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Existing studies have shown that climate change has important implications for residential electricity consumption, yet how responses to climate vary between rural and urban residents, and more importantly, the roles of electricity pricing regimes in determining such responses remain largely unknown. In this paper, we explore these issues using monthly data in Anhui province in China. Our results suggest that on average rural residents are more sensitive to cooling degree days (CDD) than urban counterparts (0.19% vs 0.08% increase in electricity consumption per unit increase in CDD). Additionally, households who adopt the time of use (TOU) pricing regime tend to be less responsive to temperatures than households choosing tiered pricing regimes (TPHE). Substantial increases in electricity demand induced by climate change are expected in the future. With the pessimistic RCP8.5 scenario, our results suggest an increase of 35.5% and 77.1% in electricity demand respectively for the urban and rural residents in the 2080s relative to 2017.

Decarbonizing the building and transport sectors requires electricity pricing designs that can effectively support the adoption of heat pumps (HPs) and electric vehicles (EVs). To inform electricity pricing design, techno-economic optimization modeling can help identify which pricing structures most effectively support the economic viability of electrification across different building characteristics and modeled EV plug-in behavior over the long term. In this paper, we explore the interplay between EV plug-in behavior, building energy system investment decisions, and electricity pricing design, testing electricity tariffs with varying energy and grid components. Using a comprehensive techno-economic optimization framework, we analyze how various combinations of energy and grid charges impact HP adoption across six diverse Swiss residential buildings from 2025 to 2050. We incorporate three distinct EV plug-in behavior scenarios modeled from real-world travel data. Our findings reveal that time-of-use energy charges consistently lead to the highest HP adoption rates across all building types, outperforming both flat and hourly energy pricing structures. Among grid charges, increasing block charges generally support heating electrification more effectively than peak or volumetric charges. Building characteristics substantially affect HP adoption, while EV plug-in behavior has more impact on the choice of charging infrastructure. Overall, our results show the importance of coordinated electricity tariff designs that account for the diversity of building characteristics and user behaviors to enable cost-effective electrification.

With the acceleration of the construction of a unified electricity market in China, there is an urgent need to improve the existing transmission pricing mechanism to meet the trading needs of a multi-level unified electricity market. The existing transmission pricing methods have shortcomings, such as affecting the efficiency of resource optimization and reducing the stability of the full recovery cost of the power grid. This article proposes a transmission product system and pricing mechanism suitable for multi-level electricity markets, defines standardized transmission service products, and converts post-electricity prices into pre-capacity prices, effectively solving the above problems. The effectiveness of the proposed mechanism is verified through a simplified East China zonal power grid model example.

For the first time in the history of the power industry, because of smart meters, we now have the technology deployed to charge real-time prices for electricity to large populations of consumers. At the same time, homes are becoming smarter with appliances that can autonomously respond to their environment and optimize their performance. Imagining a world in which smart appliances anticipate future changes in electricity prices, in “Dynamic Electricity Pricing to Smart Homes” Adelman and Uçkun develop an approach to evaluating market price–load equilibria when such smart appliances saturate a power utility’s service region on a large scale. Their findings indicate substantial improvements in social welfare as compared with the current status quo of flat prices or popular peak-pricing policies. With the rapid growth in residential smart meters across the United States in recent years, most homes in the United States will soon be capable of moving to time-varying prices for electricity. We develop a methodology for studying the welfare impacts of different pricing strategies on an electricity market when homes deploy smart, price-responsive appliances with forward-looking capabilities. Without assuming any functional form for dynamic prices, we show conditions under which asymptotically, as the number of homes increases, social welfare–maximizing price schedules in equilibrium are linear in load, are the same for all homes, and incrementally equal expected marginal supply costs over equilibrium loads. We provide an algorithm to compute equilibria for a large population. Using real-world data to calibrate a smart thermostat model, we compare this dynamic pricing strategy against flat and peak pricing strategies when smart thermostats are deployed across ComEd’s service region of approximately 3.5 million residential homes. We show that dynamic pricing in equilibrium dominates these competing pricing strategies and measure the expected improvements as smart thermostats are increasingly deployed. As compared against the current status quo of relatively few smart thermostats and flat pricing, we reduce adopters’ monthly power bills and generation costs from air conditioning loads by 41% and 35%, respectively, while simultaneously increasing social welfare and consumer surplus. Despite these benefits, supplier surplus from adopters decreases by half.

In this study, we propose a modified model predictive control (MPC) strategy for managing the thermal load in buildings, aimed at creating a fine-tuned balance between indoor thermal comfort and electricity cost reduction. Here, the multi-zone building’s state-space model is employed to dynamically manage energy consumption while preserving occupant comfort. The key contributions of this work include the development of a novel economic MPC strategy tailored for multi-zone heating, ventilation, and air conditioning (HVAC) systems, integrating thermal energy storage to optimise energy usage and occupant comfort. Additionally, we introduce an enhanced multi-objective optimisation framework that transforms the conflicting objectives of energy efficiency and occupant comfort into a single-objective problem for improved computational efficiency. The control strategy also incorporates dynamic electricity pricing, enabling cost-effective operation by shifting energy consumption to lower-cost periods. The proposed control method reduces fluctuations in indoor air temperature, extending the operational life of HVAC system actuators. Beyond reducing costs and consumption, this approach alleviates energy production strain and peak demand on the smart grid. The optimisation process incorporates user-defined temperature preferences for each zone, ensuring tailored comfort conditions. Simulation results show that this method maintains indoor air temperature within the desired comfort range, outperforming traditional methods prone to fluctuations. Furthermore, the proposed MPC strategy effectively shifts the peak load to periods of lower electricity prices, achieving an 18.58% reduction in overall energy costs.

The smart grid technology permits the revolution of the electrical system from a conventional power grid to an intelligent power network which has led the improvements in electrical system in terms of energy efficiency and sustainable energy integration. This study presents the energy management/coordination scheme for domestic demand using the key strategy of smart grid energy efficiency modelling. The structure consists of combining renewable energy resources, photovoltaic (PV) and wind power generation connected to the utility grid with energy storage system (ESS) in an optimal control manner to coordinate the power flow of a residential home. Based on the demand response schemes in the framework of real-time electricity pricing, this work designs a closed-loop optimal control strategy that is created by the dynamic model of the ESS to compute the system performance index, which is formulated by the cost of the energy flows. A dynamic distributed energy storage strategy (DDESS) is implemented to optimally coordinate the energy system, which reduces the total energy consumption from the main grid of more than 100% of the load demand. The designed model introduces a payback scheme while robustly optimising the energy flows and minimising the utility grid's energy consumption cost.

The imperative to decarbonize long-haul, heavy-duty trucking for mitigating both global climate change as well as air pollution is clear. Given recent developments in battery and ultra-fast charging technology, some of the prominent barriers to electrification of trucking are dissolving rapidly. Here we shed light on a significant yet less-understood barrier, which is the general approach to retail electricity pricing. We show that this is a near term pathway to $0.06/kWh charging costs that will make electric trucking substantially cheaper than diesel. This pathway includes (i) reforming demand charges to reflect true, time-varying system costs; (ii) avoiding charging during a few specific periods (<45 h in a year) when prices are high; and (iii) achieving charging infrastructure utilization of 33% or greater. However, without reforming demand charges and low utilization of charging infrastructure, charging costs more than quadruple (to $0.28/kWh). We also illustrate that a substantial share of current trucking miles within select large regions of the United States can be reliably electrified without constraining electricity generation capacity as it exists today. Using historical hourly electricity price and load data for last 10 years and future projections in Texas and California, we show that electricity demand is at least 10% lower than yearly peak demand for at least 15 h on any given day. In sum, with electricity rates that closely reflect actual power system costs of serving off-peak trucking load, we show that electric trucks can provide overwhelming cost savings over diesel trucks. For reference, at diesel prices of $3.16/gal and charging costs of $0.06/kWh (inclusive of amortized charging station infrastructure costs), an electric truck's fuel cost savings are $251 000 (NPV), providing net savings of $61 000 (18% of lifetime diesel fuel cost) over the truck's lifetime at battery price of $170/kWh, or up to $148 000 (44% of lifetime diesel fuel cost) at a battery price of $100/kWh (figure 1).

This paper contributes to the well-known challenge of active user participation in demand side management (DSM). In DSM, there is a need for modern pricing mechanisms that will be able to effectively incentivize selfishly behaving users in modifying their energy consumption pattern towards system-level goals like energy efficiency. Three generally desired properties of DSM algorithms are: user satisfaction, energy cost minimization and fairness. In this paper, a personalized–real time pricing (P-RTP) mechanism design framework is proposed that fairly allocates the energy cost reduction only to the users that provoke it. Thus, the proposed mechanism achieves significant reduction of the energy cost without sacrificing at all the welfare (user satisfaction) of electricity consumers. The business model that the proposed mechanism envisages is highly competitive flexibility market environments as well as energy cooperatives.

Using a unique and highly detailed data set on energy consumption at the appliance-level for 200 Swedish households, seemingly unrelated regression (SUR)-based end-use specific load curves are estimated. The estimated load curves are then used to explore possible restrictions on load shifting (e. g. the office hours schedule) as well as the cost implications of different load shift patterns. The cost implications of shifting load from \"expensive\" to \"cheap\" hours, using the Nord pool spot prices as a proxy for a dynamic price, are computed to be very small; roughly 2-4% reduction in total daily cost from shifting load up to five hours ahead, indicating small incentives for households (and retailers) to adopt dynamic pricing of electricity.

Background Demand response is an important option for accommodating growing shares of renewable electricity, and therefore, crucial for the success of the energy transition in Germany and elsewhere. In conjunction with smart meters, real-time (or ‘dynamic’) electricity tariffs can facilitate the flexibilization of power consumption and reduce energy bills. Whilst such tariffs are already quite common in several EU member states, Germany lags behind in this respect. The country makes for an interesting case study because of the sheer volume of additional flexibility that its energy transition necessitates. Main text This paper discusses how German policymakers can make real-time tariffs more attractive for households and thus entice them to better adapt their consumption to current market conditions. Following an analysis of the current impediments to the adoption of such tariffs, we discuss four policy options: (1) a more ambitious legal definition of real-time tariffs that can promote market transparency and leverage potential savings for consumers, (2) a shift in energy taxation that encourages the uptake of renewable power and increases price spreads, (3) a new model of dynamic network charges which combines grid-serving and market-serving incentives, and (4) a subsidy for users of real-time tariffs that helps internalise the benefits they provide to all electricity consumers. Given the similar regulatory framework, our suggestions should generally also apply to other countries in Europe and beyond. Conclusions Overall, we argue that there is considerable scope for policymakers to better exploit market forces to ensure security of electricity supply at lower social cost. Our call for stricter regulation in order to allow the markets to better guide consumer behaviour may seem like a paradox—but it is one well worth embracing.