On-peak Hours Research Articles

Experimental assessment of using phase change materials in vapor compression refrigeration systems for condenser pre-cooling

The primary objective of this investigation is to empirically assess a new vapor compression cycle while employing phase change material (PCM) energy storage. During off-peak periods, the PCM undergoes charging, and during on-peak hours, it is discharged to cool the refrigerant entering the condenser, thereby enhancing the condenser's overall performance. In contrast to previous studies that exclusively examined the charging or discharging processes of air conditioning (AC) units, this research delves into the entire 24-h charge and discharge cycle. The proposed system is subjected to testing under identical conditions, both without PCM (conventional mode) and with a PCM storage tank, throughout a 24-h period. The PCM storage tank, which utilizes water as its phase change medium, possesses a volume of approximately 300 L and starts at an initial temperature of 25 °C. The outcomes of the study demonstrate notable improvements when incorporating the PCM storage tank. Specifically, the daily coefficient of performance (COP) increases by approximately 7 %, rising from 2.17 to 2.33. Additionally, it leads to a reduction in both the daily available cooling load and daily compressor energy consumption, with decreases of approximately 3.7 % (from 58.4 to 56.2 kWh) and 10.3 % (from 26.9 to 24.1 kWh), respectively.

Dynamic evaluation of a vapor compression refrigeration cycle combined with a PCM storage tank for improving condenser performance

In this study, a vapor compression refrigeration cycle integrated with a phase change material (PCM) storage tank has been dynamically simulated over a 24-h period. The primary objective of this system is to reduce electric energy consumption during on-peak hours (12:00–19:00) and shift it to off-peak hours (1:00–10:00). During off-peak hours, the vapor compression refrigeration system stores cooling energy in the PCM storage tank. The stored cooling energy is then reused during on-peak hours to pre-cool the condenser inlet refrigerant, enhancing the cooling system's performance and reducing electric consumption during on-peak hours. Oleic acid, eutectic mixtures of 45 % capric acid and 55 % lauric acid by weight (CL), a commercial blend of salt hydrate and paraffin wax (SP224A), and CaCl2.6H2O have been selected as the PCM mediums. The effects of weather conditions and PCM storage tank parameters on the system's performance have been investigated. The results indicate that SP224A performs the best in most weather conditions. The maximum electric peak load shaving occurred under Ramsar city weather conditions, achieving 98.85 %. Additionally, the compressor's electric energy consumption is reduced by about 23.38 %. Moreover, increasing the length of PCM storage pipes leads to a reduction in compressor energy consumption during on-peak hours, as well as electric peak load shaving, with an improvement of up to 156 %.

Heuristic model predictive control implementation to activate energy flexibility in a fully electric school building

This paper presents a heuristic model predictive control (MPC) methodology to activate energy flexibility in fully electric school buildings in cold climates to reduce electricity demand during peak demand periods of the electric grid. To streamline the implementation of MPC, the proposed approach employs grey-box archetypes, a clustering of weather conditions to identify typical scenarios and a limited number of possible setpoint profiles. A data-driven grey-box approach is used to create archetype models for different thermal zones in a typical school building; this approach enables rapid development and requires much less calibration data than black-box models. A third-order resistance-capacitance thermal network for zones with convective heating and a fourth-order model for zones with radiant floor heating are developed and calibrated using measured data from an all-electric school building in Québec, Canada. The weather data are clustered into several categories, representing different weather conditions (6 clusters representing two ambient temperature ranges and three solar radiation ranges). The heuristic MPC strategy uses predefined optimal setpoint profiles for each cluster and weather prediction one day ahead to shift the building load from on-peak to off-peak hours. For each heuristic MPC scenario, the model runs a simulation using forecast weather data to quantify and activate energy flexibility in response to grid requirements. The developed MPC framework was implemented in the school used as the case study. Ten classrooms are investigated, with six using the MPC and four as a reference case with the reactive control system and default zone setpoint profiles. Results indicate that the school building can provide between 47% and 95% energy flexibility (load shifting relative to reference) during on-peak hours and up to 44% electricity cost reduction while satisfying acceptable temperature constraints. By implementing the proposed MPC, energy flexibility of 32 W/m2 of floor area for the zones with a convective heating system and 65 W/m2 of floor area for the zones with radiant heating can be achieved during a demand response event. The proposed strategy can be generalized and replicated in other school buildings.

Hydropower Reservoir Optimization with Solar Generation-Changed Energy Prices in California

AbstractGrowing solar photovoltaic supply has significantly reshaped energy prices, lowering them during solar generating hours. Large-scale hydropower reservoir operations need to adapt to changes in energy prices to maximize hydropower revenue. This paper evaluates effects of solar generation-changed energy prices on hydropower generation for five multipurpose reservoirs in California using a hydroeconomic optimization model. In California, major solar generation began in 2013, so years 2010–2012 are a pre-solar period, and years 2013–2018 are post-solar. Reservoir operations, hydropower generation and revenue between these periods are compared. Operations in the wet season (January to June), and the dry season (July to December) are evaluated. Results show that releases are more profitable when hydropower is generated twice a day during on-peak hours in the morning and evening in the wet season. When water is scarce, energy is generated only during the higher-price evening peak. Hydropower generation is mostly curtailed between 10am and 6pm due to large solar supplies, and increase during morning and evening peaks when solar generation is unavailable. However, by optimizing hydropower scheduling hours, the new energy price pattern can be more profitable. With increased energy price variability and adaptation, overall daily revenue can increase by about 14% in the wet season and 30% in the dry season.

Joint Multi-Objective Allocation of Parking Lots and DERs in Active Distribution Network Considering Demand Response Programs

Renewable energy sources (RESs) and electric vehicles (EVs) have been introduced as efficient technologies to address environmentally friendly and sustainable energy sources. However, the widespread integration of distributed renewable sources into the power grid and the growing adoption of EVs pose new challenges for distribution network operators. These challenges necessitate careful management to mitigate their impacts, particularly in meeting the additional demand arising from EV charging. To achieve these targets, it is necessary to strategically integrate RESs and EVs. This study focuses on the optimal allocation and energy management of distributed energy resources (DERs) and electric vehicle parking lots (EVPLs), taking into account the inherent uncertainty in the output power of these resources. Notably, parking lots (PLs) utilize vehicle-to-grid (V2G) technology of EVs and aggregate and inject their power into the distribution system. Therefore, EVs as a motion type of energy storage system play a significant role, especially in the on-peak hours. The optimization problem is addressed using the salp swarm algorithm (SSA) while adhering to operational constraints related to the power system, as well as both DERs and EVPLs. The main goal is to simultaneously enhance the technical, economic, and environmental performance of the system, by solving a multi-objective optimization problem. The effectiveness of this approach is evaluated using the IEEE 33-bus distribution system, with the study considering five different scenarios. The simulation results reveal that the planned deployment of DERs, given their proximity to the load centers, has effectively mitigated the overload impacts resulting from EVs’ charging. Furthermore, the implementation of a demand response program (DRP), cooperatively with the aforementioned resources, has significantly improved all key operating indicators of the system.

Efficiency boost and financial analysis of the thermophotovoltaic power system by photonic potential

A hybrid thermophotovoltaic system for thermal-to-electrical energy conversion is a solid-state technique that exclusively utilizes infrared light. However, it faces an upper limit on its conversion efficiency. In this study, its performance is enhanced by integrating concentrated solar energy and a thermal battery. A surface-to-surface radiative analysis in COMSOL is conducted to determine the thermal-to-electrical conversion efficiency. In addition to photon recycling, facilitated by spectral filters, the cells' junction is externally biased to impart a positive photonic chemical potential. This enhances the spectral intensity due to a higher apparent temperature and an increased entropy content. Consequently, it raises the thermal-to-electric energy conversion efficiency to 49% in a real-world scenario and up to 56% in an ideal case. Arbitrage is introduced into this power plant by incorporating an electric grid heater in the system, which bypasses solar heating. It stores electric energy during off-peak hours and provides it during on-peak hours. In this research, financial analysis is carried out using GAMS, and an estimated arbitrage of up to $920/kW-year is achieved from the electricity grid in Pennsylvania, New Jersey, and Maryland (PJM). The thermal battery in the system is also compared with other energy storage devices, proving it to be advantageous compared to Li-Ion and lead-acid batteries, which exhibit higher unit energy storage and conversion costs.

Experimental evaluation of a vapor compression cycle integrated with a phase change material storage tank based on two strategies

In this survey, the integration of a vapor compression cycle with a phase change material (PCM) storage tank has been evaluated experimentally during the hottest days of five cities with different weather conditions: Tehran, Ramsar Hamedan, Bushehr, and Ahvaz. The temperature conditions for outdoor (condenser, compressor, etc.) and indoor (evaporator) air conditioning units are provided with the help of two separated test chambers and a controller system. The desired system has been examined based on two scenarios. In each scenario, the system is assessed for the conventional air conditioning (AC) system (with no PCM involved) and the AC plus PCM unit. Based on scenario 1 operating strategy, the results indicate that adding the PCM tank decreases the total daily COP compared to the conventional AC unit, which varies from 32.07% for Bushehr city to 17.23% for Tehran city. Whereas the AC plus PCM system’s performance enhances during on-peak hours compared to the conventional AC system, which varies from 65.99% for Hamedan city to 12.84% for Bushehr city. Based on scenario 2 operating strategy, adding the PCM storage tank to the conventional AC unit increases total electric energy consumption over 24 h, which varies from 29.35% for Bushehr city to 5.49% for Tehran city. While it leads to shaving the electric peak load from 45.11% to 67.02% in which the highest peak load shaving belongs to Tehran and the least is related to Bushehr city.

Optimized Power Dispatch for Smart Building and Electric Vehicles with V2V, V2B and V2G Operations

The operation of smart buildings (with solar, storage and suitable power routing infrastructure) can be optimized with the addition of parking stations for electric vehicles (EVs) with vehicle-to-everything (V2X) operations including vehicle-to-vehicle (V2V), vehicle-to-building (V2B) and vehicle-to-grid (V2G) operations. In this paper, a multi-objective optimization framework is proposed for the smart charging and discharging of EVs along with the maximization of revenue and savings of smart building (prosumers with solar power, a battery storage system and a parking station) and non-primary/ordinary buildings (consumers of electricity without solar power, a battery storage system and parking station). A mixed-integer linear program is developed to maximize the profits of smart buildings that have bilateral contracts with non-primary buildings. The optimized charging and discharging (V2X) of EVs at affordable rates utilizing solar power and a battery storage system in the smart building helps to manage the EV load during on-peak hours and prevent utility congestion. The results indicate that in addition to the 4–9% daily electricity cost reductions for non-primary buildings, a smart building can achieve up to 60% of the daily profits. Further, EVs can save 50–69% in charging costs while performing V2X operations.

Reliable energy management of residential buildings with hybrid energy systems

Distributed energy resources like photovoltaic (PV), fuel cells (FCs), and others are receiving more and more attention as a solution to the rise in residential energy consumption. In this paper, a reliability-based energy management model is proposed for residential buildings with local generation units. The proposed model studies the contingency analysis of the hybrid system of the residential buildings considering demand response (DR) programs under different participation rates of residents in DR. Along with accounting for the energy flows across the various components of the hybrid energy system, the analysis of PV power output utilization to sale of power to the main grid, battery charging, and demand supply are also taken into consideration. The proposed model aims to choose the best operating strategies while ensuring the system is well-functioning with energy supplement costs as the objective function to be reduced. In addition to the optimal electric and thermal balances, findings allow for the reasonable deduction of the PV module and fuel cell forms. A numerical example is implemented and examined to confirm the practicality of the suggested approach. From various viewpoints, the best operational techniques are determined and contrasted, where the findings are analyzed considering the reliable operation of the system. Applying DR with different participation rates has varying effects on energy management during contingency occurrences at off/on-peak hours. A 30% DR participation during off-peak hours reduces daily energy supplement cost by roughly one third. During on-peak hours, DR participation reduces energy supplement cost by over 76%.

A rational pricing model for demand response programs in contemporary distribution systems

In the context of emerging electricity market, the demand response (DR) coupled dynamic pricing has assumed significant importance. However, the existing dynamic price models have raised typical socio-economic problems pertaining to cross-subsidy, free-riders, social inequity, and unjustified allocation of benefits, etc. This paper presents a new dynamic price model which aims to overcome the problems of cross-subsidy and free-riders of the existing price models for widespread acceptance and efficient utilization of price-based DR programs in contemporary distribution systems. The model is designed based on the concept of economic equilibrium that describes the balance between purchase and sale of the electricity. Proposed price model generates demand-linked price signal during on-peak hours that imposes different rates to different customers based on their share in peak demand, but remains static otherwise. Further, the financial benefits and penalties pertaining to DR are self-adjusted among customers while preserving social equity and profit of the utility. These novel features make the proposed model class apart by outperforming the other pricing models that have been already discussed in the literature. The proposed model shows mass customers are saving up to 60% in electricity bills thus providing a suitable environment for enhanced participation in DR. It also outperforms the existing models in terms of risk-reward trade-off. The detailed investigation of the proposed model is carried out on standard test bench bus load data and is also compared with the existing pricing models.

Multi-objective optimization of a phase change material-based shell-and-tube heat exchanger for cold thermal energy storage: experiments and numerical modeling

• PCM-based cold thermal energy storage is numerically and experimentally analysed. • The experimental outcomes show room for optimization in the PCM discharging phase. • Charging/discharging time and heat transfer area/melting difference are optimized. • Multi-objective genetic algorithm allows to exploit up to 72% PCM latent heat. • This optimal solution implies a 69% increase in minimum heat transfer area. In recent years, significantly increasing demand for air conditioning systems has led to higher power consumption during on-peak hours. If optimized, latent heat thermal storage for chiller systems – thanks to its high storage density and compact structure – can reduce installed cooling capacity and allow the chiller to operate more continuously. Starting from the existing design, this work presents a multi-objective optimization framework to improve the storage performance of a phase change material (PCM)-based shell-and-tube heat exchanger. To address this issue – based on experimental data – a 2D axial-symmetric transient numerical model is first developed. To investigate the overall performance of the system – depending on geometrical features and chiller operating conditions – a parametric analysis is performed. Then, by coupling the numerical model developed in COMSOL Multiphysics environment with MATLAB, the system performance is optimized through the genetic algorithm (GA), i.e., minimizing PCM charging/discharging time, by varying the chiller operating conditions. The optimal solution is achieved under a water mass flow rate of 0.095 kg/s, implying a reduction in the inlet temperature of 1.25 °C with respect to the reference case. Results are further validated through experimental tests and discussed looking at the PCM melting and solidification processes for better exploitation of this storage technique. As the main outcome of the model optimization on user’s demand, the maximum amount of PCM that can be fully exploited is equal to 40 % of the initial one. Therefore, based on this value, a further optimization step by GA is performed to define the minimum heat transfer area, resulting in a shell diameter reduction of 12 cm to exploit 72 % of the PCM potential.

Distribution locational marginal pricing for congestion management of an active distribution system with renewable-based microgrids under a privacy-preserving market clearing approach and load models

Microgrid formation has changed the one-direction and passive structure of distribution systems. Increasing the energy demand makes it more sophisticated to keep the operational constraints for the distribution system operator, especially during on-peak hours, which may lead to line congestion. In this study, a distributed locational marginal pricing strategy for congestion management of a radial distribution system with several renewable-based MGs is proposed. The proposed model considers the independent operation for each MG, confidentiality aspects for sensitive data, and the computational burden of problem-solving based on a distributed problem-solving approach. The results show the achievement of the optimal global solution for the feasible problem with more than 99.99% accuracy. Due to the prominent role of load in line congestion, voltage-dependent load models at the distribution system and price-based load models at the MGs side have been considered. The results show that the ignorance of accurate load models can lead to the estimation error for energy trading revenue of MGs up to 29.83% and up to 1.47% for the distribution system compared to non-consideration of load models in a congestion state. Distribution locational marginal prices have also changed by up to 4.18% compared to the case of no-load model considerations.

NOMA-Based Integrated Satellite-Terrestrial Networks with Wireless Caching

To decrease the transmission delay and alleviate communication congestion, caching has been applied in the integrated satellite-terrestrial networks (ISTNs). In traditional caching schemes, the cache-enabled relays update files during off-peak hours and push them during on-peak hours. However, for the enormous number of end devices in ISTNs, the long-time waiting for off/on-peak hours would decrease the quality of service. To address this issue, this paper proposes a two-tier nonorthogonal multiple access- (NOMA-) based ISTN with wireless caching. Specifically, data transmission of this model consists of two phases. For the first phase, named the file-push-and-placement (FPAP), the satellite employs the NOMA protocol to send information to both the relay and first-tier user. While for the second phase, which is called the file-push-and-delivery (FPAD), second-tier users are served by the relay employing NOMA. Performance analysis of the proposed configuration is carried out, focusing on the exact and asymptotic outage probability (OP), diversity order, and hit probability. Moreover, the influence of key factors on the system performance is also investigated. Compared with the traditional configuration, it is shown analytically and numerically that the proposed scheme achieves lower OP and higher diversity order.

Temporal robustness assessment framework for city-scale bus transit networks

Despite the prevalence of bus transit networks in urban public transportation, they are very susceptible to disruptions that can have negative implications, accompanied by severe socioeconomic consequences and service quality consequences. In addition, since bus transit system is comprised of stops, routes, and passengers, any disruptions in the bus network can trigger chain reaction disruption scenarios, leading to cascade failures across the entire network. Unlike the existing literature, the present study evaluates the robustness of bus transit network considering the varied hourly operation. The robustness assessment is evaluated using a weighted network considering losses in bus frequency and passenger flow, both are essential to inform service quality improvement. Some of the key findings include the observed significant variation in the hourly performance of the network compared to the daily performance when a failure occurs. Moreover, the network is more robust during on-peak hours and mid-peak compared to off-peak hours. However, the network is sensitive to losing service frequency compared passenger flow when a failure happens. These insights could be used to inform a robustness-based spatiotemporal design of bus transit networks.

Model-Based Control Strategies to Enhance Energy Flexibility in Electrically Heated School Buildings

This paper presents a general methodology to model and activate the energy flexibility of electrically heated school buildings. The proposed methodology is based on the use of archetypes of resistance–capacitance thermal networks for representative thermal zones calibrated with measured data. Using these models, predictive control strategies are investigated with the aim of reducing peak demand in response to grid requirements and incentives. A key aim is to evaluate the potential of shifting electricity use in different archetype zones from on-peak hours to off-peak grid periods. Key performance indicators are applied to quantify the energy flexibility at the zone level and the school building level. The proposed methodology has been implemented in an electrically heated school building located in Québec, Canada. This school has several features (geothermal heat pumps, hydronic radiant floors, and energy storage) that make it ideal for the purpose of this study. The study shows that with proper control strategies through a rule-based approach with near-optimal setpoint profiles, the building’s average power demand can be reduced by 40% to 65% during on-peak hours compared to a typical profile.

In-duct phase change material-based energy storage to enhance building demand flexibility

This paper presents a novel energy storage solution by incorporating phase change material (PCM) panels in supply ducts to increase a building’s thermal storage capacity and demand flexibility. During off-peak hours, the system runs at a supply air temperature (SAT) below the PCM solidification point to charge the storage unit with “cooling” energy. During on-peak hours, a higher SAT is utilized so that the stored “cooling” energy can be discharged into the supply-air as a means to reduce the peak air-conditioning power usage. To evaluate the potentials of peak demand reduction and utility cost savings, a numerical model for a PCM panel prototype and its heat exchange with the air flow in the duct was developed and calibrated using experimental data. Whole building energy simulations were conducted in a co-simulation environment that has integrated the developed PCM model, EnergyPlus Department of Energy (DOE) prototypical model for a medium office building and a calibrated model for variable-speed direct-expansion cooling systems. The simulations covered five cities in different U.S. climate zones over a three-month cooling season and used actual time-of-use (TOU) rate schedules offered by the local electric utility companies. The simulation results have shown the PCM storage could reduce the on-peak energy consumption by 23–32% and the seasonal cooling electricity cost by up to 16%, with a simple rule-based control strategy. A simple payback analysis resulted in payback periods from 7.5 to 27 cooling months.

Innovative combined optimization and modified maximum rectangle method for equipment sizing and operational strategy of an integrated system with cold thermal storage

An innovative method is proposed here for optimum sizing of an integrated system and finding its operational strategy of equipment during a year. This system provided power, heating, and cooling loads of buildings. To decrease the electricity consumption and operating cost of compression cooling system, an ice thermal energy storage (ITES) and also absorption chiller included in the integrated system. Modified Maximum Rectangle Method (MRM M) is defined and combined with multi-objective optimization technique. Two objective functions were Relative Annual Benefit (RAB) and Exergy Efficiency (ηEx,tot). For a residential complex, MRMM and GA optimization methods obtained results in a more accurate and faster procedure. Analysis of results from OPT-MRMM showed that two 884 kW gas engines provided the highest exergy efficiency (46.54%), the maximum value of RAB (1.04 × 106 $/year), and the shortest payback period (3.64 years) for the optimized and selected integrated system in comparison with system specifications obtained by other studied methods. Results also showed that OPT-MRMM method provided the lowest waste heat, the lowest fuel consumption in backup boilers, the highest amount of selling electricity to the grid and the lowest amount of specific emission release in comparison with those for optimization or MRM method alone. Applying this method provided low amount of buying electricity from the grid as well.The results for our case study also showed that the annual electricity consumption of traditional Vapor Compression Refrigeration (VCR) reduced from 19.99 × 105 kWh/year to 16.18 × 105 kWh/year (3.81 × 105 kWh/year reduction i.e., 19%) when ITES added to the VCR equipment. Furthermore, shifting the electricity consumption from high tariff period (on-peak and mid-peak hours) to low tariff period (off-peak hours), reduced the annual electricity cost for 26.62%. Finally, the results showed 25%–32% reduction for annual electricity consumption of VCR-ITES in OPT-MRMM method in comparison with that for VCR alone.

Optimizing energy consumption patterns of smart home based on Sine Cosine Algorithm

This paper presents a new application of the sine cosine algorithm (SCA) to obtain optimal home energy management systems (HEMS) that use the load shifting strategy of demand side management (DSM) to optimize the energy consumption patterns of a smart home. It aims to manage the load demand in an efficient way to decrease electricity bills and peak to average ratio (PAR) while maintaining user comfort through coordination among home devices. In order to meet the load demand of electricity consumers, this study schedules the load on a day-ahead and real-time basis. The main objective of the paper is to help balance the load through ON-peak hours and OFF-peak hours. Three price signals, namely: real-time pricing (RTP), time of use (TOU), and critical peak pricing (CPP), are used to evaluate the proposed algorithm. The results show that the electricity bill and PAR are minimized by up to 40% and 50%, respectively. The scheduling using coordination between devices in real-time does not significantly impact the cost of electricity and the peak to average ratio.

Exergy, economic and pinch analyses of a novel integrated structure for cryogenic energy storage and freshwater production using ejector refrigeration cycle, desalination unit, and natural gas combustion plant

This study aims to investigate an innovative hybrid structure of electricity storage at off-peak hours and its application at on-peak hours. In this paper, a novel hybrid system for energy storage and freshwater production using air compression and liquefaction system, ejector refrigeration cycle (ERC), thermal multi-effect desalination (MED) system, and natural gas combustion unit (GCU) is developed. The wasted heat of the liquefaction cycle is stored at off-peak hours to provide heat to the ERC and thermal desalination system and preheat the inlet streams to the turbines at on-peak hours. Also, wasted refrigeration of liquid air stream and refrigeration resulting from liquefied natural gas (LNG) regasification operations are stored at on-peak hours and are utilized in compressed air liquefaction at off-peak hours. This integrated structure produces 83.12 kg/s liquid air and 38.91 kg/s freshwater at off-peak hours with 58.24 MW power consumption. During on-peak hours, the liquid air along with 2.681 kg/s LNG enters the turbines after preheating and combustion and generated 122.6 MW power. Round-trip and storage efficiencies are obtained as 64.91% and 75.96%, respectively. Also, the exergy efficiencies of the energy storage system at off-peak hours, energy production system at on-peak hours and the whole system are estimated as 78.13%, 75.85%, and 65.92%, respectively. The results of the exergy study illustrate the reactors (43.37%), heat exchangers (26.76%), and throttle valves (10.58%) have the highest contribution to exergy destruction in the proposed combined structure, respectively. This study provides the refrigeration cycle integration with process core as hot and cold composite curves and pinch analysis. Pinch investigation is conducted on the system multi-stream exchangers and the heat exchanger networks are extracted. The economic analysis illustrates that the investment return period, the levelized cost of the product, and net annual benefit in the hybridized system are 4.877 years, 0.0919 US$/kWh, 78.38 MMUS$/year, respectively. The results of sensitivity analysis indicate the round-trip efficiency increases up to 65.32% by raising the pressure of the on-peak power generation cycle.

Improving the Effectiveness of Time-of-Use Pricing to Make Household Electricity Consumption More Sustainable

To increase efficiencies and reduce greenhouse gas emissions, policymakers and electric utility providers are increasingly adopting time-of-use (TOU) pricing policies, which charge the most for electricity consumption during on-peak hours, the times when the demand for electricity is greatest. TOU policies aim to disincentivize on-peak electricity use in favor of use during usually low-demand, off-peak periods to reduce the suppliers’ need to augment electricity generated by low- or nonemitting sources (such as hydro-electric and nuclear power) with electricity generated by high-emitting sources (such as coal- or gas-fired power plants). Researchers and policymakers are attempting to apply behavioral science tactics to enhance the effectiveness of TOU pricing by making behavioral science-based changes to electricity bills or delivering personalized information about electricity use and pricing, or doing both. In this article, we describe several studies we conducted in Ontario, Canada, in which we examined customer responses to various bill designs and communications. Simplifying bills and emphasizing the high cost of on-peak use (that is, making on-peak pricing more salient) were effective at shifting behavior, as was the delivery of nudge reports, which compared a household's electricity use with its past consumption, offered conservation tips, and asked customers to make a pledge to reduce consumption. These studies demonstrate that incorporating behavioral tactics into existing consumer-facing communications can be an effective, low-cost, and scalable way to induce customers to increase off-peak electricity use and thus limit greenhouse gas emissions.