and weather conditions make one heating technology and/or tariff more convenient each time. For the whole season, the dynamic time-of-use tariff considered would imply heating costs up to 40% lower than the static gas tariff. The results are strongly conditioned by climate conditions and national energy market evolutions. Day-ahead information on the actual heating costs might lead to domestic end-users to adapt their behavior and consumption patterns for more cost-effective use of the energy.

Dynamic tariffs are expected to be implemented as commercial offers of electricity retailers in smart grids, conveying price signals aimed at shaping usage patterns, with potential benefits to enhance grid efficiency and reduce end‐users' costs. Retailers and consumers have divergent goals. While the objective of the retailer is to maximize profits, the purpose of consumers is to minimize their electricity bill. The interaction between the retailer and consumers can be modeled by means of bi‐level (BL) programming: the retailer sets the prices to be charged to consumers and these react by scheduling flexible appliances according to those prices and their comfort requirements. In this work, two hybrid BL optimization approaches are proposed to solve this problem, considering one leader (retailer) and multiple followers (consumers). Two population‐based approaches were developed, a genetic algorithm (GA) and a particle swarm optimization (PSO) algorithm, to deal with the upper level problem, both encompassing an exact mixed‐integer linear programming solver to address the lower level optimization problem. Different scenarios were generated, comprising one leader (retailer imposing different price schemes) and three followers (with different consumer profiles). Typical residential appliances were considered, with different operation cycles. Also, diverse tariff structures set by the retailer were analyzed. The performance of the two algorithms was compared. Results revealed a consistent superiority of PSO over GA. [ABSTRACT FROM AUTHOR]

The energy transition requires large investments. The public debate in the Netherlands focuses on the associated costs, and the government uses traditional instruments such as subsidies. From the perspective of behavioral economics this approach has a few drawbacks. The goal of this article is to discuss behavioral-economic pitfalls which are important for two topics regarding the energy transition. First, households need to take measures themselves with regard to the switch to alternative energy sources. Several behavior-economic pitfalls influence households' decisions. The most important ones are risk aversion, loss aversion, choice overload and motivation crowding out. A number of measures are discussed which the government might employ to facilitate this behavioral change by households. Second, large investments will be needed in the electricity network due to the energy transition. These costs might be mitigated by a behavioral change in energy use of households and firms: either by using less energy or by using energy at other times of the day. This might be achieved by introducing dynamic electricity tariffs. However, behavioral-economic pitfalls will affect the interest in such tariffs, most notably motivation crowding out and moral licensing. Their effects will be lessened if financial incentives are not emphasized. [ABSTRACT FROM AUTHOR]

Last-mile delivery is regarded as an essential, yet challenging problem in city logistics. One of the most common initiatives, implemented to streamline and support last-mile activities, are satellite depots. These intermediate logistics facilities are used by companies in urban areas to decouple last-mile activities from the rest of the distribution chain. Establishing a business model that considers different stakeholders' interests and balances the economic and operational dimensions, is still a challenge. In this paper, we introduce a novel problem that broadly covers such a setting, where the delivery to customers is managed through satellite depots. The interplay and the hierarchical relation between the problem agents are modeled in a bi-level framework. Two mathematical models and an exact solution approach, properly customized for our problem, are presented. To assess the validity of the proposed formulations and the efficiency of the solution approach, we conduct an extensive set of computational experiments on benchmark instances. In addition, we present managerial insights for a case study on parcel delivery in Turin, Italy. • We study an integrated bi-level approach for setting the tariffs in a collaborative last-mile logistic problem. • We propose two mathematical models and an exact solution approach. • We present an extensive set of computational experiments to assess the performance of the exact method. • We discuss insights from a real case study on the medium-sized metropolitan area of Turin. [ABSTRACT FROM AUTHOR]

Demand-side management is one of the most appropriate mechanisms for improving energy efficiency and managing consumption in the presence of distributed, intermittent, and irregular electricity generation. The European Commission has mandated Member States to install smart metres in the homes of at least 80% of consumers by 2020, which will enable the technical implementation of demand-side management programmes and real-time consumer response. However, despite the technical feasibility and the existing regulatory framework, the benefits of implementing demand reduction programmes need to be understood and ultimately accepted by end-users. The results of the analysis show that Slovenian consumers are willing to participate in demand response programmes, but expect significant savings in return. By introducing financial incentives, electric utilities could provide consumers with greater incentives to purchase smart appliances, which would contribute significantly to faster and more successful adoption of demand-side management programmes.

Copyright of Scientific Papers of Electrical & Control Engineering Faculty Technical University of Gdansk / Zeszyty Naukowe Wydzialu Elektrotechniki & Automatyki Politechniki Gdanskiej is the property of Gdansk University of Technology, Faculty of Electrical & Control Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Increasing peaks from high-power loads such as EVs and heat pumps lead to congestion of electric distribution grids. The inherent flexibility of these loads could be used to resolve congestion events. Possible options for this are smart network tariffs, market-based approaches, and direct control of flexible loads by the network operator. In most instances, these approaches are looked at in isolation, without considering potential connections and trade-offs between them. In this contribution, we aim to bridge this gap by presenting an overarching design framework for congestion management mechanisms. We classify proposals based on design choices and qualitatively discuss their benefits and risks based on an extensive literature analysis. As there is no one-size-fits-all solution, we map possible risks and discuss the pros and cons of different mechanisms for various problem types. We caution against using market-based mechanisms for local congestion, as they can be susceptible to undesired strategic behavior of market actors. • Congestion problems occur with differing spatial and temporal characteristics. • Congestion can be managed by network tariffs or specialized solutions. • The design choices of methods should be fitted to problem characteristics. • Market-based proposals can be problematic due to undesired strategic behavior. [ABSTRACT FROM AUTHOR]

We analyze a model in which a government uses a second best policy to affect the reallocation of labor, following a change in relative prices. We consider two extreme cases, in which the government has either unlimited or negligible ability to commit to future actions. We explain why the ability to make commitments may be unimportant, and we illustrate this conjecture with numerical examples. For either assumption about commitment ability, the equilibrium policy involves gradual liberalization. The dying sector is protected during the transition to a free market, in order to decrease the amount of unemployment. Our results are sensitive to the assumptions about migration.

One of the important goals of a smart city is to increase the quality of urban mobility. This paper will explore the use of dynamic tariffs for this purpose. Transactive control, the concept of feedback through economic transactions, is a promising concept for accomplishing such dynamic tariffs, and is the focus of this paper. Two specific examples of transactive control are considered in this paper, the first of which is the synthesis of dynamic toll prices with the goal of reducing traffic congestion in highways. We examine how a model-based approach can result in optimal toll pricing schemes. Sociotechnical models that combine behavioral models of drivers and traffic flow models, together with real-time traffic information obtained from on-road sensors are used to determine the transactive control strategies. The overall pricing strategy is evaluated using real traffic data from an existing dynamic toll-pricing framework. The second example of transactive control is in the context of Mobility on Demand (MoD), where new modes of transportation other than private and public are being proposed, providing a smorgasbord of options for passengers. We investigate a dynamic routing concept for multipassenger transport, and propose a transactive control strategy to regulate the achievable performance around desired values. Numerical simulations using actual passenger data are carried out to demonstrate the advantages of the proposed concept. [ABSTRACT FROM PUBLISHER]

Statistics from the United States (US) Energy Information Administration and the US Census Bureau reveal that a growing number of households are threatened by energy poverty. Both argue that energy poverty is becoming more common than traditional poverty. Recently, dynamic pricing programmes have been made possible by the increasing use of smart meters and other devices which enable consumers to play a more active role in electricity systems. This study aims to determine if dynamic pricing programmes can reduce the energy burden on households and diminish the number suffering from energy poverty. Data from the Annual Electric Power Industry Report and the American Community Survey were merged and analysed by state and year to assess the impact of dynamic pricing on energy poverty for 51 US states from 2013 until 2021. The results show that time-of-use and critical-peak-pricing tariffs can reduce the number of households suffering from energy poverty. Conversely, CPP and RTP increase the number of households in energy poverty. These findings indicate that policymakers and retailers need to develop pricing programmes that will encourage consumers to modify their consumption habits and enhance the integration of intermittent renewable energy sources and reduce energy poverty. • This research extensively studies the economic and social impacts of dynamic pricing. • This is the first research analsying the impact of dynamic tariffs on energy poverty. • US states were evaluated from 2013 to 2021 using dynamic panel estimators. • Dynamic tariffs, except for the CPP, did not reduce energy poverty as expected. • ToU tariffs contribute to reducing the number of households in energy poverty. [ABSTRACT FROM AUTHOR]

Understanding consumer behaviours is important in designing dynamic tariffs, which are usually considered the first-best solution when the conventional flat tariff does not reflect the varying cost of electricity generation. I estimate households' own- and cross-price elasticities using dataset from a smart metering project, and investigate which household specific characteristics determine the impact of peak prices on electricity consumption. I find peak prices (17:00–20:00) reduce peak and post-peak consumption (20:00–23:00), indicating a spillover effect of peak prices. The underlying mechanisms that could be generating the spillover effect have been further discussed and investigated. Finally, I estimate dynamic tariffs' distributional and welfare effects, and demonstrate that the spillover effect is crucial in determining the cost effectiveness of a smart metering programme. [ABSTRACT FROM AUTHOR]

To efficiently facilitate the energy transition it is essential to evaluate the potential of demand response in practice. Based on the results of a Dutch smart grid pilot, this paper assesses the potential of both manual and semi-automated demand response in residential areas. To stimulate demand response, a dynamic tariff and smart appliances were used. The participating households were informed about the tariff day-ahead through a home energy management system, connected to a display installed on the wall in their living room. The tariff was intuitively displayed: self-consumption of photovoltaic generation was stimulated by means of a low tariff, but also the generation itself played a central role on the display. Household flexibility is analyzed, focusing on: (i) the load shift of (smart) appliances, and (ii) the response of the (overall) peak load towards the dynamic tariff. To assess the latter, i.e. price responsiveness, the participants were split up in two comparable groups which were subject to a different moment of evening peak-pricing. Based on the results, it is concluded that mainly the flexibility of the white goods (i.e. the washing machine, tumble dryer and dishwasher) is used for demand response. The main part of the flexible load of these (smart) appliances is shifted from the evening to the midday, to match local generation. This load shift remained stable over a long period of time (>1 year) and is not responsive to the exact moment of peak-pricing. Therefore, it is concluded that a simple and transparent design for dynamic tariffs is sufficient and most effective to stimulate (manual) residential demand response. Such a tariff should emphasize the ‘right’ moments to use electricity, intuitively linked to renewable generation. [ABSTRACT FROM AUTHOR]

Due to the ongoing expansion of renewable energies and the increasing energy demand of household consumers, e.g., due to the electrification of the mobility and heating sectors, grid bottlenecks and voltage band violations in low-voltage grids in Germany are expected to occur more frequently in the future. Dynamic pricing, implemented through digitization, is seen as one option to incentivize flexible and responsive electricity use of household consumers. Flexible consumption of household consumers could make grid utilization more efficient. So far, most studies on the effects of dynamic pricing focus on only one tariff for all household consumers within one low-voltage grid, whereas, in reality, households are free to choose between many different static and dynamic electricity tariffs. As previous studies have shown, the choice of tariff plays a major role in how flexibility is used. To address this research gap, we developed a model which integrates households and their static and flexible consumption and generation units into a home energy management system (HEMS) and minimizes the households' power purchase costs. The model considers a range of dynamic tariffs that are already available today and includes the decision-making of heterogeneous household customers regarding the choice of tariff. Load flow calculations are then used to determine and analyze the effects on a low-voltage grid area. With this methodology including the free choice of tariff we go beyond the state of the art and paint a more realistic picture of the effects of dynamic tariffs on low-voltage grids. Our results show that, even though dynamic tariffs lead to increased peak demand at the level of individual households, peak loads are spread more widely within a grid area as the result of households choosing different tariffs based on economic considerations. Due to these effects, dynamic pricing has positive effects on grid utilization and could reduce the need for grid expansion. • Simultaneous availability of several electricity tariffs within a grid area is considered. • We select dynamic tariffs already available today for our analysis. • We model decision-making behavior of household customers regarding choice of tariff. • We analyze effects of dynamic tariffs on a LV grid under free choice of tariff. • Free choice of tariff leads to lower thermal load and lower voltage band deviations. [ABSTRACT FROM AUTHOR]