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In this article, we study a multiproduct multiperiod production planning problem faced by a cash-strapped manufacturer, who utilizes inventory-based financing (IBF) to capture additional demand during peak season, which otherwise will not be met due to cash shortage. Each product has a unique selling season and its production incurs a significant setup cost. We formulate the problem as an integer program and show the special case with one product is NP-hard. By exploiting the inherent structure of three distinct but related decisions, i.e., the production, financing, and working capital allocation decisions, we develop a Lagrangian relaxation-based branch-and-bound procedure to solve the model. Through a series of numerical experiments using real data, this article presents managerial insights into how the manufacturer's optimal production plan could differ with and without IBF. In an optimal plan, we show the profit maximizing strategy for the cash-strapped manufacturer is to overstock and pledge inventories to secure more loans prior to peak demand periods.

The digital revolution requires greater reliability from electric power systems. However, predicting the growth of electricity demand is challenging as there is still much uncertainty in terms of demographics, industry changes, and irregular consumption patterns. Machine learning has emerged as a powerful tool, particularly with the latest developments in deep learning. Such tools can predict electricity demand and, thus, contribute to better decision-making by energy managers. However, it is important to recognize that there are no efficient methods for forecasting peak demand growth. In addition, features that add complexity, such as climate change and economic growth, take time to model. Therefore, these new tools can be integrated with other proven tools that can be used to model specific system structures, such as system dynamics. This research proposes a unique framework to support decision-makers in dealing with daily activities while attentively tracking monthly peak demand. This approach integrates advances in machine learning and system dynamics. This integration has the potential to contribute to more precise forecasts, which can help to develop strategies that can deal with supply and demand variations. A real-world case study was used to comprehend the needs of the environment and the effects of COVID-19 on power systems; it also helps to demonstrate the use of leading-edge tools, such as convolutional neural networks (CNNs), to predict electricity demand. Three well-known CNN variants were studied: a multichannel CNN, CNN-LSTM, and a multi-head CNN. This study found that the multichannel CNN outperformed all the models, with an R2 of 0.92 and a MAPE value of 1.62% for predicting the month-ahead peak demand. The multichannel CNN consists of one main model that processes four input features as a separate channel, resulting in one feature map. Furthermore, a system dynamics model was introduced to model the energy sector’s dynamic behavior (i.e., residential, commercial, and government demands, etc.). The calibrated model reproduced the historical data curve fairly well between 2005 and 2017, with an R2 value of 0.94 and a MAPE value of 4.8%.

Concerns around reliability in China’s electricity sector have rekindled interest in a traditional solution: building more coal-fired generation. However, over the past decade China’s electricity sector has seen significant changes in supply costs, demand patterns, and regulation and markets over the past decade, with falling costs for renewable and storage generation, “peakier” demand, and the creation of initial wholesale markets. These changes suggest that traditional approaches to evaluating the economics of different supply options may be outdated. This paper illustrates how a net capacity cost metric – fixed costs minus net market revenues – might be better suited to evaluating supply options in China. Using a simplified example with recent resource cost data, the paper illustrates how, with a net capacity cost metric, solar PV and electricity storage may be a more cost-effective option for meeting demand growth than coal-fired generation. &nbsp

The building sector accounts for nearly 40% of global energy consumption. In Nigeria, more than two-thirds of the consumption comes from residential buildings. Energy efficiency measures through the adoption of insulation materials are tools that could crash the peak demand of energy in buildings while improving its thermal comfort and aerogel is considered as the most effective material for insulation, owing to its unique thermal properties. In this paper, we present the performance of aerogel as a thermal insulation material towards a sustainable design of residential buildings for tropical climates in Nigeria. First, a typical residential building in the tropical region was modeled with conventional materials utilized in the region and was later modified through the application of aerogel material on various surfaces of the model. A whole building energy simulation was then carried out in each variation and the outcome was compared to effectively conclude on the significance of aerogel in terms of thermal comfort improvement and energy consumption reduction. Results show that aerogel had the highest influence when inserted in the attic and floor slabs of the designed model. A reduction of more than 6% was attained in the recorded indoor mean air and operative temperatures while still maintaining an acceptable humidity range. Concerning energy consumption, a reduction of more than 15% was achieved. However, the high price of aerogel may hinder its application on the studied building but could be a good investment where climate change and sustainability are of high importance and less concern is given to expenditure. Aerogel demonstrated significant potential with respect to both thermal comfort improvement and energy consumption reduction on the designed model. The outcome of the study is hoped to serve as a base reference for the insulation of residential buildings with similar climate and characteristics to the adopted case building.

Phase change material (PCM)-based thermal energy storage (TES) can provide energy and cost savings and peak demand reduction benefits for grid-interactive residential buildings. Researchers established that these benefits vary greatly depending on the PCM phase change temperature (PCT), total TES storage capacity, system configuration and location and climate of the building. In this study, preliminary techno-economic performance is reported for a novel heat pump (HP)-integrated TES system using an idealized approach. A simplified HP-TES was modeled for 1 year of space heating and cooling loads for a residential building in three different climates in the United States. The vapor compression system of the HP was modified to integrate with TES, and all heat transfer to and from the TES was mediated by the HP. A single PCM was used for heating and cooling, and the PCT and TES capacity were varied to observe their effects on the building’s energy consumption, peak load shifting and cost savings. The maximum reduction in electric consumption, utility cost and peak electric demand were achieved at a PCT of 30 °C for New York City and 20 °C for Houston and Birmingham. Peak energy consumption in Houston, New York City, and Birmingham was reduced by 47%, 53%, and 70%, respectively, by shifting peak load using a time-of-use utility schedule. TES with 170 MJ storage capacity allowed for maximum demand shift from on-peak to off-peak hours, with diminishing returns once the TES capacity equaled the daily building thermal loads experienced during the most extreme ambient conditions.

This report considers the evidence for reducing heating system flow temperatures in Scottish housing stock by modelling suitability against external temperatures and considering upgrades required to make houses suitable.

Electric vehicles (EVs) are becoming a more prominent portion of Texas’s light duty vehicle (LDV) fleet as they become more attractive to consumers and as relevant governing bodies work to incentivize further adoption rates in an effort to reduce emissions within the transportation industry. Meanwhile, the Electric Reliability Council of Texas (ERCOT), the electric grid that services about 90% of the state’s residents, has been seeing increases in power demand. This has been due to factors such as a growing population, increased air conditioning use, and pushes for electrification across other industries, all factors that are expected to continue contributing to power demand increases within the foreseeable future. As more vehicles in the state are electrified, they will add further power demand increases on top of the existing contributing factors. This work focuses on evaluating different EV adoption, charging management, and policy scenarios in order to then evaluate how they may be expected to impact ERCOT, particularly regarding peak demand increase within two time horizons: one into 2030 and one into 2050. Peak demand is an important consideration because as it increases, it presents challenges for maintaining the electrical grid’s reliability when it exceeds generation capacity. After constructing and refining models which predict EV presence at the county level based on socio-economic and infrastructure related feature variables, the anticipated impacts of a growing EV fleet are quantified using historical data from ERCOT, planned installations and reserve margins, EV charging patterns, and travel patterns. Additionally, this work includes results from a collaboration which culminated in applying relevant EV fleet growth predictions to a DC-OPF simulation for Austin Energy, serving as a case study for the future of EV fleet impacts on relatively small-scale utilities. The results of this study showcase the fact that it is possible to accurately predict EV presence at the county level with 6 publicly available feature variables. In examining adoption pathways, it importantly finds that current incentives will very likely be insufficient for the achievement of the Biden Administration’s 50% market share goal by 2030. Should this market share goal come to fruition, however, it is expected to be manageable at the state level and within the Austin Energy case study regarding electricity supply in 2030. Sustained growth from this scenario, however, will necessitate ambitious charging management strategies in order to limit the potentially heavy impact of a growing EV fleet on peak demand looking forward into and beyond 2050.

The difference between load demand in the off-peak and peak period is usually high in the context of Nepal, and the high peak load for a short time is the main reason for insufficiency in electricity, frequent electricity outage, and poor power quality. This paper investigates the effectiveness of the time of use (TOU) strategy to reduce peak demand and to improve power quality. TOU electricity price is one of the electric demand response (DR) strategies, which may motivate the customers to reduce their consumption in peak periods and shift load in the off-peak period, which propel the electricity industry towards a higher efficiency compared to that of common flat prices. In this paper, a process based on an equal step length iteration algorithm is used to obtain an optimal period partition. Using the particle swarm optimization (PSO) algorithm, a TOU-based optimization model is proposed to find the optimal electricity price. Then, its impact on peak demand reduction and voltage fluctuation is analyzed. The impact of TOU price on peak demand reduction is investigated in the hourly load profile of Nepal, which indicates the significant reduction in peak demand. Similarly, the impact on voltage fluctuation is analyzed through a case study of the IEEE 33-bus radial distribution test system, which shows voltage profile is improved.

Increased electrification in the residential and transport sectors is changing the energy demand profiles significantly, which results in reshaped peak demand. These changes in demand profiles can cause grid overloading and jeopardize network reliability especially when the excessive use of electricity within a network is uncoordinated. In this article, an aggregated coordination mechanism is proposed for electric vehicle (EV) charge–discharge scheduling to manage the peak demand in the low-voltage (LV) residential networks. The proposed model uses mixed-integer-programming-based optimization approach to minimize the cost of energy while managing the peak demand and complying with grid constraints. A stochastic model is presented to account for the uncertainties associated with forecast inaccuracies of the day-ahead scheduling. The proposed strategy is assessed by means of simulation studies considering an LV residential neighborhood in Sydney, Australia. The results indicate the effectiveness of the proposed strategy to minimize the cost of electricity for the EV owners while managing the peak demand for the grid operators. Comparison with the state-of-the-art EV scheduling strategies indicates that the proposed strategy can improve the load factor of the local network up to 36%, the peak-to-average ratio up to 27%, and cost reductions up to 56%.

Recent research findings indicate that the non-monotonic consumption of energy from lithium-ion (Li-ion) batteries results in a higher heat generation in electrical energy storage systems. During peak demands, a higher heat generation due to high discharging current increases the temperature from 80 °C to 120 °C, thereby resulting in thermal runaway. To address peak demands, an additional electrical energy storage component, namely supercapacitor (SC), is being investigated by various research groups. This paper provides insights into the capability of SCs in lightweight electric vehicles (EVs) to address peak demands using the worldwide harmonized light-duty driving test cycle (WLTC) driving profile in MATLAB/Simulink at different ambient temperatures. Simulation results indicate that temperature imposes a more prominent effect on Li-ion batteries compared with SCs under peak demand conditions. The effect of the discharging rate limit on the Li-ion battery current is studied. The result shows that SCs can accommodate the peak demands for a low discharging current limit on the battery, thereby reducing heat generation. Electrochemical impedance spectroscopy and cyclic voltammetry are performed on SCs to analyze their thermal performance at different temperatures ranging from 0 °C to 75 °C under different bias values of −0.6, 0, 0.6, and 1 V, respectively. The results indicate a higher specific capacitance of the SC at an optimum operation temperature of 25 °C for the studied bias. This study shows that the hybrid combination of the Li-ion battery and SC for a light-weight EV can address peak demands by reducing thermal stress on the Li-ion battery and increasing the driving range.

Water utilities strive to achieve a sustainable reduction in per capita consumption (PCC) by optimising their peak demand management strategies. Socioeconomic (SE) and socio-demographic (SD) characteristics have been proven to correlate with PCC. However, the full extent to which these characteristics underpin peak demand and PCC is yet to be fully understood. Previous work used medium resolution smart meter data from 10,000 households to discover and characterise temporal consumption patterns that underpin peak demand, identifying four distinct clusters of households, namely "Evening Peak" (EP), Late Morning Peak (LM), Early Morning Peak (EM) and Multiple Peak (MP). Using survey results, "Acorn household classification", household occupancy and UK population and household attribute data, this study attempts to draw a correlation between the four clusters and known variables of the participating households. Results have revealed a strong correlation between many endogenous attributes (particularly housing, occupancy, age, number of children and household income) and households' consumption patterns underpinning peak demand. Some 56% of families in privately rented housing show EP characteristics compared with 22% owner-occupiers and 9% social renters. EP households with teenage boys have 37% higher per household consumption (PHC) than average, while EM families with teenage girls use 47% more water in early morning showers than average.

Over the last decade, the electricity sector has seen a significant increase in the number of residential battery systems, as well as increasing interest in using them to reduce demand during network peaks. Although there is an abundance of literature assessing this ability using modelled residential batteries, there is a lack of detailed assessment using deployed residential batteries. This paper analyses 1-min resolution data from 15 non-coordinated residential batteries deployed in Australia across 6 network peak demand periods. A novel metric was used to quantify errors in BESS load-following, which occurred when the batteries did not completely mitigate grid import and export even when they had sufficient energy capacity and rated power. On average the 15 batteries discharged around 25% of their rated power during network demand peaks, whereas those that load-followed discharged around 40%. Despite the small sample size, these results suggest that the outcomes from modelled batteries represent the ideal upper bound and the actual performance of some batteries is likely to be lower. This there is a need for more research into the actual operation of deployed batteries, and what this means for the current modelled findings regarding their ability to reduce demand during network peaks.

Ensuring power system reliability under high penetrations of variable renewable energy is a critical task for system operators. In this study, we use a loss of load probability model to estimate the capacity credit of solar photovoltaics and energy storage under increasing penetrations of both technologies, in isolation and in tandem, to offer new understanding on their potential synergistic effects. Increasing penetrations of solar PV alter the net load profile on the grid, shifting the peak net load to hours with little or no solar generation and leading to diminishing capacity credits for each additional increment of solar. However, the presence of solar PV decreases the duration of daily peak demands, thereby allowing energy-limited storage capacity to dispatch electricity during peak demand hours. Thus, solar PV and storage exhibit a symbiotic relationship when used in tandem. We find that solar PV and storage used together make a more significant contribution to system reliability: as much as 40% more of the combined capacity can be counted on during peak demand hours compared to scenarios where the two technologies are deployed separately. Our test case demonstrates the important distinction between winter and summer peaking systems, leading to significantly different seasonal capacity values for solar PV. These findings are timely as utilities replace their aging peaking plants and are taking energy storage into consideration as part of a low carbon pathway.

As our power grids transition toward a decarbonized energy mix, ensuring reliability and provision of grid services remains paramount. The power system has always been heavily influenced by the weather—extreme temperatures determine the timing of peak demand, winter cold snaps can limit natural gas supply, gas turbine reliability and output are affected by ambient conditions, and hydro output varies seasonally and annually. However, as the grid increasingly relies on variable renewable energy (VRE), like wind and solar, the attention to reliability and weather conditions is increasingly important. The implications of changing reliability are large. The Electric Reliability Council of Texas (ERCOT) rolling blackouts from earlier this year impacted millions of people across the state and could be seen from space ( Figure 1 ).

Peer-to-peer (P2P) transactive energy trading has emerged as a promising paradigm towards maximizing the flexibility value of prosumers’ distributed energy resources (DERs). Despite reinforcement learning constitutes a well-suited model-free and data-driven methodological framework to optimize prosumers’ energy management decisions, its application to the large-scale coordinated management and P2P trading among multiple prosumers within an energy community is still challenging, due to the scalability, non-stationarity and privacy limitations of state-of-the-art multi-agent deep reinforcement learning (MADRL) approaches. This paper proposes a novel P2P transactive trading scheme based on the multi-actor-attention-critic (MAAC) algorithm, which addresses the above challenges individually. This method is complemented by a P2P trading platform that incentivizes prosumers to engage in local energy trading while also penalizes each prosumer’s addition to rebound peaks. Case studies involving a real-world, large-scale scenario with 300 residential prosumers demonstrate that the proposed method significantly outperforms the state-of-the-art MADRL methods in reducing the community’s cost and peak demand.

The paper deals with the long-term and seasonal variability of low flows using the example of a mountain river. The study covers the Laborec River in the eastern part of Slovakia, and the main aim of the research is to identify and establish long-term fluctuations of low flows on this river. The analysis aims to indicate trends of low flows and seasonal variability of outflows based on various measures and research methods as well as the links between them. Basic data on daily flow and precipitation series were collected from 1980 to 2019. Low flow periods were identified in relation to the fitting of the threshold level method to the 70th and 95th percentile on the flow duration curve as a constant, multi-annual cut-off (Q70%, Q95%). The longest lasting flows were those below q70%, which were determined in the shallow cut-offs that occurred for most of the year, i.e. from June to December and in January. The greatest culmination of flows below q95% was in August and September. The range of minimal unit outflow is the smallest in the summer-autumn period and results from long periods without precipitation and with increased evapotranspiration. The highest range of unit outflow was recorded from December to April. Knowledge of low river flows should be one of the important elements of advanced planning, which in the future may help to reduce conflicts between water users during the peak demand period.

We determine the gains in efficiency accruing to a monopolist producer facing a non-linear market demand under a time-of-use (TOU) pricing structure as opposed to a flat rate pricing (FRP) structure. In particular, we consider the constant elasticity of demand function and the exponential demand function for this analysis. We estimate the price and quantity demanded for these two types of functions and optimize the profit earned by the producer. A comparison of the linear, exponential, and constant elasticity of demand functions shows that in cases of linear and exponential demand, the TOU pricing works to reduce the peak demand below the installed capacity and saves on additional investment and operation costs, while no such reduction takes place in the case of constant elasticity of demand. However, profit accruing to the monopolist under the TOU pricing structure exceeds that under FRP, irrespective of the form of the demand function. Thus, we conclude that regardless of the shape of the demand function, a time-varying pricing structure is better than the traditional FRP. Finally, we study some implications for the policy maker if such a pricing structure is implemented.

In this study, a nuclear hybrid energy system (NHES) with large-scale hydrogen storage integrated with a gas turbine cycle is proposed as a flexible system for load following. The proposed system consists of a nuclear reactor, a steam Rankine cycle, a hydrogen electrolyzer, a storage system for hydrogen in an underground salt cavern, and a Brayton cycle that uses hydrogen as fuel to generate additional electricity to meet peak demand. A dynamic mathematical model is developed for each subsystem of the NHES. To evaluate the potential benefits of the system, a one-year study is conducted, using scaled grid demand data from ISO New England. The dynamic simulation results show that the system is capable of meeting the demand of the grid without additional electricity from outside sources for 93% of the year, while decreasing the number of ramping cycles of the nuclear reactor by 92.7%. There is also potential for economic benefits as the system only had to ramp up and down 7.4% of the year, which increased the nuclear capacity factor from 86.3% to 98.3%. The simulation results show that the proposed hybrid system improves the flexibility of nuclear power plants, provides more electricity, and reduces greenhouse gas emissions.

Large customers in many electric distribution utilities must enter into demand contracts for the ensuing year for defining contracted demand. Customer demand charge equals contracted demand billed at contracted tariff if the peak demand is less than the contracted demand, and, if not, the excess is billed at the uncontracted tariff. Both scenarios lead to economic loss for the customer, as the uncontracted tariff is much higher than the contracted tariff. Further, optimization of demand contracts is also important for utilities, as they plan and operate their system to satisfy customer peak demand. If under planned, it leads to technical challenges, and otherwise, it leads to economic loss. This challenge of determining the best demand to be contracted is known as the demand cost optimization problem and would save US$ 38 billion globally to customers. This work describes the problem through a graphical approach and proposes three mathematical models to find the optimum demand even in the presence of intermittent renewable generation. Each model is verified through a case study and an exhaustive study with 7,000 large customers from a Brazilian utility. The formulations are easily implementable and have the potential to assist large customers and utilities with planning studies.

The concept of smart grid was introduced a decade ago. Demand side management (DSM) is one of the crucial aspects of smart grid that provides users with the opportunity to optimize their load usage pattern to fill the gap between energy supply and demand and reduce the peak to average ratio (PAR), thus resulting in energy and economic efficiency ultimately. The application of DSM programs is lucrative for both utility and consumers. Utilities can implement DSM programs to improve the system power quality, power reliability, system efficiency, and energy efficiency, while consumers can experience energy savings, reduction in peak demand, and improvement of system load profile, and they can also maximize usage of renewable energy resources (RERs). In this paper, some of the strategies of DSM including peak shaving and load scheduling are highlighted. Furthermore, the implementation of numerous optimization techniques on DSM is reviewed.

This study presents a privacy-aware stochastic multiobjective optimization framework that considers the objectives of both consumers and utility companies (UCs) in a demand response (DR) scheme. This framework enables a fair optimization of all the objectives together, and can be used to observe their effects on one another. Using this framework, we performed a comprehensive exploration of the relationships between the objectives of consumers and UCs. The uncertainty of photovoltaic energy sources is modeled by using a stochastic optimization approach. The objectives of the households include minimizing monetary cost and maximizing privacy and comfort. The UC's objectives include minimizing the peak demand and variation of the power demand. This study is the first to present a holistic analysis of privacy-aware DR by incorporating all five objectives of both supply and demand sides. The results reveal that near-optimal privacy performance is achievable with small compromises from the other objectives.

In analysis of generation reliability assessment in power systems, the effect of both forced outages, and uncertainties in load forecasting must be taken into consideration. In this paper, the analysis has been evolved from loss-of-load and loss-of-energy probability techniques. The student-t-distribution method is used to calculate the uncertainty of the forecast peak demand at specified intervals of confidence. The effects of the numbers and the forced outage rates of the generating units have been studied.

One to three years anticipation of monthly and weakly peak demand are required to: prepare maintenance schedules, develop power pooling agreements, select peaking capacity and provide data required certain reliability coordinating centers. The total monthly forecast of the maximum demand (m.d.) is deduced and computed along the future three years till April 1981. This is accomplished for a one of vital important electrical network in EGYPT. The anticipation is executed for El-Mehalla El-Kubra City network has an industrial and residential daily load characteristics Direct monthly m.d. forecasting is executed by separate treatment of non-weather (NW) and weather-induced (W) DEMAND. The forecast required is derived through this paper by two methodologies: the probabilistic extrapolation – correlation and that suggested by the authors Date collected for our work is the daily and monthly data for more reliable determination of weather load models. Complete analysis, discussions and comments on the results are exhibited. An entire comparison reveals an acceptable and reasonable percentage error obtained on applying the proposed methodology.

As electricity prices and environmental awareness increase, more customers are becoming interested in installing distributed renewable generation, such as rooftop photovoltaic systems. Yearly load profile data could become very relevant to these customers to help them to time efficiently and accurately determine optimal energy investments for these customers. A new multi-dimension objective-oriented clustering-based method (MOC) is developed to identify a set of typical energy and/or demand periods. These typical periods can then be used to quantify the yearly cost savings for various renewable energy investment options. The optimal investment option can be determined after examining the financial viability of each option. This method was applied to a real community case study to evaluate renewable energy generation and storage options under two tariff situations: energy only or peak demand. Simulation results show that the MOC method can guide renewable energy investment planning with significant computational time reduction and high accuracy, compared to iterative simulations using a year of electricity load data. This energy investment planning method can help enable informed distributed renewable energy investment practices.

Flexibility in electricity demand can be leveraged for demand side management (DSM) to enable aspects such as “demand following generation” and provide ancillary services to support grid integration of renewable and distributed energy resources. The upcoming connected devices in demand centers such as commercial buildings and households may be leveraged for this. However, the coordination among distributed demand centers in a scalable decentralized manner to achieve DSM objectives is still a challenge. In this article, a generalized hierarchical transactive energy based multiagent framework is proposed. This framework includes energy management demand agents (EMDAs) at the building level, which coordinate the operation of different appliances within the buildings. EMDAs also actively participate on day-ahead Walrasian market at the cluster of buildings level. In this market, the effect of wide-adoption of DSM on generation dispatch is also studied. A particular instance of this framework is also implemented in a cyber-test system consisting of standard industrial microcontroller platforms to emulate practical implementations via smart meters. Experimental results of the proposed system are included to demonstrate the possibility of avoiding disptach from expensive generation units by reduction in peak demand and providing demand response inherently.

This article proposes a two-stage optimization strategy for solving the voltage-var optimization (VVO) problem considering stochastic penetration of plug-in electric vehicles (PEVs) to unbalanced, three-phase power distribution networks (PDN). The optimization strategy considers the prospect of forced active power curtailment at a minimized level, bidirectional PEVs activities. It also includes simple tap operations of shunt capacitor banks, online tap changing transformers, and voltage regulators to achieve optimal economic gain while satisfying the VVO operational constraints. The first stage aims for the optimal decomposition of the PDN into well-defined, cross-checked smaller partitions via a proposed community-based detection particle swarm optimization algorithm. The second stage incorporates a mixed-integer linear programming formulation to solve the VVO problem per partition level. This is done considering the nonlinearity and nonconvexity of the electrical system via applied approximation. This reduces the complexity and computational burdens that usually arise in solving the problem on a larger scale. The proposed two-stage strategy was tested on the modified IEEE 123 bus system with various case scenarios. Economical operation of the PDN is achieved during peak demand hours while maintaining a safe operation within the VVO problem.

With the increasing number of electricity consumers, production, distribution, and consumption problems of produced energy have appeared. This paper proposed an optimization method to reduce the peak demand using smart grid capabilities. In the proposed method, a hybrid Grasshopper Optimization Algorithm (GOA) with the self-adaptive Differential Evolution (DE) is used, called HGOA. The proposed method takes advantage of the global and local search strategies from Differential Evolution and Grasshopper Optimization Algorithm. Experimental results are applied in two scenarios; the first scenario has universal inputs and several appliances. The second scenario has an expanded number of appliances. The results showed that the proposed method (HGOA) got better power scheduling arrangements and better performance than other comparative algorithms using the classical benchmark functions. Moreover, according to the computational time, it runs in constant execution time as the population is increased. The proposed method got 0.26 % enhancement compared to the other methods. Finally, we found that the proposed HGOA always got better results than the original method in the worst cases and the best cases.

Background:Hospitals are expected to operate at a high performance level even under exceptional conditions of peak demand and resource disruptions. This understanding is not mature yet and there are wide areas of possible improvement. In particular, the fast mobilization and reconfiguration of resources frequently result into the severe disruption of elective activities, worsening the quality of care. This becomes particularly evident during the on-going coronavirus disease 2019 (COVID-19) pandemic. More resilient resource allocation strategies, that is, which adapt to the dynamics of the prevailing circumstance, are needed to maximize the effectiveness of health-care delivery. In this study, a simulation approach was adopted to assess and compare different hospital’s adaptive resource allocation strategies in responding to a sudden onset disaster mass casualty incident (MCI).Methods:A specific set of performance metrics was developed to take into consideration multiple objectives and priorities and holistically assess the effectiveness of health-care delivery when coping with an MCI event. Discrete event simulation (DES) and system dynamics (SD) were used to model the key hospital processes and the MCI plan.Results:In the daytime scenario, during the recovery phase of the disaster, a gradual disengagement of resources from the emergency department (ED) to restart ordinary activities in operating rooms and wards returned the best performance. In the night scenario, the absorption capacity of the ED was evaluated by identifying the current bottleneck and assessment of the benefit of different resource mobilization strategies.Conclusions:The present study offers a robust approach, effective strategies and new insights to design more resilient plans to cope with MCIs. It becomes particularly relevant when considering the risk of indirect damage of emergencies, where all the available resources are shifted from the care of the ordinary to the “disaster” patients, like during the on-going COVID-19 pandemic. Future research is needed to widen the scope of the analysis and take into consideration additional resilience capacities such as operational coordination mechanisms among multiple hospitals in the same geographic area.

This article presents two low bandwidth distributed model predictive control (MPC) based algorithms for the coordinated control of residential energy storage (ES) to mitigate overvoltage and reduce peak demand along LV radial distribution feeders. Each ES unit consists of a low level controller (LLC) that utilises MPC with three distinct objective functions that can optimise for financial benefit, voltage regulation and peak shaving. These modes are activated via binary signals sent by the high level controller (HLC). The HLC, operating at the feeder level, determines if any network violations such as overvoltage or overloading are predicted to occur. Using inter-bus $\boldsymbol{P}$ , $\boldsymbol{|V|}$ and $\boldsymbol{\Delta P}$ data, the HLC determines the minimum ES units to dispatch to mitigate these issues. The result is a very low bandwidth distributed control architecture with low computational complexity, that can achieve acceptable network operating conditions while dispatching the minimum number of ES units. The proposed distributed MPC algorithms were shown to outperform decentralised control strategies for voltage regulation and peak shaving from the Australian Standards and also had significantly less computational requirements than a centralised MPC controller to achieve the same network overvoltage and peak load objectives.

This study aims to develop a general framework for predicting the duration of the Turning Period (or Turning Phase) for the COVID-19 outbreak in China that started in late December 2019 from Wuhan. A new concept called the Term Structure for Turning Period (instead of Turning Point) is used for this study, and the framework, implemented into an individual SEIR (iSEIR) model, has enabled a timely prediction of the turning period when applied to Wuhan's COVID-19 epidemic, and provided the opportunity for relevant authorities to take appropriate and timely actions to successfully control the epidemic. By using the observed daily COVID-19 cases in Wuhan from January 23, 2020 to February 6 (and February 10), 2020 as inputs to the framework it allowed us to generate the trajectory of COVID-19 dynamics and to predict that the Turning Period of COVID-19 outbreak in Wuhan would arrive within one week after February 14. This prediction turned out to be timely and accurate, which has provided adequate time for the government, hospitals and related sectors and services to meet peak demand and to prepare aftermath planning. We want to emphasize that emergency risk management entails the implementation of an emergency plan, where timing the Turning Period is key to express a clear timeline for effective actions. Our study confirms the observed effectiveness of Wuhan's Lockdown and Isolation control program imposed since January 23, 2020 to the middle of March, 2020 and resulted in swiftly flattened epidemic curve, and Wuhan's success offers an exemplary lesson for the world to learn in combating COVID-19 pandemic.

Although developing countries typically deal with the challenge of mobilizing investments for adding new generation and transmission capacity to cope with high demand growth, there are often opportunities to enhance operation of existing generators and extract significant cost reduction as well as the ability to meet greater demand. This is particularly relevant for the Nigerian power system that has 13 GW of installed capacity but could meet only 5.2 GW of peak demand in 2018 at an average capacity utilization of 27% of its thermal (mostly gas) generation fleet. There are simple steps that can be adopted to restore merit-order based dispatch that can immediately yield an operational cost reduction. Increased plant availability, gas supply to power plants, and removal of uneconomic take-or-pay obligation are increasingly onerous tasks, but with commensurate significantly higher payoffs. We have used a dispatch analysis model to show that merit-order based dispatch within the current constraints can yield an annual benefit of $29 million or 4% of variable costs. With the removal of physical availability constraints around plants and gas supply, benefits increase by an order of magnitude to $309 million, or 19% of total annual costs. If it is possible to get rid of the uneconomic take-or-pay obligations, benefits would further increase to $579 million or over 30% of total annual costs. These are major findings especially if we consider these in the context of tremendous financial stress in the power sector. Although we do not estimate the cost of implementing these measures, there are very little hard investments needed in some of these cases such as establishing a strict merit-order based dispatch or enhancing operational practices to improve plant and gas availability. There are indeed difficult commercial and institutional issues when it comes to renegotiating take-or-pay contracts, albeit the massive benefits in excess of half a billion dollar per year should make it a worthwhile undertaking. Removal of operational and commercial constraints could also allow the Nigerian power system to meet increased demand to the benefit of unserved residents or businesses, and pave the way towards increased penetration of variable renewable energy (VRE) resources. We have shown that an addition of 500 MW solar capacity in the current system will earn reasonable return on investment between 4% and 9% and help to meet increased demand. However, an important precondition for adding large-scale solar is to raise the spinning reserve capability in the system through additional investments in flexible resources, namely, open cycle gas turbines (OCGT), pumped storage hydro or battery storage systems.

This article investigates the need of coordinated operation of multiple voltage regulation devices under different time scales to enhance energy efficiency via conservation voltage reduction (CVR) in distribution networks in the presence of high photovoltaic (PV) penetration. A multistage multiobjective Volt/VAR control strategy for smart grid-enabled CVR has been proposed. In order to maximize the benefits of CVR, the method has been implemented in multiple stages of slow and fast time scale control periods for planning and real-time operations, respectively. The stochastic multiobjective Volt/VAR optimization considering uncertainty and forecasting errors has been formulated for the planning stage. However, in real-time operation, an adaptive Volt/VAR droop-controlled approach has been proposed for fast time controllers to mitigate voltage fluctuations during the sudden change in PV active power output. Besides, the economic impact of CVR in terms of cost savings has been investigated. The proposed control algorithms have been validated in real-time co-simulation platform. The simulated results demonstrate a higher reduction in peak demand, daily energy demand, and system losses. Further, a higher reduction in power purchase cost has been achieved in the presence of PV system with CVR in comparison to PV alone. Moreover, the proposed control algorithms work well in real-time operation under intermittency in PV power output also.

A Robotic Mobile Fulfillment System (RMFS) is an automated parts-to-picker material handling system, in which robots carry pods with products to the order pickers. It is particularly suitable for e-commerce order fulfillment and can quickly and frequently reallocate workers and robots across the picking and replenishment processes to respond to strong demand fluctuations. More resources for the picking process means lower customer wait times, whereas more resources for the replenishment process means a higher inventory level and product availability. This paper models the RMFS as a queuing network and integrates it within a Markov decision process (MDP), that aims to allocate robots across the pick and replenishment processes during both high and low demand periods, based on the workloads in these processes. We extend existing MDP models with one resource type and one process to an MDP model for two resources types and two processes. The policies derived from the model are compared with benchmark policies from practice. The results show that the length of the peak demand phase and the height of the peak affects the optimal policy choice. In addition, policies that continually reallocate resources based on the workload outperform benchmark policies from practice. Moreover, if the number of robots is limited, continual resource reallocation can reduce costs sharply. The results show that optimal dynamic policies can reduce the cost by up to 52.18% on average compared to optimal fixed policies.

The design of the water distribution networks in a given region must guarantee 24-hour supply, meeting the times of greatest demand, defined as factors of peak consumption. Thus, it is important that water reaches users effectively, ensuring adequate quantity and quality for carrying out daily activities. The premise then is the average flow demanded by the population, and the fluctuations that may occur from this value, weighted from the dimensionless peak coefficients K1 (coefficient of the day of greatest consumption) and K2 (coefficient of the hour of greatest consumption). In this paper, these coefficients are calculated from both water consumption data and from the application of empirical equations. These values were compared with those suggested by the NBR 12218/2017 standard, which suggests K1 = 1.2 and K2 = 1.5 in the absence of water consumption data. However, some surveys that assessed water consumption in three regions based on water consumption data for three to four years indicated that the peak coefficients recommended by the standard may lead to undersizing of the supply network. In one of the cases assessed, the values of K1 and K2 respectively corresponded to 2.19 and 4.95. Results of two studies previously developed at the Faculty of Civil Engineering, Architecture and Urbanism of the State University of Campinas (FEC-UNICAMP) were used for the calculation based on water consumption data. These studies defined the peak water consumption coefficients for the same three regions examined by the present research: Parque Jambeiro and Parque Oziel, located in the city of Campinas (third most populous city in the State of São Paulo, with an estimated 1,204,703 inhabitants in 2019), and Jardim América II, located in the municipality of Várzea Paulista, in the interior of the State of São Paulo, based on data provided by the water provider. The analyses carried out considered that the peak water consumption factor Cp is given by the product of K1 and K2. Seven empirical equations, available in the specific literature and developed in different locations, were used. As the empirical equations were developed in different regions, the average of the results obtained through these equations was used in order to reduce the existing uncertainties, related, for example, to the socioeconomic profile and climate, parameters that vary according to the region of study. As a result, this research shows that in all the neighborhoods observed, the normative suggestion for the Cp value was below those obtained by applying the empirical equations in all the years used to calculate the coefficient. So, there was no year in which the normative reference was sufficiently adequate to describe water consumption. Furthermore, the evaluation using water consumption data resulted in a peak coefficient equivalent to 240% of the normative suggestion (2.4 times higher) whereas the empirical equations suggest the adoption of a value corresponding to 200% of the indicated value by the standard (twice as high). It was also found that the computation of Cp through empirical equations resulted in values 1.55 times higher than those obtained from the water consumption data. As the calculation of the design flow depends directly on the peak consumption coefficient, the use of smaller values leads to lower design flow and, consequently, to undersized network diameters. As a direct consequence, there are greater head losses at times of higher flow. This situation should result in a lack of water in peak consumption days and times, so as not to serve the population continuously, which is a premise of the public supply system. Future research may focus on expanding the number of regions evaluated in the comparison between different ways to calculate the peak water consumption coefficients, and it is also possible to explore the evaluation of water consumption data to determine the Cp value and the subsequent correlation with local factors such as resident population, supplied area, typical climate, among other factors, adding value to the results already existing in the specific literature, and also expanding the knowledge related to the dimensioning of water distribution networks. O dimensionamento das redes de distribuição de água em uma determinada região deve garantir o suprimento de água 24 horas por dia, antendendo os horários de maior demanda, definidos como fatores de pico de consumo. Desta forma é importante que a água chegue aos usuários de maneira eficaz, garantindo a quantidade e a qualidade adequadas para a realização das atividades necessárias. Tem-se então como premissa a vazão média demandada pela população, e as flutuações que podem ocorrer deste valor, ponderadas a partir dos coeficientes adimensionais de pico K1 (coeficiente do dia de maior consumo) e K2 (coeficiente da hora de maior consumo). Neste trabalho, estes coeficientes foram calculados através de dados de consumo hídrico e também através da aplicação de equações empíricas. Estes valores foram comparados com os valores sugeridos pela norma NBR 9649/1986, que indica o uso dos valores K1 = 1,2 e K2 = 1,5 na falta dos dados de consumo hídrico. Entretanto, algumas pesquisas que avaliaram o consumo hídrico de três regiões com base nos dados de consumo hídrico de três a quatro anos indicaram que os coeficientes de pico recomendados pela norma podem incorrer em subdimensionamento. Em um dos casos avaliados, os valores de K1 e K2 respectivamente corresponderam a 2,19 e 4,95. Resultados de dois estudos previamente desenvolvidos na Faculdade de Engenharia Civil, Arquitetura e Urbanismo da Universidade Estadual de Campinas (FEC-UNICAMP) foram utilizados para os cálculos baseados em dados de consumo hídrico. Estes estudos definiram os coeficientes de pico de consumo hídrico para as mesmas três regiões examinadas pela presente pesquisa: Parque Jambeiro e Parque Oziel, localizados no município de Campinas, e Jardim América II, situado no município de Várzea Paulista, interior do Estado de São Paulo, com base em dados fornecidos pelas concessionárias. As análises realizadas levaram em consideração que o coeficiente de pico de consumo hídrico Cp é dado pelo produto entre K1 e K2. Sete equações empíricas, disponíveis na literatura específica e desenvolvidas em diferentes localidades, foram utilizadas. Como as equações empíricas foram desenvolvidas em diferentes regiões, foi utilizada a média dos resultados obtidos através destas equações, a fim de diminuir as incertezas existentes, relacionadas, por exemplo, ao perfil socioeconômico e ao clima, parâmetros que variam de acordo com a região de estudo. Como resultado, este trabalho mostra que em todos os bairros observados a sugestão normativa para o valor de Cp ficou abaixo dos valores obtidos pela aplicação das equações empíricas e pela avaliação dos dados de consumo hídrico, em todos os anos aos quais se referiram os estudos. Desta maneira, a referência normativa não foi suficientemente adequada para descrever o consumo hídrico em nenhum dos anos avaliados. Ademais, a avaliação por meio dos dados de consumo hídrico indicou um coeficiente de pico equivalente a 240% da sugestão normativa (2,4 vezes maior) ao passo em que as equações empíricas sugerem a adoção de um valor correspondente a 200% do valor indicado pela norma (duas vezes maior). Constatou-se ainda que o cômputo de Cp por meio de equações empíricas resultou em valores 1,55 vezes maiores do que os dados de consumo hídrico indicaram. Como o cálculo da vazão de projeto depende diretamente do coeficiente de pico de consumo, e o uso de valores menores leva evidentemente a vazões de projeto menores. Deste modo, menores diâmetros dimensionados das redes de abastecimento serão determinados e, por consequência direta, maiores perdas de carga serão verificadas nos horários de maior vazão. Esta situação pode resultar em falta de água nos dias e horários de pico de consumo, de forma a não atender a população de forma contínua, que é uma premissa do sistema de abastecimento público. Futuras pesquisas podem se concentrar em ampliar o número de regiões avaliadas na comparação entre diferentes fontes de obtenção de coeficientes de pico de consumo hídrico, podendo-se também explorar a avaliação dos dados de consumo hídrico para determinação do valor de Cp e posterior correlação com fatores locais como população residente, área de abastecimento, clima típico, entre outros fatores, agregando valor aos resultados já existentes na literatura específica da área, e ampliando também o conhecimento relacionado ao dimensionamento de redes de distribuição de água.

Optimal planning and dispatch for satisfying peak demand are challenging, especially for microgrids with significant penetration of renewable energy. In extreme cases such as weather contingencies, microgrid operators may resort to load shedding. Alternatively, conservation voltage reduction (CVR) can be used to decrease the power and energy demand across the network. In this article, a mixed-integer linear programming problem has been formulated for a droop-based microgrid to implement network-wide CVR along with minimal line losses. For planning purpose, optimal sizing and placement of distributed generators (DG) is performed initially. Afterwards, a day-ahead dispatch is generated for these DGs for a realistic hourly load profile considering residential and industrial loads. Besides, network reconfiguration concept has been explored to boost the demand reduction. Furthermore, the effect of using a constant impedance-current-power (ZIP) load model instead of considering the classic constant impedance load model has been tested. The effectiveness of the proposed optimization has been tested on 33 and 69 bus islanded microgrids. The obtained results demonstrate the accuracy of the proposed planning and dispatch formulation, and validate its utility in reducing the power and energy consumption while attracting very low line losses.

This study evaluates the efficiency of phase change materials (PCMs) in improvements in thermal performance and thermal comfort of a residential building. The heat transfer of concrete containing PCM, which has been experimentally examined, was numerically modeled and validated in this study. PCMs with melting temperatures ranging from 19 to 29 °C and thicknesses of 5 and 10 mm were applied in different building elements. After finding the optimum PCM with respect to the energy analysis, the impacts of the meteorological parameters and cooling and heating loads were evaluated. The experimental results were in a good agreement with the EnergyPlus PCM module in the numerical model. The results indicated that models integrated with PCM are able to improve the indoor comfort and to reduce the heating and cooling loads and temperature fluctuations. The PCM with a melting temperature of 21 °C and thickness of 10 mm positioned in the roof and wall showed the best performance in the energy consumption and transfering the loads away from the peak demand times. The environmental analysis indicated that the total CO2 emission reduction would be about 264 tone when PCM with 10 mm thick is applied to a building with a life span of 50 years. The shortest payback period for building using PCM-concrete was 16.6 years.

Increasing numbers of customers intend to install batteries with PV to manage their power bill or even go off-grid. To encourage the sharing of locally generated renewable energy and suburban customers to stay on grid, a novel grid friendly neighborhood energy trading mechanism (NET) has been developed. The NET is intuitive as it uses the existing network topology as the trading medium for scalability with low computation and communication requirements. The NET is designed to account for three technical goals: reflecting losses, meeting network flow constraints, and regulating voltages A MV-LV distribution network case study shows that NET effectively achieves the three goals with low communication and fast convergence. This NET mechanism helps to enable grid friendly neighborhoods to share locally generated renewable energy in a market context.

Electrical peak load demand all over the world is always anticipated to grow, which is challenging electrical utility to supply such increasing load demand in a cost effective, reliable and sustainable manner. Thus, there is a need to study some of load management (LM) techniques employed to minimize energy consumption, reduce consumers' electricity bills and decrease the greenhouse gas emissions responsible for global warming. This paper presents a review of several recent LM strategies and optimization algorithms in different domains. The review is complemented by tabulating several demand side management (DSM) techniques with a specific view on the used demand response (DR) programs, key finding and benefits gained. A special focus is directed to the communication protocols and wireless technology, incorporation of renewable energy resources (RERs), battery energy storage (BES), home appliances scheduling and power quality applications. The outcome of this review reveals that the real time pricing (RTP) is the most efficient price-based mechanism program (PBP), whilst time of use (TOU) is the basic PBP and easiest to implement. Energy efficiency programs have proved the highest influential impact on the annual energy saving over the other dynamic pricing mechanism programs. Through a forecasted proposal of future study, DSM proved tremendous potential annual energy savings, peak demand savings, and investment cost rates within different consumption sectors progressively up to year 2030.

As the use of clean energy such as wind power and nuclear power has been increasing, the base load operation of nuclear power units usually means huge pressure for local power systems in regulating peak demand. In order to use more regulation resources, a multi-area joint optimization model involving peak regulating of nuclear power is proposed. Match performance of peak load regulating of wind power and nuclear power is also examined according to their output characteristics. Furthermore, aiming to make nuclear power constraint linearize, a method is introduced herein to subdivide the depth of peak load regulating of nuclear power. On the other hand, considering the power constraints of AC/DC transmission channel, wind curtailment cost and the operating cost of peak load regulating of nuclear power, a joint scheduling model with complementary energy sources in multi-area is established, which can effectively reduce the adverse impact of negative peak regulating of wind power and enhance consumption of clean energy. Finally, China’s inter-regional interconnected power system is introduced to validate the effectiveness of the proposed model.

Demand Side Management (DSM) is a very important tool that converts a consumer's passive role to an active one by changing energy consumption and helps the energy utilities to decrease the peak demand and reshape the load curve. This paper presents a detailed analysis of the impacts of three of the most important DSM techniques which are energy efficiency, direct load control, and shifting load through two cases which are one day in summer and one in winter. The effectiveness of the three techniques is demonstrated on average daily consumption of an Egyptian residential house in Shebin El-Kom, Menoufia Governorate. The desired objectives are reducing the energy consumption, electricity bill, and minimizing peak to average ratio (PAR). The obtained results confirm that applying DSM techniques has significant potential in terms of financial savings for the consumers and utility provider, and a cleaner environment without any impact on service quality or customer satisfaction.

Modern data analytics systems use long-running executors to run an application’s entire DAG. Executors exhibit salient time-varying resource requirements. Yet, existing schedulers simply reserve resources for executors statically, and use the peak resource demand to guide executor placement. This leads to low utilization and poor application performance. We present Elasecutor, a novel executor scheduler for data analytics systems. Elasecutor dynamically allocates and explicitly sizes resources to executors over time according to the predicted time/varying resource demands. Rather than placing executors using their peak demand, Elasecutor strategically assigns them to machines based on a concept called dominant remaining resource to minimize resource fragmentation. Elasecutor further adaptively reprovisions resources in order to tolerate inaccurate demand prediction and reschedules tasks to deal with inadequate reprovisioning resources on one machine. Testbed evaluation on a 35-node cluster with our Spark-based prototype implementation shows that Elasecutor reduces makespan by more than 36% on average, and improves cluster utilization by up to 55% compared to existing work.

Система автоматичного частотного розвантаження (АЧР) є одним із основних засобів, який широко застосовується в енергосистемах для стримування швидкого падіння частоти. Система АЧР здатна за мілісекунди відключити частину споживачів, а за секунди – зупинити падіння частоти в районах енергосистеми, які утворилися в результаті каскадної аварії з відключення ліній і генераторів. Для підтримки тимчасового балансу активної потужності в аварійних ситуаціях в енергосистемі повинна бути передбачена кількість навантаження на відключення. Тому національні оператори систем передачі або мережа операторів систем передачі електроенергії встановлюють так званий загальний обсяг розвантаження. Найчастіше в стандартах та нормативних документах енергосистем цей показник розраховують для загального пікового попиту і рекомендують розмістити в енергосистемі рівномірним географічним способом. Такий спосіб розміщення загального обсягу розвантаження не враховує структуру електромережі, добової та сезонної зміни потужностей генерації і споживання, незважаючи на те, що стандарти, нормативні документи вимагають це враховувати. В роботі запропоновано математичну модель і спосіб визначення загального обсягу розвантаження системи АЧР і його розміщення в мережі енергосистеми з урахуванням розподілених (у вузлах споживачів) відновлюваних джерел енергії, ймовірних варіантів аварійного поділу енергосистеми, вимог міжнародних стандартів та нормативних документів, що регулюють функціонування систем протиаварійного захисту в галузі електроенергетики. Модель являє собою задачу цілочисельного (бінарного) лінійного програмування, що здійснює вибір оптимального за критерієм категорійності набору пристроїв АЧР, які розміщені у наперед заданих вузлах енергосистеми і спрацювання яких забезпечує баланс потужності в аварійних районах її поділу. Електричні параметри усталених режимів, а також ефективність оптимального обсягу і розподілу розвантаження в мережі визначаються і перевіряються (верифікуються) у серії апріорних та апостеріорних розрахунків на точних математичних моделях, визнаних у світовій практиці програмних продуктів електроенергетики. Отриманий таким чином розподіл обсягів розвантаження підвищує імовірність балансування якнайбільшої кількості аварійних районів енергосистеми за умови задоволення вимог щодо частотно-часової зони відповідних перехідних процесів. Бібл. 9, табл. 3, рис. 2.

Residential electrical peak demand has attracted much attention as this demand is unpredictable. Several approaches have been used in the literature to analyze residential electrical demand. Howeve...

Water is the most valuable source for human life. Egypt is one of the driest countries facing an enormous shortage of potable water especially with the population increase and limited water resources. It is becoming aware of the need to preserve water and sustainable water management. Potable water supply pipes are important for delivering water to consumers and serving as the last process between the treatment plant and the consumer. Water demand can vary periodically over the course of the day and for the same average daily demand, the peak value of the demand pattern can vary. These variations in peak demand can have significant effect on the hydraulic behavior of pipes network, water quality, and energy consumption through it. A significant amount of cost is spent on energy consumption in the water supply pipes network. In this research, the effect of water demand pattern variation on hydraulic behavior, water quality and energy consumption in water supply pipes network is studied. EPANET2 software was used to perform the hydraulic analysis through extended period simulation through a hypothetical water supply pipes network. Five patterns of demand variation were adopted in this analysis called pattern demand for big city, village, rural area, industrial zone and hotels. It was found that the type of demand pattern has a significant effect on velocity through the different pipes and pressure head of the different nodes and chlorine decay through the network.

The prediction of electricity consumption is a vital foundation for smart energy management. Since the consumption of power varies with different appliances, better forecasting of power and peak demand is an essential accomplishment for the proper planning and development of the power generation and distribution system. This forecast analysis helps the service providers and the government to understand the lifestyle of the customers. The existing prediction and forecasting models are not meeting the standard requirements and moreover difficult to apply in practice. The forecast says that the boom of electric vehicles will increase the demand of electricity globally by 3% for the upcoming year. There exists a number of machine learning algorithms for classification and decision making. But the accuracy of the exiting methods have shown inferior performance in terms of prediction which leads to inefficient decision making in the quantity of electricity generation. This paper proposes the use of random forest supervised learning model to forecast the consumption of power and identify the level of peak demand. The large smart meter dataset collected at varying seasons of the year is fed to the random forest classifier technique for better analysis and forecasting. This approach outperforms in terms of accuracy, stability and generalization. In addition, this paper investigates the existing models and compares the performance with those models. The performance analysis shows that this model performs better than the other investigated models with performance accuracy of 95.67% and enhanced accuracy of precision and recall.

Ridesourcing services from transportation network companies, like Uber and Lyft, serve the fastest growing share of U.S. passenger travel demand.1 Ridesourcing vehicles' high use intensity is economically attractive for electric vehicles, which typically have lower operating costs and higher capital costs than conventional vehicles. We optimize fleet composition (mix of conventional vehicles (CVs), hybrid electric vehicles (HEVs), and battery electric vehicles (BEVs)) and operations to satisfy demand at minimum cost and compare findings across a wide range of present-day and future scenarios for three cities. In nearly all cases, the optimal fleet includes a mix of technologies, HEVs and BEVs make up the majority of distance traveled, and CVs are used primarily for periods of peak demand (if at all). When life cycle air pollution and greenhouse gas emission externalities are internalized via a Pigovian tax, fleet electrification increases and externalities decrease, suggesting a role for policy. Externality reductions vary from 10% in New York (where externality costs for both gasoline and electricity consumption are relatively high and a Pigovian tax induces a partial shift to BEVs), to 22% in Los Angeles (where high gasoline and low electric grid externalities lead a Pigovian tax to induce a near-complete shift to BEVs).

INTRODUCTION: Spikes in the demand for blood components represent a substantial challenge to transfusion services. Simple metrics for characterizing volatility in blood components within the hospital transfusion service have not been established. METHODS: We measured the volatility of demand for blood services at a large academic urban general hospital over a 6-month period from July 2019 to December 2019 prior to the SARS-CoV2 pandemic. RESULTS: Among 4416 consecutive hours assessed, there were 693 h (16%) with spikes in demand for blood components with a mean (sd) of 3.8 (2.7) spikes/day. Spikes in demand were frequently clustered. The median number of hours between spikes differed by shift (6 h for days; 3 h for evenings; 3 h for nights). The percentage of shift hours with demand spikes also differed (9% day; 19% evening; 18% night). During the study, 32,447 components were distributed to 19,431 patients. Of these, 11,819 components (36%) were distributed during hours of peak demand. Hours with a simultaneous spike in both component demand and patient demand occurred in 5% of hours or approximately once each day. CONCLUSION: Demand for transfusion services was highly volatile in an unpredictable fashion. We provide an approach that could be used to benchmark spikes in demand for blood services at hospitals. Consideration of the frequency, unpredictability, and magnitude of spikes in demand may be relevant for hemovigilance programs and for strategies to determine the laboratory staffing needed for good patient care.

Off-grid systems, and mini-grids in particular, are expected to play a significant role in improving electricity access to one billion people until 2040. One of the major challenges for mini-grids is associated with their high costs, low financial viability and local development impact. Productive use of electricity can be an important driver of local development and impacts the total load in a mini-grid. By using a mixture of high-resolution (minutes) measurements and long-term data (years) on electricity expenditures and purchased electricity from a mini-grid in the Tanzanian highlands, we analyse the technical and economic impact from household and productive use of electricity, respectively. The high-resolution data is analysed using performance indicators and the long-term data using regression tools. We find that a mixture of household use and productive use of electricity provides both technical and economic benefits for the operator. In addition, we find that while productive use customers only represent 25% of the customers, they generate 44% of the operator's income. Furthermore, productive use of electricity customers are also likely responsible for the peak demand in the mini-grid, which occurs during day time. Lastly, we find empirical evidence suggesting that expenditures and demand are unit elastic, which has implications on economic policies for supporting rural electrification.

Este documento expone una investigación que tiene como objetivo aportar a la mitigación del cambio climático, además de buscar autonomía energética basándose en Energías Renovables. Por tal motivo esta investigación evalúa la prefactibilidad de un sistema de almacenamiento de energía renovable basado en bombeo de agua en la isla San Cristóbal - Galápagos, donde se tiene una fuente de energía almacenada debido a la diferencia de nivel disponible en la laguna El Junco. Se realizan los cálculos en base a la curva de demanda de la isla durante el año 2018, contando con que se desea cubrir el pico de dicha demanda de energía de la noche que se encuentra entre las 16h00 y las 22h00. Se calculan los tanques de almacenamiento de acuerdo al volumen requerido además de las características que deben tener los generadores de energía. Para el retorno del agua usada en la generación se calcula un sistema de bombeo en base a la energía obtenida por medio del recurso eólico de San Cristóbal, mismo que corresponde a 3 aerogeneradores de los cuales se analiza su curva de potencia, y junto con la velocidad promedio del viento por hora durante el año 2018, se obtiene la energía entregada por los aerogeneradores para realizar el bombeo requerido, además de las características de las bombas. Los resultados muestran que la propuesta es viable en vista de que se cubre la demanda del pico de la noche, considerando que la energía usada es netamente renovable.