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1
Sundt, Swantje, Katrin Rehdanz, and Jürgen Meyerhoff. "Consumers’ Willingness to Accept Time-of-Use Tariffs for Shifting Electricity Demand." Energies 13, no. 8 (April 13, 2020): 1895. http://dx.doi.org/10.3390/en13081895.
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Time-of-use (TOU) electricity tariffs represent an instrument for demand side management. By reducing energy demand during peak times, less investments in otherwise necessary, costly, and CO2 intensive redispatch would be required. We use a choice experiment (CE) to analyze private consumers’ acceptance of TOU tariffs in Germany. In our CE, respondents choose between a fixed rate tariff and two TOU tariffs that differ by peak time scheme and by a control of appliances’ electricity consumption during that time. We use a mixed logit model to account for taste heterogeneity. Moreover, investigating decision strategies, we identify three different strategies that shed light on drivers of unobserved taste heterogeneity: (1) Always choosing the status quo, (2) always choosing the maximum discount, and (3) choosing a TOU tariff but not always going for the maximum discount. Overall, about 70% of our 1398 respondents would choose a TOU tariff and shift their electricity demand, leading to a decline in energy demand during peak times. Rough estimates indicate that this would lead to significant savings in electricity generation, avoiding up to a mid to large-sized fossil-fuel power plant.
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Sundt, Swantje. "Influence of Attitudes on Willingness to Choose Time-of-Use Electricity Tariffs in Germany. Evidence from Factor Analysis." Energies 14, no. 17 (August 31, 2021): 5406. http://dx.doi.org/10.3390/en14175406.
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Time-of-use (TOU) electricity tariffs are a demand side measure to ease balancing of demand and supply to cope with a rising share of renewables in a country’s electricity mix. In general, consumers require compensation for accepting these tariffs. This study analyzes how attitudes drive consumers’ willingness to choose a TOU tariff in Germany. To identify attitudinal profiles, I use an exploratory factor analysis on items capturing positive and negative attitudes towards TOU tariffs, climate change awareness, and belief in energy saving measures. I use these factors as predictors in an ordered logit specification to estimate consumers’ stated willingness to choose a TOU tariff. Three factors are significant: positive and negative attitudes towards TOU tariffs, and climate change awareness. These findings highlight that decision makers who aim at balancing demand and supply through the use of TOU tariffs should focus on informing consumers about the positive impacts of these tariffs on climate change mitigation, grid stability, and possible energy savings.
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Azrina Mohd Azman, Nur, Md Pauzi Abdullah, Mohammad Yusri hasan, Dalila Mat Said, and Faridah Hussin. "Enhanced Time of Use Electricity Pricing for Industrial Customers in Malaysia." Indonesian Journal of Electrical Engineering and Computer Science 6, no. 1 (April 1, 2017): 155. http://dx.doi.org/10.11591/ijeecs.v6.i1.pp155-159.
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<p>New Time of Use (ToU) tariff scheme known as Enhanced ToU (EToU) has been introduced on 1st January 2016 for industrial customers in Malaysia. EToU scheme is the advanced version of current ToU where the daily time frame is divided into six period blocks, as compared to only two in the existing ToU. Mid-peak tariff is introduced on top of peak-hour and off-peak tariff. The new scheme is designed to reduce Malaysia’s peak hour electricity demand. On customer side, they could be benefited from the low off-peak tariff by simply shifting their consumption. However, it depends on their consumption profile and their flexibility in shifting their consumption. Since EToU scheme is voluntary, each customer needs to perform cost-benefit analysis before deciding to switch into the scheme. This paper analyzes this problem by considering EToU tariff scheme for industry and customer’s electricity consumption profile. Case studies using different practical data from different industries are presented and discussed in this paper.</p>
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Phan, Binh Thi Thanh, Qui Minh Le, and Cuong Viet Vo. "Optimizing the time of use tariff with different scenarios of load management." Science and Technology Development Journal 20, K7 (November 27, 2018): 15–20. http://dx.doi.org/10.32508/stdj.v20ik7.1206.
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Demand Response program is applied in many countries as an effective instrument to regulate the electricity consumption. In this program, time of use (TOU) tariff is used widely. Optimal TOU pricing according to different objectives was mentioned in this paper such as peak load reduction, improving load curve, energy conservation, avoiding a new peak load. This is a problem with multiobjective functions in different unit of measurement and is solved by PSO algorithm. An example to find optimal TOU tariff for one utility is also presented in this paper.
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Xue, Wanlei, Xin Zhao, Yan Li, Ying Mu, Haisheng Tan, Yixin Jia, Xuejie Wang, Huiru Zhao, and Yihang Zhao. "Research on the Optimal Design of Seasonal Time-of-Use Tariff Based on the Price Elasticity of Electricity Demand." Energies 16, no. 4 (February 6, 2023): 1625. http://dx.doi.org/10.3390/en16041625.
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Building a new power system with renewable energy as its main component is a key measure proposed by China to address the climate change problem. Strengthening demand-side management (DSM) is an important way to promote the development of a new power system. As an important economic incentive measure in DSM, the current TOU tariff is faced with the problem of a weak incentive effect due to the small tariff difference between the peak and valley periods. Against this background, a novel hybrid three-stage seasonal TOU tariff optimization model is proposed in this paper. First, the K-means++ algorithm is adopted to select the typical days of the four seasons through load curve clustering. Then, the price elasticity of the electricity demand model is constructed to calculate the self-elasticity and cross-elasticity in four seasons. Finally, the seasonal TOU tariff optimization model is constructed to determine the optimal TOU tariff. Through the proposed model, the tariff in the peak period has increased by 8.06–15.39%, and the tariff in the valley period has decreased by 18.48–27.95%. The result shows that the load in the peak period has decreased by 4.03–8.02% and the load in the valley period has increased by 6.41–9.75% through the proposed model.
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Chen, Lu, Yong Biao Yang, and Li Huang. "Design of Time-of-Use Model for Promoting Wind Power’s Penetration." Advanced Materials Research 953-954 (June 2014): 575–81. http://dx.doi.org/10.4028/www.scientific.net/amr.953-954.575.
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Considering wind power output characteristics, TOU model with frequency is proposed, in the form of “normal tariff plus frequency tariff”. Response mode and capacity is analyzed on the basis of consumer psychology. The numerical examples shows its effectiveness, that wind power output can be complemented by demand through tariff incentives, avoid abandoning the wind and reducing peak power difference.
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Jang, Minseok, Hyun-Cheol Jeong, Taegon Kim, and Sung-Kwan Joo. "Load Profile-Based Residential Customer Segmentation for Analyzing Customer Preferred Time-of-Use (TOU) Tariffs." Energies 14, no. 19 (September 26, 2021): 6130. http://dx.doi.org/10.3390/en14196130.
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Smart meters and dynamic pricing are key factors in implementing a smart grid. Dynamic pricing is one of the demand-side management methods that can shift demand from on-peak to off-peak. Furthermore, dynamic pricing can help utilities reduce the investment cost of a power system by charging different prices at different times according to system load profile. On the other hand, a dynamic pricing strategy that can satisfy residential customers is required from the customer’s perspective. Residential load profiles can be used to comprehend residential customers’ preferences for electricity tariffs. In this study, in order to analyze the preference for time-of-use (TOU) rates of Korean residential customers through residential electricity consumption data, a representative load profile for each customer can be found by utilizing the hourly consumption of median. In the feature extraction stage, six features that can explain the customer’s daily usage patterns are extracted from the representative load profile. Korean residential load profiles are clustered into four groups using a Gaussian mixture model (GMM) with Bayesian information criterion (BIC), which helps find the optimal number of groups, in the clustering stage. Furthermore, a choice experiment (CE) is performed to identify Korean residential customers’ preferences for TOU with selected attributes. A mixed logit model with a Bayesian approach is used to estimate each group’s customer preference for attributes of a time-of-use (TOU) tariff. Finally, a TOU tariff for each group’s load profile is recommended using the estimated part-worth.
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Sulaima, Mohamad Fani, Nurliyana Binti Baharin, Aida Fazliana Abdul Kadir, Norhafiz Bin Salim Obtained, and Elia Erwani Hassan. "Investigation of electricity load shifting under various tariff design using ant colony optimization algorithm." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 1 (October 1, 2022): 1. http://dx.doi.org/10.11591/ijeecs.v28.i1.pp1-11.
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<span>A price-based program through a time of use tariff (TOU) program is one of the initiatives to offer sufficient benefit for both consumers and generations sides. However, without any strategy for implementing optimal load management, a new tariff design structure will lead to the miss perception by electricity consumers. Therefore, this study offers an investigation toward appropriate TOU tariff design to reflect load profiles. Concurrently, the ant colony optimization (ACO) algorithm was proposed to deal with the load shifting strategy to determine the best load profiles and reducing the consumers’ electricity cost. The sample load profiles data is obtained from various residential houses, such as single-story, double-story, semi-D, apartment, and bungalow houses. The significant comparison between baseline flat tariffs to several TOU tariffs has shown an improvement in the percentage of cost saving for approximately 7 to 40%. Furthermore, the identified load management was observed where the maximum load shifting weightage was set up to 30% to reflect the consumers’ effort towards energy efficiency (EE) program. The previously proposed TOU design was identified to be a suitable structure that can promote balancing of EE and demand response (DR) program effort in most consumers' houses category in Malaysia.</span>
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Nazar, J., J. J. Jamian, M. A. Baharudin, and S. N. Syed Nasir. "New Dynamic Time-of-Use Tariff For Islanded Microgrid System With High Penetration of Renewable Energy." Journal of Physics: Conference Series 2523, no. 1 (July 1, 2023): 012027. http://dx.doi.org/10.1088/1742-6596/2523/1/012027.
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Abstract This paper proposes a dynamic time-of-use (d-TOU) tariff scheme for microgrid (MG) systems in islanded mode. The main problem for the islanded MG is the high cost of electricity, and the output from renewable energy is uncontrollable compared to the traditional grid. Therefore, this paper focuses on developing a suitable tariff scheme that provides reliability and financial benefits for both utility and customer. The time zone energy prices based on the Levelized Cost of Energy (LCOE) are introduced for islanded MG. The results show a contradiction between islanded MG with the standard traditional power generation TOU. Even though the LCOE obtained for MG is higher than conventional electricity rates, the greenhouse gas (GHG) emissions rate is reduced by 85%. In conclusion, the proposed d-TOU tariff scheme is suitable for the islanded MG system and it is beneficial for both the utility and the customer by not causing a financial burden to the utility and encouraging the customer to make a demand response in the future.
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Lee, Jinyeong, Jaehee Lee, and Young-Min Wi. "Impact of Revised Time of Use Tariff on Variable Renewable Energy Curtailment on Jeju Island." Electronics 10, no. 2 (January 10, 2021): 135. http://dx.doi.org/10.3390/electronics10020135.
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Jeju Island announced the “Carbon Free Island (CFI) Plan by 2030” in 2012. This plan aims to replace conventional generators with distributed energy resources (DERs) up to a level of 70% by 2030. Akin to Jeju Island, as DERs have been expanded in islanded power systems, variable renewable energy (VRE) has become a significant component of DERs. However, VRE curtailment can occur to meet power balance, and VRE curtailment generally causes energy waste and low efficiency, so it should be minimized. This paper first presents a systematic procedure for estimating the annual VRE curtailment for the stable operation of the islanded power systems. In this procedure, the VRE curtailment is estimated based on the power demand, the grid interconnection, the capacity factor of VRE, and conventional generators in the base year. Next, through the analysis of the hourly net load profile for the year in which the VRE curtailment is expected to occur, a procedure was proposed to find the season and hour when VRE curtailment occurs the most. It could be applied to revised Time-of-Use (ToU) tariff rates as the most cost-effective mitigation method of VRE curtailment on the retail market-side. Finally, price elasticity of electricity demand was presented for applying the revised ToU tariff rate scenarios in a specific season and hour, which found that VRE curtailment occurred the most. Considering self- and cross-price elasticity of electricity, revised ToU tariff rate scenarios were used in a case study on Jeju Island. Eventually, it was confirmed that VRE curtailment could be mitigated when the revised ToU tariff rates were applied, considering the price elasticity of demand.
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Lee, Jinyeong, Jaehee Lee, and Young-Min Wi. "Impact of Revised Time of Use Tariff on Variable Renewable Energy Curtailment on Jeju Island." Electronics 10, no. 2 (January 10, 2021): 135. http://dx.doi.org/10.3390/electronics10020135.
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Jeju Island announced the “Carbon Free Island (CFI) Plan by 2030” in 2012. This plan aims to replace conventional generators with distributed energy resources (DERs) up to a level of 70% by 2030. Akin to Jeju Island, as DERs have been expanded in islanded power systems, variable renewable energy (VRE) has become a significant component of DERs. However, VRE curtailment can occur to meet power balance, and VRE curtailment generally causes energy waste and low efficiency, so it should be minimized. This paper first presents a systematic procedure for estimating the annual VRE curtailment for the stable operation of the islanded power systems. In this procedure, the VRE curtailment is estimated based on the power demand, the grid interconnection, the capacity factor of VRE, and conventional generators in the base year. Next, through the analysis of the hourly net load profile for the year in which the VRE curtailment is expected to occur, a procedure was proposed to find the season and hour when VRE curtailment occurs the most. It could be applied to revised Time-of-Use (ToU) tariff rates as the most cost-effective mitigation method of VRE curtailment on the retail market-side. Finally, price elasticity of electricity demand was presented for applying the revised ToU tariff rate scenarios in a specific season and hour, which found that VRE curtailment occurred the most. Considering self- and cross-price elasticity of electricity, revised ToU tariff rate scenarios were used in a case study on Jeju Island. Eventually, it was confirmed that VRE curtailment could be mitigated when the revised ToU tariff rates were applied, considering the price elasticity of demand.
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Esa, Nur Farahin Asa @., Md Pauzi Abdullah, Mohammad Yusri Hassan, and Faridah Hussin. "Disaggregated Electricity Bill Base on Utilization factor and Time-of-use (ToU) Tariff." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 3 (June 1, 2017): 1498. http://dx.doi.org/10.11591/ijece.v7i3.pp1498-1505.
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Time of Use tariff is introduced to motivate users to change their electricity usage pattern. Commonly the tariff is high during peak hours and relatively low during off peak hours, to encourage users to reduce consumption during peak hours or shift it to off-peak hours. This tariff scheme provides opportunities for building owners to reduce their electricity bill provided that their electricity usage patterns of various spaces in that building at every hour are known. In practice, the kWh meter installed by the utility can only provide the overall hourly electricity consumption pattern. To know the usage pattern of different spaces or rooms, separate individual meter need to be installed in each space/room, which is costly and impractical. This paper presented the disaggregated electricity bill method based on user utilization factor and time of use (ToU) tariff. It estimates hourly electricity bill of each appliance at each space/room. Utilization factor is used to represent the electricity usage behavior of the occupants. The proposed method is applied on practical load profile data of a university building.
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Chen, Wen, Chun Lin Guo, Zong Feng Li, Dong Ming Jia, Jun Chen, Xiang Zhen Li, Guo Zhong Zhuang, and Zhu Liu. "Research of Time-of-Use Tariff Considering Electric Vehicles Charging Demands." Advanced Materials Research 953-954 (June 2014): 1354–58. http://dx.doi.org/10.4028/www.scientific.net/amr.953-954.1354.
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With the large-scale EV(electric vehicle) integrating into the power system, new challenges has been brought to the planning as well as the security of the network. There will be a great impact on the system if the system operator ignores the vast quantity of EV charging at the same. Thus, taking measures, e.g. the multiple tariff, is of vital importance to give the guidance to the EV owners to charging wisely to save the daily cost on charging, as well as reduce the gap between peak load and valley load. A model for TOU has been presented in this paper. In the model , an objective function is declared to describe the purpose of TOU, and the optimal solution is gained according to the response of EV when the price of electricity changes. Finally , a case based on the daily load curve of a certain place is calculated with the model in this paper.
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Ludwig, Patrick, and Christian Winzer. "Tariff Menus to Avoid Rebound Peaks: Results from a Discrete Choice Experiment with Swiss Customers." Energies 15, no. 17 (August 31, 2022): 6354. http://dx.doi.org/10.3390/en15176354.
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While automation helps to increase load-shifting, the combination of automation with time-of-use (TOU) or critical-peak prices (CPP) may lead to rebound peaks at the beginning of low-tariff periods which may exceed the original peak. Using a discrete choice experiment with a representative sample of 696 Swiss consumers, we find that a tariff menu including (i) a flat price with direct load control (DLC) and (ii) a time-of-use tariff without direct load control could avoid this problem. The majority (57%) of mostly younger customers, which could be interested in automation would likely sign up for a DLC with flat prices, while the remaining customers would either chose a TOU tariff with manual load control (28%) or avoid any form of load-shifting incentives (15%).
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Kwon, Yeongenn, Taeyoung Kim, Keon Baek, and Jinho Kim. "Multi-Objective Optimization of Home Appliances and Electric Vehicle Considering Customer’s Benefits and Offsite Shared Photovoltaic Curtailment." Energies 13, no. 11 (June 3, 2020): 2852. http://dx.doi.org/10.3390/en13112852.
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A Time-of-Use (TOU)-tariff scheme, helps residential customers to adjust their energy consumption voluntarily and reduce energy cost. The TOU tariff provides flexibility in demand, alleviate volatility caused by an increase in renewable energy in the power system. However, the uncertainty in the customer’s behavior, causes difficulty in predicting changes in residential demand patterns through the TOU tariff. In this study, the dissatisfaction model for each time slot is set as the energy consumption data of the customer. Based on the actual customer’s consumption pattern, the user sets up a model of dissatisfaction that enables aggressive energy cost reduction. In the proposed Home Energy Management System (HEMS) model, the efficient use of jointly invested offsite photovoltaic (PV) power generation is also considered. The optimal HEMS scheduling result considering the dissatisfaction, cost, and PV curtailment was obtained. The findings of this study indicate, that incentives are required above a certain EV battery capacity to induce EV charging for minimizing PV curtailment.
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Andruszkiewicz, Jerzy, Józef Lorenc, and Agnieszka Weychan. "Price-Based Demand Side Response Programs and Their Effectiveness on the Example of TOU Electricity Tariff for Residential Consumers." Energies 14, no. 2 (January 7, 2021): 287. http://dx.doi.org/10.3390/en14020287.
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Demand side response is becoming an increasingly significant issue for reliable power systems’ operation. Therefore, it is desirable to ensure high effectiveness of such programs, including electricity tariffs. The purpose of the study is developing a method for analysing electricity tariff’s effectiveness in terms of demand side response purposes based on statistical data concerning tariffs’ use by the consumers and price elasticity of their electricity demand. A case-study analysis is presented for residential electricity consumers, shifting the settlement and consequently the profile of electricity use from a flat to a time-of-use tariff, based on the comparison of the considered tariff groups. Additionally, a correlation analysis is suggested to verify tariffs’ influence of the power system’s peak load based on residential electricity tariffs in Poland. The presented analysis proves that large residential consumers aggregated by tariff incentives may have a significant impact on the power system’s load and this impact changes substantially for particular hours of a day or season. Such efficiency assessment may be used by both energy suppliers to optimize their market purchases and by distribution system operators in order to ensure adequate generation during peak load periods.
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Andruszkiewicz, Jerzy, Józef Lorenc, and Agnieszka Weychan. "Price-Based Demand Side Response Programs and Their Effectiveness on the Example of TOU Electricity Tariff for Residential Consumers." Energies 14, no. 2 (January 7, 2021): 287. http://dx.doi.org/10.3390/en14020287.
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Demand side response is becoming an increasingly significant issue for reliable power systems’ operation. Therefore, it is desirable to ensure high effectiveness of such programs, including electricity tariffs. The purpose of the study is developing a method for analysing electricity tariff’s effectiveness in terms of demand side response purposes based on statistical data concerning tariffs’ use by the consumers and price elasticity of their electricity demand. A case-study analysis is presented for residential electricity consumers, shifting the settlement and consequently the profile of electricity use from a flat to a time-of-use tariff, based on the comparison of the considered tariff groups. Additionally, a correlation analysis is suggested to verify tariffs’ influence of the power system’s peak load based on residential electricity tariffs in Poland. The presented analysis proves that large residential consumers aggregated by tariff incentives may have a significant impact on the power system’s load and this impact changes substantially for particular hours of a day or season. Such efficiency assessment may be used by both energy suppliers to optimize their market purchases and by distribution system operators in order to ensure adequate generation during peak load periods.
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Phan, Binh Thi Thanh, and Thao Thi Thu Huynh. "Demand response model for TOU (Time of Use) analysis." Science and Technology Development Journal 19, no. 4 (December 31, 2016): 5–13. http://dx.doi.org/10.32508/stdj.v19i4.778.
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The demand response program is focused on changing the electrical consumption as the response to the time of use tariff changing. This program is considered by utilities currently. To estimate the effectiveness of TOU changing, the works try to find the analytical models expressing the changing of electrical consumption and electrical prices. All models are based on the assumption about the optimal response. This paper proposed three ways to find the models. The first way is based on the cost-share function knowing that the response is optimal. The second way is an approximately estimation of demand elasticity coefficients. The third is based on the neural network. The two first ways tried to find the analytical model, the third focused on the consumption response by prices of day.
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Choi, Bong-Gi, and Sung-Yul Kim. "Evaluation of Power System Impact by V2G Operation Using Time-of-Use Tariff." Transactions of The Korean Institute of Electrical Engineers 69, no. 6 (June 30, 2020): 855–60. http://dx.doi.org/10.5370/kiee.2020.69.6.855.
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Punmiya, Rajiv, and Sangho Choe. "ToU Pricing-Based Dynamic Electricity Theft Detection in Smart Grid Using Gradient Boosting Classifier." Applied Sciences 11, no. 1 (January 4, 2021): 401. http://dx.doi.org/10.3390/app11010401.
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In the near future, it is highly expected that smart grid (SG) utilities will replace existing fixed pricing with dynamic pricing, such as time-of-use real-time tariff (ToU). In ToU, the price of electricity varies throughout the whole day based on the respective utilities’ decisions. We classify the whole day into two periods with very high and low probabilities of theft activities, termed as the “theft window” and “non-theft window”, respectively. A “smart” malicious consumer can adjust his/her theft to mostly targeting the theft window, manipulate actual usage reporting to outsmart existing theft detectors, and achieve the goal of “paying reduced tariff”. Simulation results show that existing schemes do not detect well such window-based theft activities conversely exploiting ToU strategies. In this paper, we begin by introducing the core concept of window-based theft cases, which is defined at the basis of ToU pricing as well as consumption usage. A modified extreme gradient boosting (XGBoost) based machine learning (ML) technique called dynamic electricity theft detector (DETD) has been presented to detect a new type of theft cases.
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Andruszkiewicz, Jerzy, Józef Lorenc, and Agnieszka Weychan. "Demand Price Elasticity of Residential Electricity Consumers with Zonal Tariff Settlement Based on Their Load Profiles." Energies 12, no. 22 (November 13, 2019): 4317. http://dx.doi.org/10.3390/en12224317.
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The concept of price elasticity of demand has been widely used for the assessment of the consumers’ behavior in the electricity market. As the residential consumers represent a significant percentage of the total load, price elasticity of their demand may be used to design desirable demand side response programs in order to manage peak load in a power system. The method presented in this study proposes an alternative approach towards price elasticity determination for zonal tariff users, based on comparisons of load profiles of consumers settled according to flat and time-of-use electricity tariffs. A detailed explanation of the proposed method is presented, followed by a case-study of price elasticity determination for residential electricity consumers in Poland. The forecasted values of price elasticity of demand for the Polish households using time-of-use (TOU) tariff vary between −1.7 and −2.3, depending on the consumers’ annual electricity consumption. Moreover, an efficiency study of residential zonal tariff is performed to assess the operation of currently applicable electricity tariffs. Presented analysis is based on load profiles published by Distribution System Operators and statistical data, but the method can be applied to the real-life measurements from the smart metering systems as well when such systems are accessible for residential consumers.
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Jeong, Hyun Cheol, Jaesung Jung, and Byung O. Kang. "Development of Operational Strategies of Energy Storage System Using Classification of Customer Load Profiles under Time-of-Use Tariffs in South Korea." Energies 13, no. 7 (April 4, 2020): 1723. http://dx.doi.org/10.3390/en13071723.
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This study proposes a methodology to develop adaptive operational strategies of customer-installed Energy Storage Systems (ESS) based on the classification of customer load profiles. In addition, this study proposes a methodology to characterize and classify customer load profiles based on newly proposed Time-of-Use (TOU) indices. The TOU indices effectively distribute daily customer load profiles on multi-dimensional domains, indicating customer energy consumption patterns under the TOU tariff. The K-means and Self-Organizing Map (SOM) sophisticated clustering methods were applied for classification. Furthermore, this study demonstrates peak shaving and arbitrage operations of ESS with current supporting polices in South Korea. Actual load profiles accumulated from customers under the TOU rate were used to validate the proposed methodologies. The simulation results show that the TOU index-based clustering effectively classifies load patterns into ‘M-shaped’ and ‘square wave-shaped’ load patterns. In addition, the feasibility analysis results suggest different ESS operational strategies for different load patterns: the ‘M-shaped’ pattern fixes a 2-cycle operation per day due to battery life, while the ‘square wave-shaped’ pattern maximizes its operational cycle (a 3-cycle operation during the winter) for the highest profits.
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Chen, Lixing, and Hong Zhang. "Orderly Discharging Strategy for Electric Vehicles at Workplace Based on Time-of-Use Price." Mathematical Problems in Engineering 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/7025879.
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According to the parking features of electric vehicles (EVs) and load of production unit, a power supply system including EVs charging station was established, and an orderly discharging strategy for EVs was proposed as well to reduce the basic tariff of producer and improve the total benefits of EV discharging. Based on the target of maximizing the annual income of producer, considering the total benefits of EV discharging, the electric vehicle aggregator (EVA) and time-of-use (TOU) price were introduced to establish the optimization scheduling model of EVs discharging. Furthermore, an improved artificial fish swarm algorithm (IAFSA) combined with the penalty function methods was applied to solve the model. It can be shown from the simulation results that the optimal solution obtained by IAFSA is regarded as the orderly discharging strategy for EVs, which could reduce the basic tariff of producer and improve the total benefits of EV discharging.
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Sulaima, Mohamad Fani, Musthafah Mohd Tahir, Mohamad Firdaus Shukri, Aida Fazliana Abdul Kadir, Ainuddin Abu Kasim, Mohd Rahimi Yusoff, and Luqman Ali. "Optimal load management strategy under off-peak tariff riders in UTeM: a case study." Bulletin of Electrical Engineering and Informatics 11, no. 2 (April 1, 2022): 646–57. http://dx.doi.org/10.11591/eei.v11i2.3556.
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Demand response (DR) program through tariff initiative has been established in Malaysia since 1990. The available time of use (TOU) tariff focuses on providing price signals to consumers, especially from industrial and commercial sectors. In achieving a certain standard for off-peak tariff rider (OPTR) initiative to receive discount rate, consumers must improve load factors compared to the baseline declared. However, not all consumers are able to commit. In Universiti Teknikal Malaysia Melaka (UTeM), the TOU (C1-OPTR) tariff is proposed and applied when the available cost discount of 20% can be enjoyed by sustaining the load factor improvement (LFI). A simulator projected a flexible optimal load profile referred by the energy management team to achieve the university's sustainable energy management goal. Thus, securing the LFI would allow the energy consumption (kWh) and peak demand (kW) to be managed concurrently. As for testing results for two buildings, the load factor improves to 0.40, and the maximum demand reduces by about 35 kW. When getting the 20% discount for the OPTR scheme, the total cost saving is forecasted approximately USD 29,441.40 yearly. The current pilot project presents a positive sign with the peak demand reduction and load factor improvement close to the simulator's optimal profile.
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Li, Wensheng, Donglei Sun, Yajin Li, Xian Wang, Rui Liu, and Dayang Yu. "Research on Capacity Configuration and Optimal Operation of Microgrid Energy Storage under Demand Management." E3S Web of Conferences 329 (2021): 01015. http://dx.doi.org/10.1051/e3sconf/202132901015.
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In order to reduce the energy consumption cost, considering the influence of the correlation of photovoltaic output, load demand and peak valley TOU price in different periods on the optical storage capacity configuration, the system operation strategy and capacity configuration principle are determined based on the peak valley TOU price and energy storage charge state information and the actual size of photovoltaic output and load.Integrated with the historical load, time of use electricity price, state of charge, charge discharge power and other constraints of the energy storage system, the minimum objective function is utilized to optimize the output power of energy storage.The effectiveness of the model is verified by an example. The sensitivity analysis shows that different demand tariff rules affect the user demand declaration strategy. The energy storage system planning selects the light storage combination with appropriate capacity according to the demand tariff rules and the change of energy storage investment cost, which has practical engineering value.
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Usman, Muhammad, Wajahat Ullah Khan Tareen, Adil Amin, Haider Ali, Inam Bari, Muhammad Sajid, Mehdi Seyedmahmoudian, Alex Stojcevski, Anzar Mahmood, and Saad Mekhilef. "A Coordinated Charging Scheduling of Electric Vehicles Considering Optimal Charging Time for Network Power Loss Minimization." Energies 14, no. 17 (August 27, 2021): 5336. http://dx.doi.org/10.3390/en14175336.
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Electric vehicles’ (EVs) technology is currently emerging as an alternative of traditional Internal Combustion Engine (ICE) vehicles. EVs have been treated as an efficient way for decreasing the production of harmful greenhouse gasses and saving the depleting natural oil reserve. The modern power system tends to be more sustainable with the support of electric vehicles (EVs). However, there have been serious concerns about the network’s safe and reliable operation due to the increasing penetration of EVs into the electric grid. Random or uncoordinated charging activities cause performance degradations and overloading of the network asset. This paper proposes an Optimal Charging Starting Time (OCST)-based coordinated charging algorithm for unplanned EVs’ arrival in a low voltage residential distribution network to minimize the network power losses. A time-of-use (ToU) tariff scheme is used to make the charging course more cost effective. The concept of OCST takes the departure time of EVs into account and schedules the overnight charging event in such a way that minimum network losses are obtained, and EV customers take more advantages of cost-effective tariff zones of ToU scheme. An optimal solution is obtained by employing Binary Evolutionary Programming (BEP). The proposed algorithm is tested on IEEE-31 bus distribution system connected to numerous low voltage residential feeders populated with different EVs’ penetration levels. The results obtained from the coordinated EV charging without OCST are compared with those employing the concept of OCST. The results verify that incorporation of OCST can significantly reduce network power losses, improve system voltage profile and can give more benefits to the EV customers by accommodating them into low-tariff zones.
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Pasetti, Marco, Stefano Rinaldi, Alessandra Flammini, Michela Longo, and Federica Foiadelli. "Assessment of Electric Vehicle Charging Costs in Presence of Distributed Photovoltaic Generation and Variable Electricity Tariffs." Energies 12, no. 3 (February 5, 2019): 499. http://dx.doi.org/10.3390/en12030499.
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In this paper a general model for the estimation of the uncoordinated charging costs of Electric Vehicles (EVs) in the presence of distributed and intermittent generation, and variable electricity tariffs is presented. The proposed method aims at estimating the monthly average cost of uncoordinated charging of a single EV depending on the hour at which the EV is plugged into the EV Supply Equipment (EVSE). The feasibility and relevance of the proposed model is verified by applying the considered cost estimation method to a suitable use case. A single EV charging service offered at a public building equipped with a Photovoltaic (PV) system has been considered as reference case. The proposed model has been applied to the PV production and loads consumption data collected during one year, and the results of the study compared with the Time-Of-Use (TOU) electricity tariff. The application of the proposed model identified noticeable deviations among the computed EV charging costs and the reference TOU profile, with differences up to 40%, depending on the considered month and on the time of charging during the day. It can be concluded that such model could be used to properly detect opportunities of energy savings, and to define dedicated EV price signals that could help to promote the optimal use of distributed energy resources.
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Zhao, Ziyi. "Operation Simulation and Economic Analysis of Household Hybrid PV and BESS Systems in the Improved TOU Mode." Sustainability 15, no. 11 (May 31, 2023): 8853. http://dx.doi.org/10.3390/su15118853.
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With the popularization of electric vehicles and electric boilers, household electricity consumption will increase significantly. Household hybrid photovoltaic (PV) systems and battery energy storage systems (BESSs) can supply increasing household electricity consumption without expanding the existing distribution network. This paper validates the technical feasibility of connecting a large number of household power users that contain BESSs and PVs in a distribution line by a simulation in Matlab. In addition to technical feasibility, this article improves the time-of-use (TOU) form to achieve economic feasibility (covering equipment costs). In the past, the TOU was set from the perspective of the load demand of the grid, but the actual user participation would affect this effect. In this paper, based on a social science survey, a new three-level rate TOU is introduced, which has little impact on residents’ lifestyle, to effectively increase the response frequency effectively. Combined with the improved TOU and the state of PVs, the BESS control mode is set for simulation. To compare the three-tier rate TOU with the normal TOU tariff and select the best household BESS size, a MATLAB simulation is used to simulate the common household BESS capacity. The results indicate that the combination of the three-tier rate TOU with a 4 kWh household BESS can afford the investment of household PVs and BESSs. The high cost issue that previously primarily limited the true use of BESSs is expected to be resolved.
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Zeng, Zhiqiang, Xiaobin Chen, and Kaiyao Wang. "Energy Saving for Tissue Paper Mills by Energy-Efficiency Scheduling under Time-of-Use Electricity Tariffs." Processes 9, no. 2 (January 31, 2021): 274. http://dx.doi.org/10.3390/pr9020274.
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Environmental concerns and soaring energy prices have brought huge pressure of energy saving and emission reduction to tissue paper mills. Electricity is one of the main energy sources of tissue paper mills. The production characteristics of tissue paper mills make it easy to decrease energy cost by using time-of-use (TOU) electricity tariffs. This study investigates the bi-objective energy-efficiency scheduling of tissue paper mills under time-of-use electricity tariffs, the objectives of which are makespan and energy cost. First, considering the processing energy cost, setup energy cost, and transportation energy cost, an energy cost model of a tissue paper mill under TOU electricity tariffs is established. Second, the energy-efficiency scheduling model under TOU electricity tariffs is built based on the energy cost model. Finally, on the basis of decomposition and teaching–learning optimization, this study proposes a novel multi-objective evolutionary algorithm and further combined with the variable neighborhood search to solve the problem. The case study results demonstrate that our study of tissue paper mill energy saving is feasible, and the proposed method has better performance than the existing methods.
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Jaruwatanachai, Pramote, Yod Sukamongkol, and Taweesak Samanchuen. "Predicting and Managing EV Charging Demand on Electrical Grids: A Simulation-Based Approach." Energies 16, no. 8 (April 20, 2023): 3562. http://dx.doi.org/10.3390/en16083562.
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Electric vehicles (EVs) are becoming increasingly popular, and it is important for utilities to understand their charging characteristics to accurately estimate the demand on the electrical grid. In this work, we developed simulation models for different EV charging scenarios in the home sector. We used them to predict maximum demand based on the increasing penetration of EV consumers. We comprehensively reviewed the literature on EV charging technologies, battery capacity, charging situations, and the impact of EV loads. Our results suggest a method for visualizing the impact of EV charging loads by considering factors such as state of charge, arrival time, charging duration, rate of charge, maximum charging power, and involvement rate. This method can be used to model load profiles and determine the number of chargers needed to meet EV user demand. We also explored the use of a time-of-use (TOU) tariff as a demand response strategy, which encourages EV owners to charge their vehicles off-peak in order to avoid higher demand charges. Our simulation results show the effects of various charging conditions on load profiles and indicate that the current TOU price strategy can accommodate a 20% growth in EV consumers, while the alternative TOU price strategy can handle up to a 30% penetration level.
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Ren, Rui Huan, Rong Hu, and Liu Bin. "Optimal Sizing of Battery Energy Storage System in Distribution Network." Advanced Materials Research 860-863 (December 2013): 572–76. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.572.
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Economical value including reducing investment in generation side as well as transmission and distribution (T&D) side of the building capacity and decreasing the power expenses for important users brought by BESS are studied.Economic benefits obtained by BESS through decreasing loss of power grid and making use of time of use (TOU) price to reduce electricity tariff are analysed.Considering the primarily capital cost and operation and maintenance (O&M) cost,the capacity optimal allocation model for different types of BESS is developed under optimal conomy condition and genetic algorithm is used to solve it.
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Sulaima, M. F., N. Y. Dahlan, Z. H. Bohari, M. N. M. Nasir Nasir, R. F. Mustafa, and Duc Luong Nguyen. "Simultaneous Load Management Strategy for Electronic Manufacturing Facilities by using EPSO Algorithm." Journal of Mechanical Engineering 18, no. 3 (September 15, 2021): 193–214. http://dx.doi.org/10.24191/jmeche.v18i3.15426.
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Increased power demand has contributed to the power generation tension. Thus, there were critical needs for a better Price Based Program (PBP) policy for the consumers. In Peninsular Malaysia, through the development of a policy for the regulated market plan, the Enhanced Time of Use (ETOU) tariff was introduced by the utility to promote better price signals to the industrial consumers who contribute to the most massive energy consumption every year. However, fewer industrial consumers join the program due to a lack of Load Management (LM) knowledge while not confident in the price rate signal compared to the previous tariffs. Due to that reason, this study proposed simultaneous LM strategies for the selected power consumption profile in the electronic manufacturing facilities. Meanwhile, the Evolutionary Particle Swarm Optimization (EPSO) was adopted to search for the upright power consumption profiles of those average 11 locations of the manufacturing. The analysis of the results has compared to the baseline existing flat and Time of Use (TOU) tariffs. The results show an improvement in the energy consumption and maximum demand costs reduction of ~14-16% when load management was applied correctly. It is hoped that this study's results could help companies’ management of developing a strategic plan for the successful load management program.
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Xie, Kan, Weifeng Zhong, Weijun Li, and Yinhao Zhu. "Distributed Capacity Allocation of Shared Energy Storage Using Online Convex Optimization." Energies 12, no. 9 (April 30, 2019): 1642. http://dx.doi.org/10.3390/en12091642.
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This paper studies capacity allocation of an energy storage (ES) device which is shared by multiple homes in smart grid. Given a time-of-use (TOU) tariff, homes use the ES to shift loads from peak periods to off-peak periods, reducing electricity bills. In the proposed ES sharing model, the ES capacity has to be allocated to homes before the homes’ load data is completely known. To this end, an online ES capacity allocation algorithm is developed based on the online convex optimization framework. Under the online algorithm, the complex allocation problem can be solved round by round: at each round, the algorithm observes current system states and predicts a decision for the next round. The proposed algorithm is able to minimize homes’ costs by learning from home load data in a serial fashion. It is proven that the online algorithm can ensure zero average regret and long-term budget balance of homes. Further, a distributed implementation of the online algorithm is proposed based on alternating direction method of multipliers framework. In the distributed implementation, the one-round system problem is decomposed into multiple subproblems that can be solved by homes locally, so that an individual home does not need to send its private load data to any other. In simulation, actual home load data and a TOU tariff of the United States are used. Results show that the proposed online approach leads to the lowest home costs, compared to other benchmark approaches.
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Gheouany, Saad, Hamid Ouadi, and Saida El Bakali. "Hybrid-integer algorithm for a multi-objective optimal home energy management system." Clean Energy 7, no. 2 (March 29, 2023): 375–88. http://dx.doi.org/10.1093/ce/zkac082.
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Abstract Most of the energy produced in the world is consumed by commercial and residential buildings. With the growth in the global economy and world demographics, this energy demand has become increasingly important. This has led to higher unit electricity prices, frequent stresses on the main electricity grid and carbon emissions due to inefficient energy management. This paper presents an energy-consumption management system based on time-shifting of loads according to the dynamic day-ahead electricity pricing. This simultaneously reduces the electricity bill and the peaks, while maintaining user comfort in terms of the operating waiting time of appliances. The proposed optimization problem is formulated mathematically in terms of multi-objective integer non-linear programming, which involves constraints and consumer preferences. For optimal scheduling, the management problem is solved using the hybridization of the particle swarm optimization algorithm and the branch-and-bound algorithm. Two techniques are proposed to manage the trade-off between the conflicting objectives. The first technique is the Pareto-optimal solutions classification using supervised learning methods. The second technique is called the lexicographic method. The simulations were performed based on residential building energy consumption, time-of-use pricing (TOU) and critical peak pricing (CPP). The algorithms were implemented in Python. The results of the current work show that the proposed approach is effective and can reduce the electricity bill and the peak-to-average ratio (PAR) by 28% and 49.32%, respectively, for the TOU tariff rate, and 48.91% and 47.87% for the CPP tariff rate by taking into account the consumer’s comfort level.
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Sulaima, Mohamad Fani, Nofri Yenita Dahlan, Intan Azmira Abd. Razak, Zul Hasrizal Bohari, Amira Noor Farhanie Ali, and Muhd Muhtazam Noor Din. "DETERMINATION OF THE OPTIMUM LOAD PROFILE UNDER ENHANCED OF USE TARIFF (ETOU) SCHEME USING COMBINATION OF OPTIMIZATION ALGORITHMS AND SELF ORGANIZING MAPPING." ASEAN Engineering Journal 12, no. 4 (November 29, 2022): 65–73. http://dx.doi.org/10.11113/aej.v12.17324.
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Demand side management (DSM) has been conventionally adopted in many ways to efficiently managing the appropriate electricity loads. However, with the sophisticated design of the Time of Use (TOU) tariff to reflect electricity cost reduction, implementing proper Load Management (LM) strategies is challenging. To date, consumers still struggle to define a figure for the LM percentage to be involved in the demand response program. Due to that reason, this study proposes a method to find the best load profile reflecting the new tariff offered by using a combination of optimization algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Evolutionary PSO (EPSO), and Self-Organizing Mapping (SOM). The evaluation has been made to the manufacturing operation with the existing flat tariff to be transferred to the Enhanced Time of Use (ETOU). The test results show that the ability of the proposed combination method to define the optimal outputs such as energy consumption cost, maximum demand cost, load factor index, and building electricity economic responsive index. Meanwhile, the SOM algorithm has been used to classify the enormous numbers of those simulation results produced by algorithms while defining the best LM weightage. As the test results for the case study, it was found that the practical 6% LM weightage was able to reflect the optimal required load profile shifting to be applied by manufacturing operation. Thus, by determining the optimal load profile that suits the ETOU scheme, the consumers can enjoy cost benefits while supporting the demand response program concurrently.
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Sediqi, Mohammad Masih, Akito Nakadomari, Alexey Mikhaylov, Narayanan Krishnan, Mohammed Elsayed Lotfy, Atsushi Yona, and Tomonobu Senjyu. "Impact of Time-of-Use Demand Response Program on Optimal Operation of Afghanistan Real Power System." Energies 15, no. 1 (January 2, 2022): 296. http://dx.doi.org/10.3390/en15010296.
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Like most developing countries, Afghanistan still employs the traditional philosophy of supplying all its load demands whenever they happen. However, to have a reliable and cost-effective system, the new approach proposes to keep the variations of demand at the lowest possible level. The power system infrastructure requires massive capital investment; demand response (DR) is one of the economic options for running the system according to the new scheme. DR has become the intention of many researchers in developed countries. However, very limited works have investigated the employment of appropriate DR programs for developing nations, particularly considering renewable energy sources (RESs). In this paper, as two-stage programming, the effect of the time-of-use demand response (TOU-DR) program on optimal operation of Afghanistan real power system in the presence of RESs and pumped hydropower storage (PHS) system in the day-ahead power market is analyzed. Using the concept of price elasticity, first, an economic model indicating the behaviour of customers involved in TOU-DR program is developed. A genetic algorithm (GA) coded in MATLAB software is used accordingly to schedule energy and reserve so that the total operation cost of the system is minimized. Two simulation cases are considered to verify the effectiveness of the suggested scheme. The first stage programming approach leads case 2 with TOU-DR program to 35 MW (811 MW − 776 MW), $16,235 ($528,825 − $512,590), and 64 MW reductions in the peak load, customer bill and peak to valley distance, respectively compared to case 1 without TOU-DR program. Also, the simulation results for stage 2 show that by employing the TOU-DR program, the system’s total cost can be reduced from $317,880 to $302,750, which indicates a significant reduction in thermal units’ operation cost, import power tariffs and reserve cost.
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Suyono, Hadi, Mir Toufikur Rahman, Hazlie Mokhlis, Mohamadariff Othman, Hazlee Azil Illias, and Hasmaini Mohamad. "Optimal Scheduling of Plug-in Electric Vehicle Charging Including Time-of-Use Tariff to Minimize Cost and System Stress." Energies 12, no. 8 (April 20, 2019): 1500. http://dx.doi.org/10.3390/en12081500.
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Technological advancement, environmental concerns, and social factors have made plug-in electric vehicles (PEVs) popular and attractive vehicles. Such a trend has caused major impacts to electrical distribution systems in terms of efficiency, stability, and reliability. Moreover, excessive power loss, severe voltage deviation, transformer overload, and system blackouts will happen if PEV charging activities are not coordinated well. This paper presents an optimal charging coordination method for a random arrival of PEVs in a residential distribution network with minimum power loss and voltage deviation. The method also incorporates capacitor switching and on-load tap changer adjustment for further improvement of the voltage profile. The meta-heuristic methods, binary particle swarm optimization (BPSO) and binary grey wolf optimization (BGWO), are employed in this paper. The proposed method considers a time-of-use (ToU) electricity tariff such that PEV users will get more benefits. The random PEV arrival is considered based on the driving pattern of four different regions. To demonstrate the effectiveness of the proposed method, comprehensive analysis is conducted using a modified of IEEE 31 bus system with three different PEV penetrations. The results indicate a promising outcome in terms of cost and the distribution system stress minimization.
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Galatsopoulos, Charalampos, Simira Papadopoulou, Chrysovalantou Ziogou, Dimitris Trigkas, and Spyros Voutetakis. "Optimal Operation of a Residential Battery Energy Storage System in a Time-of-Use Pricing Environment." Applied Sciences 10, no. 17 (August 29, 2020): 5997. http://dx.doi.org/10.3390/app10175997.
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Premature ageing of lithium-ion battery energy storage systems (BESS) is a common problem in applications with or without renewable energy sources (RES) in the household sector. It can result to significant issues for such systems such as inability of the system to cover load demand for a long period of time. Consequently, the necessity of limiting the degradation effects at a BESS leads to the development and application of energy management strategies (EMS). In this work, EMSs are proposed in order to define optimal operation of a BESS without RES under time-of-use (ToU) tariff conditions. The objective of the developed EMSs is to reduce the capacity loss at the BESS in order to extend its lifetime expectancy and therefore increase the economic profit in the long-term. The EMSs utilize a widely used battery mathematical model which is experimentally validated for a specific BESS and a battery degradation mathematical model from the literature. Indicative simulation results of the proposed strategies are presented. The outcomes of these simulated scenarios illustrate that the objectives are achieved. The BESS operates efficiently by preventing premature ageing and ensuring higher economic profit at the long term.
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López García, Dahiana, José David Beltrán Gallego, and Sandra Ximena Carvajal Quintero. "Proposing Dynamic Pricing as an Alternative to Improve Technical and Economic Conditions in Rural Electrification: A Case Study from Colombia." Sustainability 15, no. 10 (May 13, 2023): 7985. http://dx.doi.org/10.3390/su15107985.
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Electricity access in rural areas is a critical challenge for global electrification. Most countries have focused on increasing electricity coverage without assessing the long-term sustainability of such solutions. To achieve sustainability in rural electrification solutions, it is necessary to consider five dimensions: technical, environmental, economic, social, and institutional. This paper reviews the state of rural electrification worldwide and proposes a dynamic tariff scheme that increases the technical and economic conditions of implemented solutions over an extended period. The proposed time-of-use (TOU) pricing methodology aims to flatten the system demand curve and utilize on-site renewable energy potentials. For the methodology’s evaluation, we analyzed a case study focused on electrification in isolated areas of Colombia, conducting a sensitivity analysis of user-behavior to the proposed tariff scheme using the concept of price elasticity of demand. We also evaluated the effect of the achieved demand curve flattening on the system frequency. The identified benefits highlight that an accurate pricing scheme can reduce the variation range in the system frequency. Furthermore, the evaluation results show that the implementation of the proposed tariff scheme has the potential to significantly flatten the demand curve and encourage the connection of non-conventional renewable sources to improve network conditions.
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Geng, Kaifeng, Chunming Ye, Zhen hua Dai, and Li Liu. "Bi-Objective Re-Entrant Hybrid Flow Shop Scheduling considering Energy Consumption Cost under Time-of-Use Electricity Tariffs." Complexity 2020 (February 22, 2020): 1–17. http://dx.doi.org/10.1155/2020/8565921.
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Re-entrant hybrid flow shop scheduling problem (RHFSP) is widely used in industries. However, little attention is paid to energy consumption cost with the raise of green manufacturing concept. This paper proposes an improved multiobjective ant lion optimization (IMOALO) algorithm to solve the RHFSP with the objectives of minimizing the makespan and energy consumption cost under Time-of-Use (TOU) electricity tariffs. A right-shift operation is then used to adjust the starting time of operations by avoiding the period of high electricity price to reduce the energy consumption cost as far as possible. The experimental results show that IMOALO algorithm is superior to multiobjective ant lion optimization (MOALO) algorithm, NSGA-II, and MOPSO in terms of the convergence, dominance, and diversity of nondominated solutions. The proposed model can make enterprises avoid high price period reasonably, transfer power load, and reduce the energy consumption cost effectively. Meanwhile, parameter analysis indicates that the period of TOU electricity tariffs and energy efficiency of machines have great impact on the scheduling results.
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Shaw, William George, Marc Mathews, and Johan Marais. "Holistic analysis of the effect on electricity cost in South Africa’s platinum mines when varying shift schedules according to time-of-use tariffs." Journal of Energy in Southern Africa 30, no. 4 (December 5, 2019): 26–40. http://dx.doi.org/10.17159/2413-3051/2019/v30i4a5675.
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In the past the cost of electricity was not a significant concern and was not common practice for mining companies to consider peak time-of-use (TOU) tariffs for their shift schedules. It has become more prevalent, as TOU tariffs continue increasing, to consider energy saving important. A study was carried out to analyse the mining operation of a South African deep-level platinum mine in respect of integrated load management, shift changes and TOU schedules. This was achieved by thoroughly analysing energy consumers, mine operational schedules and their interconnectedness. A specific mining system was analysed as a case study and a maximum savings scenario was determined, using the methodology formulated. The maximum savings scenario schedule change resulted in a 1.3% cost reduction. System improvements had an additional potential reduction effect of 8.4%, which was primarily the result of a reduction in compressors’ power consumption. The implications of the proposed schedule adjustments necessitated a realistic scenario. The realistic scenario had an effective financial reduction of 0.7%. The realistic schedule change, however, opened the door for large system operational improvements, which could increase the reduction potential by 7.6%. The study methods described illustrate the potential implications of integrated load management and operational schedule optimisation on the power demand and cost savings in the mining industry, specifically focusing on deep-level platinum mines.
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Maiorino, Angelo, Adrián Mota-Babiloni, Manuel Gesù Del Duca, and Ciro Aprea. "Scheduling Optimization of a Cabinet Refrigerator Incorporating a Phase Change Material to Reduce Its Indirect Environmental Impact." Energies 14, no. 8 (April 13, 2021): 2154. http://dx.doi.org/10.3390/en14082154.
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Phase Change Materials (PCMs) incorporated in refrigerators can be used to shift their energy consumption from peak periods, when the electric network energy demand is the highest, to off-peak periods. While PCMs can flatten the energy demand curve, they can achieve economic savings if Time-of-Use (TOU) electricity tariffs are applied. However, the hourly carbon emission factor is not commonly linked to the hourly tariff, and the final CO2 emitted due to the operations of the refrigerator would not be fully optimized. In this work, a method based on the Simulated Annealing optimization technique was proposed to identify the optimal working schedule of a cabinet refrigerator incorporating a PCM to reduce its indirect carbon emissions. Data from countries with different representative carbon intensity profiles were used. The normalized standard deviation and normalized range are the best statistical indexes to predict carbon emission reduction in the proposed solution. These parameters proved that countries with a higher hourly carbon intensity variation (Uruguay, France, Denmark, and Germany) benefit from the application of the algorithm. Cost and carbon emission reduction cannot be maximized simultaneously, and a trade-off is required.
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Chu, Xiaolin, Yuntian Ge, Xue Zhou, Lin Li, and Dong Yang. "Modeling and Analysis of Electric Vehicle-Power Grid-Manufacturing Facility (EPM) Energy Sharing System under Time-of-Use Electricity Tariff." Sustainability 12, no. 12 (June 13, 2020): 4836. http://dx.doi.org/10.3390/su12124836.
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Electric vehicles (EVs) have obtained increasing public interest due to the associated economic and environmental benefits. Recently, studies regarding the economic advantages of adopting EVs as energy storages for commercial/residential buildings are emerging. In fact, according to the U.S. Energy Information Administration, the industrial sector consumes more energy than all of the other sectors combined, which is about 54% of the world’s total delivered energy. The energy consumption pattern in manufacturing facilities is based on production schedules and the heat transfer between machines and the ambient surroundings, thus, differs greatly from commercial/residential buildings. However, little research attention has been given to analyse the synergies of integrating EVs and manufacturing facilities to improve energy efficiency. To fill this research gap, in this study, a comprehensive model is established to evaluate the economic and environmental performance of an energy sharing system that consists of the EVs, power grid, and manufacturing facilities (EPM) under Time-of-Use (TOU) electricity tariff. The model is formulated as a mixed integer nonlinear programming format by considering practical production schedules, heat exchange between machines and ambient surroundings, as well as the heating, ventilation, and air conditioning (HVAC) system. The case study results indicate that the presented EPM energy sharing system has great potential to reduce energy cost and CO2 emissions. In addition, compared to the results from winter scenarios, it is shown that more cost savings can be achieved in summer days.
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Prapanukool, Chawin, and Surachai Chaitusaney. "Designing Solar Power Purchase Agreement of Rooftop PVs with Battery Energy Storage Systems under the Behind-the-Meter Scheme." Energies 13, no. 17 (August 27, 2020): 4438. http://dx.doi.org/10.3390/en13174438.
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With a significant growth of rooftop photovoltaic systems (PVs) with battery energy storage systems (BESS) under the behind-the-meter scheme (BTMS), the solar power purchase agreement (SPPA) has been developed into one of the most attractive models. The SPPA is a scheme where the investors propose to directly sell electricity from rooftop PVs to the customers. The proposed rates are typically performed in terms of the discount rates on the time-of-use (TOU) tariff with demand charges. The operation modes of the BESS should also be designed in accordance with the proposed rates. Therefore, this paper proposes a methodology to design the discount rates and operation modes of the BESS which will minimize the electricity charges of the customers while maintaining the revenue of the investors under the SPPA and BTMS. The reverse power flow is considered as additional revenue to the investors. This paper also implements the proposed methodology with tariff structure in Thailand. The result showed that the installed capacity of rooftop PVs and battery capacity directly affect the discount rates and operation modes of the BESS. The rate of excess energy also has a significant impact on the discount rates but not affect the operation modes.
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Pei, Rui, Jihua Xie, Hanlin Zhang, Kaiyu Sun, Zhi Wu, and Suyang Zhou. "Robust Multi-Layer Energy Management and Control Methodologies for Reefer Container Park in Port Terminal." Energies 14, no. 15 (July 23, 2021): 4456. http://dx.doi.org/10.3390/en14154456.
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The full electrification of ports is a promising prospect for saving energy and reducing greenhouse gas emissions. The control scheme of the reefer container is particularly important for the energy management of the port, as the operation of the reefer container is one of the main energy consumers of ports. This paper proposes a reefer container hierarchical control scheme that contains a day-ahead module and intra-day module which is used to generate a rough scheduling strategy based on forecast data and fine-tuning the strategy, respectively. The final strategy should realize the economical operation while ensuring that each reefer container does not exceed the temperature limit during operation. Numerical analysis on the reefer container park with 200 and 850 containers using the Time of Use (TOU) tariff and super-peak tariff is fully analyzed. In the case of 200 containers, the proposed method helps reduce operating costs by about 14.7%, and 18% in the scenario of 850 containers. The proposed method can effectively save container operating costs and ensure that the internal temperature of the container does not exceed the limit while changing the distribution of energy which could help alleviate the peak load problem of the port electric system.
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Bouzid, Mariam, Oussama Masmoudi, and Alice Yalaoui. "Exact Methods and Heuristics for Order Acceptance Scheduling Problem under Time-of-Use Costs and Carbon Emissions." Applied Sciences 11, no. 19 (September 24, 2021): 8919. http://dx.doi.org/10.3390/app11198919.
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This research focuses on an Order Acceptance Scheduling (OAS) problem on a single machine under time-of-use (TOU) tariffs and taxed carbon emissions periods with the objective to maximize total profit minus tardiness penalties and environmental costs. Due to the NP-hardness of the considered problem especially in presence of sequence-dependent setup-times, two fix-and-relax (FR) heuristics based on different time-indexed (TI) formulations are proposed. A metaheuristic based on the Dynamic Island Model (DIM) framework is also employed to tackle this optimization problem. These approached methods show promising results both in terms of solution quality and solving time compared to state-of-the-art exact solving approaches.
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Ma, Yiwei, Yuyang Chen, Xin Chen, Fuchun Deng, and Xiantong Song. "Optimal dispatch of hybrid energy islanded microgrid considering V2G under TOU tariffs." E3S Web of Conferences 107 (2019): 02007. http://dx.doi.org/10.1051/e3sconf/201910702007.
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In order to achieve a prospective economic effect of renewable energy generations and vehicle to grid (V2G), this paper proposes an optimal dispatch method of wind-PV-battery microgrid considering V2G under time-of-use (TOU) tariffs for those isolated communities in remote islands and mountainous areas. A cooperative dispatch strategy and an optimal dispatch model are both presented for the total operation cost minimization and higher utilization of renewable energy generation. Finally, the simulation results show that the proposed method is effective and feasible.
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Guru, Neelakantha, Samarjit Patnaik, Manas Ranjan Nayak, and Meera Viswavandya. "Wind generator and storage system scheduling for customer benefit and battery life." Bulletin of Electrical Engineering and Informatics 12, no. 5 (October 1, 2023): 2586–94. http://dx.doi.org/10.11591/eei.v12i5.4661.
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Due to increased fossil fuel use and fossil fuel limitations, the Indian energy industry is migrating to non-conventional energy resources such as solar power, wind production, and fuel cells, among others. The unpredictability of non-conventional energy sources makes it difficult to balance an electrical system when they are incorporated, necessitating the incorporation of a storage device into the grid. In a microgrid system with wind turbine generation (WTG) and a battery energy storage system (BESS), the BESS may reserve energy during periods of surplus generation and release it to the grid during times of peak demand. The suggested technique establishes the state of charge (SOC) schedule for the BESS by employing an artificial rabbit optimisation (ARO) algorithm that minimises energy costs for customers. The state of health (SOH) of the energy storage is incorporated as an ageing coefficient, which causes the BESS to behave conservatively in order to retain its lifespan. Using a time of use (TOU) tariff, simulation results suggest a substantial possibility to boost the savings of consumers in a grid-connected micro-grid. The simulation findings indicate that by efficiently scheduling the BESS power management technique, the proposed method improves a number of distribution system efficacy.
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Amin, Adil, Wajahat Ullah Khan Tareen, Muhammad Usman, Haider Ali, Inam Bari, Ben Horan, Saad Mekhilef, Muhammad Asif, Saeed Ahmed, and Anzar Mahmood. "A Review of Optimal Charging Strategy for Electric Vehicles under Dynamic Pricing Schemes in the Distribution Charging Network." Sustainability 12, no. 23 (December 4, 2020): 10160. http://dx.doi.org/10.3390/su122310160.
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This study summarizes a critical review on EVs’ optimal charging and scheduling under dynamic pricing schemes. A detailed comparison of these schemes, namely, Real Time Pricing (RTP), Time of Use (ToU), Critical Peak Pricing (CPP), and Peak Time Rebates (PTR), is presented. Globally, the intention is to reduce the carbon emissions (CO2) has motivated the extensive practice of Electric Vehicles (EVs). The uncoordinated charging and uncontrolled integration however of EVs to the distribution network deteriorates the system performance in terms of power quality issues. Therefore, the EVs’ charging activity can be coordinated by dynamic electricity pricing, which can influence the charging activities of the EVs customers by offering flexible pricing at different demands. Recently, with developments in technology and control schemes, the RTP scheme offers more promise compared to the other types of tariff because of the greater flexibility for EVs’ customers to adjust their demands. It however involves higher degree of billing instability, which may influence the customer’s confidence. In addition, the RTP scheme needs a robust intelligent automation system to improve the customer’s feedback to time varying prices. In addition, the review covers the main optimization methods employed in a dynamic pricing environment to achieve objectives such as power loss and electricity cost minimization, peak load reduction, voltage regulation, distribution infrastructure overloading minimization, etc.
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Chen, Shih-Hsin, Yeong-Cheng Liou, Yi-Hui Chen, and Kun-Ching Wang. "Order Acceptance and Scheduling Problem with Carbon Emission Reduction and Electricity Tariffs on a Single Machine." Sustainability 11, no. 19 (September 30, 2019): 5432. http://dx.doi.org/10.3390/su11195432.
Full textAbstract:
Order acceptance and scheduling (OAS) problems are realistic for enterprises. They have to select the appropriate orders according to their capacity limitations and profit consideration, and then complete these orders by their due dates or no later than their deadlines. OAS problems have attracted significant attention in supply chain management. However, there is an issue that has not been studied well. To our best knowledge, no prior research examines the carbon emission cost and the time-of-use electricity cost in the OAS problems. The carbon emission during the on-peak hours is lower than the one in mid-peak and off-peak hours. However, the electricity cost during the on-peak hours is higher than the one during mid-peak and off-peak hours when time-of-use electricity (TOU) tariff is used. There is a trade-off between sustainable scheduling and the electricity cost. To calculate the objective value, a carbon tax and carbon dioxide emission factor are included when we evaluate the carbon emission cost. The objective function is to maximize the total revenue of the accepted orders and then subtract the carbon emission cost and the electricity cost under different time intervals on a single machine with sequence-dependent setup times and release date. This research proposes a mixed-integer linear programming model (MILP) and a relaxation method of MILP model to solve this problem. It is of importance because the OAS problems are practical in industry. This paper could attract the attention of academic researchers as well as the practitioners.