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Engagement with new tariff scheme, Enhanced Time of Use (EToU) after switching from old tariff scheme, Time of Use (ToU) for chiller plant operation may be not promising a significant benefit for consumer in term of techno-economic value or even getting worse. This uncertainty condition make consumers doubt to switch onto latter. Therefore this study is focusing to analyze and looking potential benefit of energy cost the chiller's plant operation when engaging with the new tariff scheme EToU rather than ToU. Consumer's potential benefit appear for this study when switching from ToU to EToU tariff scheme for chiller plant operation through shifting the chiller's plant operation from peak to mid-peak hour while maintaining discharging operation of thermal energy storage (TES). This could benefit both consumer's on demand and utility on supply side respectively. Consumer may experience lower energy cost charged by utility. While utility could minimize the investment cost to install and maintain power system infrastructure in order to meet energy demand as well. However, further comprehensive study on this paper is also discussed for looking the more significant benefit to reduce energy cost of chiller's operation when engaging with EToU tariff scheme.

Daytime sleepiness impairs cognitive ability, but recent evidence suggests it is also an important driver of human motivation and behavior. We aimed to investigate the relationship between sleepiness and a behavior strongly associated with better health: social activity. We additionally aimed to investigate whether a key driver of sleepiness, sleep duration, had a similar relationship with social activity. For these questions, we considered bidirectionality, time of day, and differences between workdays and days off. Over 3 wk, 641working adults logged their behavior every 30 min, completed a sleepiness scale every 3 h, and filled a sleep diary every morning (rendering >292,000 activity and >70,000 sleepiness datapoints). Using generalized additive mixed-effect models, we analyzed potential nonlinear relationships between sleepiness/sleep duration and social activity. Greater sleepiness predicted a substantial decrease in the probability of social activity (odds ratio 95% CI = 0.34 to 0.35 for days off), as well as a decreased duration of such activity when it did occur. These associations appear especially robust on days off and in the evenings. Social duration moderated the typical time-of-day pattern of sleepiness, with, for example, extended evening socializing associated with lower sleepiness. Sleep duration did not robustly predict next-day social activity. However, extensive social activity (>5 h) predicted up to 30 min shorter subsequent sleep duration. These results indicate that sleepiness is a strong predictor of voluntary decreases in social contact. It is possible that bouts of sleepiness lead to social withdrawal and loneliness, both risk factors for mental and physical ill health.

The Smart Meter (SM) is an essential tool for successful balancing the demand-offer energy curve. It allows the linking of the consumption and production measurements with the time information and the customer’s identity, enabling the substitution of flat-price billing with smarter solutions, such as Time-of-Use or Real-Time Pricing. In addition to sending data to the energy operators for billing and monitoring purposes, Smart Meters must be able to send the same data to customer devices in near-real-time conditions, enabling new services such as instant energy awareness and home automation. In this article, we review the ongoing situation in Europe regarding real-time services for the final customers. Then, we review the architectural and technological options that have been considered for the roll-out phase of the Italian second generation of Smart Meters. Finally, we identify a collection of use cases, along with their functional and performance requirements, and discuss what architectures and communications technologies can meet these requirements.

The design and development of renewable energy resources‐based poly‐generation microgrids have recently increased to supply multiple demands such as cool, heat, and power as well as mitigate pollutants improving efficiency. This paper aims to develop a combined cooling, heating, and power production network integrating photovoltaic panels (PVs), wind and gas turbines, a battery, an ice bank tank, a heater, an electrical chiller, a thermal energy storage medium. In this tri‐generation facility, natural gas is utilized for district heating and fueling the gas turbine power generation cycle. The local power distribution system in combination with the output powers of PVs, wind and gas turbines is used to directly supply the electrical appliances, ice maker process, and chiller as well as charge the battery storage unit. Moreover, the air/water‐cooled chiller procures the cooling flux for a benchmark microgrid. Its heating energy requirement is also provided by the gas‐fired heater, the flue gases of the gas turbine, and the thermal storage medium. A mixed integer linear programming problem is coded using a generalized algebraic modeling system (GAMS) to minimize daily operating costs and emissions. Simulations are examined and analyzed over a 24‐h study horizon on a sample summer day. Time‐of‐use energy rates and RTPs are considered two strategic demand response schemes to investigate the cost‐effectiveness capability of the gas‐power nexus model. The proposed approach is coded using a GAMS to confirm its effectiveness and cost‐environ benefits in four cases with and without heat/cool/electrical storage units considering time‐of‐use energy rates and real‐time prices. In this paper, an optimal operation of the energy hub system in a microgrid is performed. To strengthen the energy hub, heating and cooling power hub have been used. In addition to dynamic system optimization, a pioneering model has been used as a mixed integer linear programming optimization problem, taking into account the demand response to minimize daily operating costs and air pollution.

Mental health in the UK had deteriorated compared with pre-pandemic trends. Existing studies on heterogenous wellbeing changes associated COVID-19 tend to segment population based on isolated socio-economic and demographic indicators, notably gender, income and ethnicity, while a more holistic and contextual understanding of such heterogeneity among the workforce seems lacking. This study addresses this gap by 1) combining UK time use surveys collected before and during COVID-19, 2) identifying latent lifestyles within three working mode groups (commuter, homeworker and hybrid worker) using latent class model, and 3) quantifying nuanced experiential wellbeing (ExWB) changes across workers of distinct lifestyles. The direction and magnitude of ExWB changes were not uniform across activity types, time of day, and lifestyles. The direction of ExWB change during the daytime activities window varied in accordance with lifestyle classifications. Specifically, ExWB decreased for all homeworkers but increased significantly for certain hybrid workers. Magnitude of ExWB change correlated strongly with lifestyle. To understand the significant heterogeneity in ExWB outcomes, a spatial-temporal conceptualisation of working flexibility is developed to explicate the strong yet complex correlations between wellbeing and lifestyles. The implications to post-pandemic “back-to-work” policies are 1) continued expansion of hybrid working optionality, 2) provide wider support for lifestyle adaptation and transitions.

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.

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.

Purpose>This paper aims to optimize the charging schedule for battery electric buses (BEBs) to minimize the charging cost considering the time-of-use electricity price.Design/methodology/approach>The BEBs charging schedule optimization problem is formulated as a mixed-integer linear programming model. The objective is to minimize the total charging cost of the BEB fleet. The charge decision of each BEB at the end of each trip is to be determined. Two types of constraints are adopted to ensure that the charging schedule meets the operational requirements of the BEB fleet and that the number of charging piles can meet the demand of the charging schedule.Findings>This paper conducts numerical cases to validate the effect of the proposed model based on the actual timetable and charging data of a bus line. The results show that the total charge cost with the optimized charging schedule is 15.56% lower than the actual total charge cost under given conditions. The results also suggest that increasing the number of charging piles can reduce the charging cost to some extent, which can provide a reference for planning the number of charging piles.Originality/value>Considering time-of-use electricity price in the BEBs charging schedule will not only reduce the operation cost of electric transit but also make the best use of electricity resources.

Multi-position refining furnaces are a critical strategy for energy-intensive industries to meet its demands of fast-paced production. In most literature, however, they serve only as a buffer, holding up to at most two ladles to maintain the proper temperature of ladles. These studies do not take full advantage of them, nor do they study the production scheduling of energy-intensive enterprises with multi-position refining furnaces under time-of-use (TOU) tariffs. Therefore, this paper presents a steelmaking-continuous casting (SCC) scheduling problem with multi-position refining furnaces under TOU tariffs. We firstly develop a mixed integer nonlinear programming (MINLP) model with the goals of minimizing jobs completion time, machines idle time, and total electricity costs, which subjects to the double-position characteristics and other process constraints. Owing to the complexity between the time-slots of TOU tariffs and the processing cycles of jobs, we design an intermediate function to calculate objectives efficiently. Furthermore, a Lagrangian relaxation (LR) algorithm based on a subgradient algorithm is utilized to solve the proposed model, and an interior point algorithm is adopted to solve sub-problems obtained by job-level and batch-level decomposition, whose solution approximates optimality comparing to GUROBI solver. The computational results demonstrate that the solution of job-level decomposition algorithm approximates the optimal scheduling scheme in an acceptable time and is superior to that of GUROBI solver. In addition, double-position instance can find a better scheduling scheme than nondouble-position one.

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