Smart energy management is critical for reducing household electricity costs and addressing rising demand. While appliance scheduling is often explored under dynamic pricing schemes, this study demonstrates its effectiveness under Inclining Block Rate (IBR) tariffs—commonly used in developing countries and currently applied in Egypt. This paper proposes a Mixed-Integer Quadratic Programming (MIQP) model for optimal appliance scheduling, incorporating user-defined daily budget limits, utility preferences, and photovoltaic (PV) integration under net metering. The model is validated using synthetic test cases and empirical data from a household survey, alongside actual solar generation profiles. All analyses reflect Egypt’s residential IBR tariff structure and PV feed-in rates as of 2023. Results show that low-income households (350 kWh/month) with a 2.5 kW PV system achieve full energy self-sufficiency under a 5 EGP/day budget, generating a surplus. Without PV, the same group meets essential needs under a 15 EGP/day budget with a net expense of 11.55 EGP/day. High-income households (1000 kWh/month) reach maximum utility under a 60 EGP/day budget. These findings confirm that appliance load scheduling is viable and impactful under IBR tariffs. Sensitivity analysis results demonstrate that beyond a certain threshold, increasing the daily budget limit yields no further gains in user satisfaction. This highlights the optimal budget levels households should aim for under the IBR tariff. The model offers a scalable, budget-aware framework to support smart energy management and PV adoption across Egyptian households and similar emerging-economy contexts.

This paper presents a comprehensive techno-economic and environmental assessment of offshore wind-powered green hydrogen production systems in Egypt and Oman, two strategically located countries within the MENA region with substantial renewable energy potential. A 120 MW offshore wind farm configuration, employing Vestas 8 MW turbines, was simulated for each country and coupled with an electrolyzer system to evaluate electricity generation, hydrogen yield, system efficiency, and cost-effectiveness. The analysis shows that both Egypt and Oman achieve high annual capacity factors (51% and 49.7%, respectively), resulting in annual green hydrogen production of 11.5 million kg and 11.2 million kg. Despite Egypt’s more stable year-round wind profile and slightly lower Levelized Cost of Hydrogen (LCOH: $3.09/kg vs. $3.17/kg), Oman’s seasonal monsoon (Khareef) offers exceptional summer productivity, with peak capacity factors exceeding 74%. A dual-framework environmental assessment was conducted to quantify CO2 emissions mitigation. In the first scenario, based on grid substitution, the systems could avoid up to 240,000 and 256,000 tonnes of CO2 annually in Egypt and Oman, respectively. The second scenario evaluates emissions avoided by replacing conventional gray hydrogen, yielding reductions of 126,500 tCO2/year and 123,200 tCO2/year, respectively. These results highlight the flexibility of offshore hydrogen systems in addressing both electricity-sector and hydrogen-market decarbonization goals. Additionally, sensitivity analysis shows that increasing turbine hub height yields only marginal wind speed and cost improvements, suggesting limited economic justification under current site conditions. Overall, the study positions Egypt as a stable, year-round hydrogen producer and Oman as a high-output seasonal exporter, supporting a complementary regional strategy for green hydrogen leadership.

Electric vehicles (EVs) play a crucial role in tertiary sectors due to their eco-friendliness and sustainability when powered by clean energy. Integrating EV charging stations with renewable energy systems is essential to alleviate energy issues and grid pressure. Exploring this integration’s feasibility is imperative for sustainable transportation. This study aims to provide a clear approach and methodology for examining the potential of integrating renewable energy technologies with EV charging stations at the district level. Additionally, the study investigates the energy, economic, and environmental benefits of an integrated system comprising photovoltaic/wind turbines (PV/WTs) connected to the electricity grid to meet the energy demand of a tertiary district consisting of five hotels in Egypt. Through the development of a simulation model, the paper verifies whether the proposed energy system can meet the district’s energy demand. In addition, the simulation model has been employed to conduct a sensitivity analysis for investigating the impact of different charging rates on economic feasibility. The results indicate that a hybrid renewable energy system (HRES) integrated with an EV charging station can effectively relieve pressure on the electricity grid and provide electricity at competitive prices compared to the national grid. Moreover, the proposed energy system significantly reduces environmental emissions by up to 510 tons of CO2 per year and has the potential to decrease fossil fuel usage by 248 tons per year. Sensitivity analysis highlights the significant impact of charging prices on project profitability.

Smart grids that integrate household renewable energy sources and share information with households can help create and maintain a smarter data-driven environment. Within this environment, flexible home energy management policies that minimize household energy costs can be adopted. This paper considers a smart home with a renewable energy source that favors satisfying its energy needs at minimum cost. This is achievable by smartly scheduling the use of its domestic appliances to match a given energy grid tariff. Focusing on the case of Egypt in which an inclining block rate (IBR) tariff is imposed, this paper fills a gap in the literature regarding the load scheduling models aiming to minimize energy cost at the household level whenever such a tariff exists. A new mixed integer quadratic programming (MIQP) model is formulated for this scheduling problem, considering the adopted net metering system with installed domestic photovoltaic (PV) systems in Egypt. The model generates the optimal household load schedule and the optimal amounts of energy to exchange with the grid while considering all the system and consumer utility constraints. To assess the applicability of the proposed model, a survey is conducted to identify the diversity and characteristics of using the electrical appliances by the Egyptian households. Based on the collected survey results, the effectiveness of the proposed MIQP model is investigated. Results confirm the effectiveness of the proposed model to minimize energy cost for different categories of the Egyptian households.

Medical gas systems are safety-critical systems due to their association with critical care situations. In particular, the oxygen gas supply system contains many complications represented in the interactions between human and equipment while operating the system and managing emergencies. Therefore, it contains many hazards that require high safety standards to maintain the integrity of the system. The traditional hazard analysis methods suffer from weaknesses as their linear reasoning assumption cannot adequately describe the actual behavior of modern socio-technical systems that are characterized by tight couplings and complex interactions among technical, human and organizational aspects. Alternatively, Systems Theoretical Process Analysis (STPA) is a new hazard analysis approach that accounts for the causal relationships between system components and their unsafe interactions, allowing the integration of both occupational and process safety. STPA is widely used in the automotive, aviation, and aerospace industries whereas there are a few applications of STPA in healthcare domain. This paper proposes the use of STPA methodology in analyzing the hazards related to the medical gas pipeline and oxygen supply systems in a low-medium income country. Specifically, the analysis considers the emergencies that stem from the total or partial failure of oxygen supply and the hazards related to temporary restoration of the medical oxygen system. The results indicate that STPA contributes to identifying the hazards resulting from the interference between the human, machines, and equipment, and studying the unsafe effect of the system elements on each other. This research suggests control measures to prevent the unsafe interaction of the system components and improve the response time to oxygen failure incidents, which greatly reduces the potential consequences.

Solid waste management is critical to sustainable, healthy, and eco-friendly cities and societies. In developing countries, only a small percentage of municipal solid waste is disposed safely, while the majority remains in the streets or disposed in open landfills. Most countries seek to establish effective and efficient solid waste management system (SWMS) that can handle and dispose of the daily generated waste at minimum cost and in a sustainable manner. Those systems usually consist of waste sources, waste collection stations, landfills, incinerators, and recycling plants, in addition to the transportation system that integrates the different sub-systems. The problem facing decision-makers while designing or reconfiguring a SWMS is to determine the optimal supply chain network design for such systems in a way that ensures the treatment and disposal of all daily generated waste at the lowest cost. In this context, this paper aims to develop a generic optimization model suitable for application in SWMS optimization in developing countries. A new mixed-integer linear programming (MILP) model is formulated for a SWMS configuration that integrates waste generation sources, collection/transfer stations, recycling plants, incinerators, and landfills. The proposed MILP model is formulated to determine the optimal number and locations of the different facilities, and the optimal flow of waste in the system that minimizes the net daily cost incurred in the system. The model has been applied in a case study on the SWMS in Fayoum Governorate, Egypt. The main contribution of this research refers both to the theoretical development of a generic MILP model that can be applied to optimally design the SWMS in developing countries, and to its operational counterpart, as per the design solutions provided in the SWMS of Fayoum Governorate (Egypt).

Several load scheduling optimization models have been proposed for smart grid systems. Nevertheless, most of the existing models assume the presence of a pricing mechanism such as time of use (TOU) and consider single objective functions that are suitable for application under specific conditions. This paper proposes a generic load scheduling optimization model that can be employed under different operating conditions and can handle different scheduling criteria. In particular, the model considers TOU and allows the integration of distributed renewable energy systems (DRES). In addition, the load scheduling model is solved using the Cuckoo optimization algorithm. Performance of the Cuckoo algorithm is validated by formulating and solving an equivalent MILP model to the load scheduling problem. A set of experiments is designed to compare optimality and time performance of the Cuckoo and its equivalent MILP model. The Cuckoo results has also shown superior or at least comparable results to the published results in the literature. A case study has been performed using real data taken from an academic building in Egypt to demonstrate the model applicability under different conditions. The results show that the building under specific conditions can achieve energy cost savings that range from 57% to 80%. The results have also provided useful managerial implications.

In this research, a technoeconomic comparison of energy efficiency options for energy districts located in different climatic areas (Naples, Italy and Fayoum, Egypt) is presented. A dynamic simulation model based on TRNSYS is developed to evaluate the different energy efficiency options, which includes different buildings of conceived districts. The TRNSYS model is integrated with the plug-in Google SketchUp TRNSYS3d to estimate the thermal load of the buildings and the temporal variation. The model considers the unsteady state energy balance and includes all the features of the building’s envelope. For the considered climatic zones and for the different energy efficiency measures, primary energy savings, pay back periods and reduced CO2 emissions are evaluated. The proposed energy efficiency options include a district heating system for hot water supply, air-to-air conventional heat pumps for both cooling and space heating of the buildings and the integration of photovoltaic and solar thermal systems. The energy actions are compared to baseline scenarios, where the hot water and space heating demand is satisfied by conventional natural gas boilers, the cooling demand is met by conventional air-to-air vapor compression heat pumps and the electric energy demand is satisfied by the power grid. The simulation results provide valuable guidance for selecting the optimal designs and system configurations, as well as suggest guidelines to policymakers to define decarbonization targets in different scenarios. The scenario of Fayoum offers a savings of 67% in primary energy, but the associated payback period extends to 23 years due to the lower cost of energy in comparison to Naples.

Extensive studies are conducted to investigate the potential and techno-economic feasibility of bioenergy routes in different countries. However, limited researches have been focused on the whole national agricultural bioenergy resources in Egypt. This research provides an assessment of the potential agricultural biomass resources for electric energy production in Egypt. It provides a strategic perspective for the design of a national network of biomass power plants to utilize the spatially available agricultural residues throughout a country. A comprehensive approach is presented and is applied to Egypt. First, the approach estimates the amount, type, and characteristics of the agricultural residues in each Egyptian governorate. Then, a techno-economic appraisal for locating a set of collection stations, and installing a direct combustion biomass power plant in each governorate is conducted. SAM simulation software is used for the technical and economic appraisals, and preliminary plant capacities are estimated assuming one plant in each governorate. Secondly, a new mixed integer linear programming (MILP) model is proposed and applied to optimally design a biomass supply chain national network to maximize the overall network profit. The network is composed of the collection stations, the potential biomass power plants, and the flow distribution of residues to supply the selected plants. Results indicate that the Egyptian agricultural residue resources can produce 10 million ton/year of dry residues, generate 11 TWh/year, an average levelized cost of electricity (LCOE) of 6.77 ¢/kWh, and supply about 5.5% of Egypt’s current energy needs. Moreover, the optimization results reveal that a network of 5 biomass power plants with capacities of 460 MW each should be established in Egypt. This approach is thought to be particularly suitable to other developing countries whose energy demand depends on fossil fuels and poses a heavy economic burden, and whose residues are massive, wasted, and not industrialized. The obtained results may also enrich future comparative research that studies the impact and feasibility of implementing agro-residue based biomass electric energy generation.

The bullwhip effect reflects the variance amplification of demand as they are moving upstream in a supply chain, and leading to the distortion of demand information that hinders supply chain performance sustainability. Extensive research has been undertaken to model, measure, and analyze the bullwhip effect while assuming stationary independent and identically distributed (i.i.d) demand, employing the classical order-up-to (OUT) policy and allowing return orders. On the contrary, correlated demand where a period’s demand is related to previous periods’ demands is evident in several real-life situations, such as demand patterns that exhibit trends or seasonality. This paper assumes correlated demand and aims to investigate the order variance ratio (OVR), net stock amplification ratio (NSA), and average fill rate/service level (AFR). Moreover, the impact of correlated demand on the supply chain performance under various operational parameters, such as lead-time, forecasting parameter, and ordering policy parameters, is analyzed. A simulation modeling approach is adopted to analyze the response of a single-echelon supply chain model that restricts return orders and faces a first order autoregressive demand process AR(1). A generalized order-up-to policy that allows order smoothing through the proper tuning of its smoothing parameters is applied. The characterization results confirm that the correlated demand affects the three performance measures and interacts with the operating conditions. The results also indicate that the generalized OUT inventory policy should be adopted with the correlated demand, as its smoothing parameters can be adapted to utilize the demand characteristics such that OVR and NSA can be reduced without affecting the service level (AFR), implying sustainable supply chain operations. Furthermore, the results of a factorial design have confirmed that the ordering policy parameters and their interactions have the largest impact on the three performance measures. Based on the above characterization, the paper provides management with means to sustain good performance of a supply chain whenever a correlated demand pattern is realized through selecting the control parameters that decrease the bullwhip effect.

Power factor (PF) is a measure of how effectively electricity is used. The low power factor causes considerable power losses along the power supply chain. In particular, it overloads the distribution system and increases the power plant's burden to compensate the expected power losses. Most of the existing PF correction techniques are developed based on placing centralized capacitors, assuming that power systems are static. However, the power systems are dynamic systems such that their states change over time, necessitating dynamic correction systems. In the emerging smart grid systems, real-time measurements can easily be taken for voltage, current and harmonics. Then, the measured data can be transmitted to a PF controller to reach the desired PF value. However, the problem that will arise in real-time applications is how to determine and adjust the optimal capacitor size that can balance the power factor. In this regard, we propose a real-time correction system based on multi-step capacitor banks to improve PF in co-operation with de-tuned filters to mitigate the harmonics. First, a mathematical model has been formulated for the proposed power factor correction system. The mathematical model can be employed to determine the optimal operational settings of the multi-step capacitor and the reactor value that optimize the reactive power while considering the desired PF value and restricting the harmonics. Second, a genetic optimization approach is applied to solve the proposed mathematical model as it can provide accurate solution in a short computational time. A Monte Carlo simulation approach is considered for validating the proposed PF correction system. The simulation results show that the average PF of the randomly generated test instances has improved from 0.7 to 0.95 (35% increase). Furthermore, we conducted real experiments using a PF testbed for experimental validation. The results are found to be consistent with the simulation results, which validate the effectiveness and applicability of the proposed correction system. Furthermore, the saved kVA in one day is estimated to be 26% of total kVA.

Numerous studies have confirmed the negative impact of the lack of coordination on supply chain performance. In particular, the lack of coordination leads to the bullwhip effect, which has severe impacts on supply chain stability. This paper evaluates a proposed coordination mechanism that allows a decentralized information sharing in multi-echelon supply chains. The proposed mechanism “Info-Smooth” utilizes the ordering rule of the generalized (R, S) policy in which a replenishment order can be transferred to upstream echelons including two pieces of information (demand forecast and inventory balance). As the generalized (R, S) can allow order smoothing, Info-Smooth combines the power of both information sharing and order smoothing. A simulation modeling methodology is employed to investigate the effectiveness of Info-Smooth in a multi-echelon supply chain. The factorial design results have shown that Info-Smooth is successful in mitigating the bullwhip effect whilst keeping acceptable inventory stability, compared to the traditional supply chain model.

Extensive research has shown that collaboration has substantial impact on supply chains performance. However, most of previous research has focused on information sharing-based collaboration models that require considerable effort to be implemented, such as information exchange supply chain (IESC). This paper introduces a new efficient collaboration model (IS-OUT) for multi-echelon supply chains. The IS OUT model relies upon the ordering mechanism of the classical order-up-to policy (OUT). In traditional supply chains (TSC), although OUT replenishment orders include two pieces of information: demand forecast and inventory position balance, they are transferred to upstream echelons as single-quantity orders. In IS-OUT, the order information is transferred to the upstream echelons as two component parts of information to provide better coordination in supply chains. In this paper, the mathematical formulation of IS-OUT is presented, and simulation is adopted to compare the performance of TSC, IS-OUT, and IESC under special conditions and assumptions, considering various performance metrics. Although the results indicate that IESC model offers higher performance mostly than IS-OUT, IS-OUT outperforms or at least is comparable with IESC under a few conditions. Since IS-OUT model is easier to implement than IESC, IS-OUT provides a compromise between extent and information sharing requirements, and performance efficiency. The results should help a decision maker to select the model that optimizes its operating environment. (C) 2019 Elsevier Ltd. All rights reserved.

Supply chains experience variance amplification in replenishment orders and inventory levels, leading to severe inefficiencies. Extensive studies are conducted while focusing mainly on the demand variance amplification (also known as bullwhip effect), but limited research is undertaken to optimize the variance amplification that considers both the orders and net stock variability. A single-echelon supply chain with a stationary demand process, a generalized periodic-review order-up-to (OUT) policy, and an exponential smoothing forecasting model are assumed. Hence, this paper seeks to optimize the best values of the exponential smoothing and OUT policy parameters that minimize the sum of demand and inventory variances. A hybrid approach that integrates simulation modeling and response surface methodology is proposed. The algorithm is iterative in nature, where at each iteration simulation runs are conducted to generate a response surface for the variance amplification, and a gradient search is applied to locate a new incumbent solution. Several experiments are conducted to demonstrate the applicability of the approach, and to validate its results with previous researches. The proposed RSM-Simulation based algorithm produces comparable results to existing methods and thus having a good potential to accommodate more supply chain complexities. It can be used to model and optimize nonlinear supply chains, supply chain with stochastic lead-time, supply chains with correlated demand, and supply chains with capacity constraints.

This study aims at analyzing the techno-economic feasibility of electric power generation from rice straw in in Egypt. In particular, we attempt to estimate the energy production and the levelized cost of electricity (LCOE) at some selected provinces with high potential availability of rice straw. Accordingly, a proposed configuration of biomass Rankine cycle power plant fed with rice straw is modeled and simulated using System Advisor Model (SAM). The simulation shows that the average nominal and the average real LCOE for the proposed power plants are 10.55 and 6.33 ¢/kWh respectively, which is very competitive, compared with LCOE of other renewable energy technologies in Egypt. Furthermore, a sensitivity analysis is conducted to investigate the effect of the main technical and economic variables of the power plant on the energy output and LCOE. The results reveal that flue gas temperature, rice straw moisture content and excesses fed air significantly affect the energy output. Furthermore, the results indicate that LCOE is highly sensitive to feedstock price and discount rate. Policy makers and stakeholders could use these results for developing strategic capacity plans based on biomass power plants possibly integrated with other electricity sources and the utility grid.

Offshore wind turbines are being used to harness the high value of wind energy usually available on the sea sufficiently far from the shore (i.e. some kilometers). The present study provides an assessment of the potential of offshore wind energy along the Mediterranean Sea in Egypt. The techno-economic assessment was conducted considering a 7.0 MW offshore wind turbine at seven sites along the Mediterranean Sea. Fixed platforms were considered, assuming that the maximum sea depth will be 60 m, that is representative of the sea depth in the Mediterranean coast of Egypt at 5 km from the shore. The analysis reveals that a very large amount of energy can be harvested. The minimum energy production is obtained at Alexandria with a capacity factor of 55%, and the maximum energy production is obtained at El Dabaa station with a capacity factor of 63%. The levelized cost of electricity (LCOE) is estimated as to be equal to about 0.075–0.079 US$/kWh which can be considered very competitive with other renewable energy systems in Egypt. The results prove the techno-economic feasibility of the offshore wind energy resource in Egypt, and it would motivate both the research community and the policy makers for more attention regarding this resource.

Inventory replenishment rules contribute significantly to the bullwhip effect and inventory instability in supply chains. Smoothing replenishment rules have been suggested as a mitigation solution for the bullwhip effect but dampening the bullwhip effect might increase inventory instability. This paper evaluates a real-time inventory replenishment system denoted as SPC that utilizes a control chart approach to counteract the bullwhip effect whilst achieving competitive inventory stability. The SPC employs two control charts integrated with a set of decision rules to estimate the expected demand and adjust the inventory position, respectively. The first control chart works as a forecasting mechanism and the second control chart is devoted to control the inventory position variation whilst allowing order smoothing. A simulation analysis has been conducted to evaluate and compare SPC with a generalized (R, S) policy in a four-echelon supply chain, under various operational settings in terms of demand process, lead-time and information sharing. The results show that SPC is superior to the traditional (R, S) and comparable to the smoothing one in terms of bullwhip effect, inventory variance, and service level. Further managerial implications have been obtained from the results. (C) 2014 Elsevier Ltd. All rights reserved.

Bullwhip effect represents the amplification and distortion of demand variability as moving upstream in a supply chain, causing excessive inventories, insufficient capacities and high operational costs. A growing body of literature recognizes ordering policies and the lack of coordination as two main causes of the bullwhip effect, suggesting different techniques of intervention. This paper investigates the impact of information sharing on ordering policies through a comparison between a traditional (R, S) policy and a coordination mechanism based on ordering policy (a combination of (R, D) and (R, S) policies). This policy relies on a slow, easy to implement, information sharing to overcome drawbacks of the effect, in which replenishment orders are divided into two parts; the first is to inform the upstream echelons about the actual customer demand and the second is to inform about the adjustment of the inventory position, smoothing at the same time the orders of the different levels of the supply chain. A simulation model for a multi-echelon supply chain quantifies the supply chain dynamics under these different policies, identifying how information sharing succeeds to achieve an acceptable performance in terms of both bullwhip effect and inventory variance.

This editorial article presents the bullwhip effect which is one of the major problems faced by supply chain management. The bullwhip effect represents the demand variability amplification as demand information travels upstream in the supply chain. The bullwhip effect research has been attempting to prove its existence, identify its causes, quantify its magnitude and propose mitigation and avoidance solutions. Previous research has relied on different modeling approaches to quantify the bullwhip effect and to investigate the proposed mitigation/avoidance solutions. Extensive research has shown that smoothing replenishment rules and collaboration in supply chain are the most powerful approaches to counteract the bullwhip effect. The objective of this article is to highlight the bullwhip effect avoidance approaches with providing some interesting directions for future research.

A major cause of supply chain deficiencies is the bullwhip effect, which implies that demand variability amplifies as one moves upstream in supply chains. Smoothing inventory decision rules have been recognized as the most powerful approach to counteract the bullwhip effect. Although several studies have evaluated these smoothing rules with respect to several demand processes, focusing mainly on the smoothing order-up-to (OUT) replenishment rule, less attention has been devoted to investigate their effectiveness in seasonal supply chains. This research addresses this gap by investigating the impact of the smoothing OUT on the seasonal supply chain performances. A simulation study has been conducted to evaluate and compare the smoothing OUT with the traditional OUT (no smoothing), both integrated with the Holt-Winters (HW) forecasting method, in a four-echelon supply chain experiences seasonal demand modified by random variation. The results show that the smoothing OUT replenishment rule is superior to the traditional OUT, in terms of the bullwhip effect, inventory variance ratio and average fill rate, especially when the seasonal cycle is small. In addition, the sensitivity analysis reveals that employing the smoothing replenishment rules reduces the impact of the demand parameters and the poor selection of the forecasting parameters on the ordering and inventory stability. Therefore, seasonal supply chain managers are strongly recommended to adopt the smoothing replenishment rules. Further managerial implications have been derived from the results. (C) 2015 Elsevier Ltd. All rights reserved.