Energy Cost Of System Research Articles

Planning and Optimisation of Renewable Energy Systems for Decarbonising Operations of Oil Refineries

AbstractGiven the urgency to transition to low carbon future, oil refineries need to identify feasible strategies for decarbonisation. One way to address this is by integrating renewable energy systems. However, the high initial costs and intermittency appeared to be the key barriers for the adoption of renewable energy technologies. Hence, a multi-period optimisation model is developed via mixed integer linear programming in this work to determine the optimal renewable energy system in terms of cost and its optimal energy storage technology to enhance its flexibility for oil refinery operations. This model aims to minimise the costs of the renewable energy system while considering its ability to accommodate the varying energy demands across the time periods. An oil refinery case study is used to demonstrate the effectiveness of the developed model. The developed model is expected to propose an optimal renewable energy system that meets the energy demands and, at the same time, achieves the decarbonisation goal. Based on the results, the optimal renewable energy system comprises cost-effective technologies to generate various energy outputs including electricity, hydrogen, high-pressure and medium-pressure steam to meet energy demands. Additionally, the result of the case study shows that the integration of renewable energy systems achieves a reduction of 5,353 tonnes of carbon dioxide. Apart from that, the incorporation of energy-efficient energy storage results in a 10% reduction in the total cost of the optimal renewable energy system. Compressed hydrogen gas storage and battery were used to store excess hydrogen and electricity during periods with low demands and subsequently consumed during peak demand periods. This can, therefore, reduce the technological capacity required. With the aid of storage facilities, the flexibility of the renewable energy system is elevated in meeting varied demands, which otherwise would incur additional expenses.

Self-catalytic enhancement of Cu-EDTA decomplexation and simultaneous Cu recovery via a dual-cathode electrochemical process

Heavy metals that are readily chelated with coexisting organic ligands in industrial wastewaters impose threats to environment and human health but are also valuable metal resources. Traditional treatment methods generally require additional chemicals and generate secondary contaminants. Here, a reagent-free dual-cathode electrochemical system was proposed for the efficient destruction of Cu-organic complexes and synchronous cathodic recovery of Cu, whereby in situ production of H2O2 at carbon aerogel (CA) cathode was coupled with the reduction of Cu(II) to Cu(I) and finally to Cu(0) at Ti cathode. The intermediate Cu(II) complexes enabled the self-reinforced degradation owing to their higher activities toward •OH generation by activating H2O2 in contrast to initial Cu-ethylenediaminetetraacetic acid (Cu-EDTA). The enhanced production of Cu(I) by Ti cathode facilitated both •OH and Cu(III) formation, and the copper redox cycle was realized in the self-reinforced system, maintaining its sustainable catalytic activity. The energy cost of the dual-cathode system is 0.011 kWh/g for decomplexation and 0.057 kWh/g for Cu recovery, which is much lower than single Ti or CA cathode system. This established process provides a prospective approach for cost-effective destruction of chelating metal complexes and metal resources recovery from heavy metal wastewaters.

Degradation of carbamazepine in piezo-activation of persulfate systems: Comparison of PDS and PMS

The choice of persulfate (PS) is crucial to the degradation and energy efficiencies for piezo-activation of PS (PE-PS) systems. Herein, a comparison between piezoelectrically activated peroxydisulfate (PDS) and peroxomonosulfate (PMS) using BaTiO3 are comparatively investigated for carbamazepine (CBZ) degradation. By contrast, the PE-PMS system achieves better degradation and mineralization of CBZ, as well as higher reaction stoichiometric efficiencies (%RSE). Nevertheless, the energy consumption of the PE-PDS system is only 9.69% of the PE-PMS system. The quenching test, chemical probe determination, and electron spin resonance (EPR) analysis suggest that the concentration of •OH, SO4∙-, and 1O2 except ∙O2- of the PE-PMS system, is higher than that of the PE-PDS system. The piezoelectric current density and carrier separation efficiency of the PE-PMS system are higher than that of the PE-PDS system, demonstrating that the piezoelectrically activated PMS has higher reactivity than piezoelectrically activated PDS. The ∙O2- and •OH are the primary ROS in the PE-PDS system, while 1O2 and •OH are the major ROS in the PE-PMS system. Additionally, CBZ is effectively degraded by both PE-PMS and PE-PDS systems from actual water sources. This work compares the performance, energy cost, and mechanism of PE-PMS and PE-PDS systems systematically. Hopefully, all of that is designed to tender a helpful reference for the choice of PS in piezocatalytic research and application.

Optimal Scheduling of PV Panel Cleaning and Policy Implications Considering Uncertain Dusty Weather Conditions in the Middle East

Airborne dust easily accumulates on the top of solar panel surfaces and reduces the output power in arid regions. A commonly used mitigation solution for dust deposition issues is cleaning PV panels periodically. However, cleaning frequency affects the economic viability of solar PV systems, resulting in a trade-off between cleaning costs and energy loss costs. To address this issue, this study relates several metrics and develops a generic framework based on simulation and optimization to determine the optimal cleaning interval. Based on the computational tests, the optimal cleaning interval in Abu Dhabi is determined to be 34 days, which is longer than the currently recommended cleaning interval of 28 days. This study also identifies that energy recovery is responsive to decreases in unit cleaning costs in the presence of high electricity tariffs, whereas total cost savings show sensitivity when electricity tariffs are low. Finally, this study discusses energy policy implications by presenting an innovative concept involving the introduction of a cleaning subsidy which could reshape energy system cost dynamics, making PV systems economically competitive beyond the conventional levelized cost of electricity.

Assessment of climate change mitigation potential of the Kosovo energy and transport sector

The Energy and transport sector in Kosovo are the main emitter of greenhouse gases (GHG) with a share of about 85% in total annual emissions. In energetic sector, 95-97% of emissions are associated with the electricity generation due to the predominant role of lignite fuelled power plants. The electricity sector contributes about 80% of total CO2 emissions in Kosovo. Those make the energy and transport sector the main emitters of CO2 and also the main target for CO2 emissions reduction. The National Energy and Climate Plan 2021-2030 aims to ensure a reliable supply of energy for Kosovo and proposes targets for renewable, energy efficiency, and greenhouse gas reductions. This paper presents analyses of different time frame scenarios for development of the Kosovo energy system in relation to climate change mitigation potential of the Kosovo energy and transport sector. The national energy system of Kosovo is modelled using EnergyPLAN software which integrates energy for electricity, transport and heat, and includes hourly fluctuations of energy demand and energy production. Three scenarios of Kosovo Energy system are modelled; starting point scenario 2019 and two long term scenarios 2030 with 23.3% and 41.8% share of renewable. An analysis of the overall energy system costs and security of supply with a main focus in electricity sector of the three scenarios is performed.

Feasibility study of the hybrid biogas-photovoltaic energy system in poultry and swine farms

This paper aims to evaluate the use of a hybrid photovoltaic-biogas grid-connected system in a rural property at Rio Verde region. The study focused on technical and cost issues of a rural property with swine and chicken production, located in the state of Goiás, Brazil. A comparative analysis of the costs of the hybrid system after optimization was made using HOMER© (Hybrid Optimization Model for Electric Renewables) software. The analysis shows that the optimal system’s initial cost, net present cost, electricity cost with the photovoltaic-biogas on grid system is US$ 280.180, US$ 661.682, and 0,082 US$.kWh-1, respectively. The main cost of the hybrid photovoltaic-biogas energy system consists of the biogas generator, biodigesters, inverters with the photovoltaic modules. In the short term, the implementation of the hybrid photovoltaic biogas system in the region is an interesting alternative for farmers considering the sustainability of agribusiness.

Enhanced temperature regulation in compound heating systems: Leveraging guided policy search and model predictive control

With the widespread application of solar-energy-air-source heat pump composite heating, optimizing the regulation of air temperature in heating zones, enhancing system thermal comfort, and reducing energy consumption have become crucial. To ensure residential comfort while minimizing HVAC system energy costs, a control method combining guided policy search (GPS) with model predictive control (MPC) is proposed for composite heating systems. This method replaces state estimation in MPC with policy search and substitutes the offline trajectory optimization used in guided policy search with MPC, thus integrating the strengths of both approaches. This strategy not only improves control precision but also reduces energy consumption and enhances system robustness. The GPS-MPC control algorithm was validated against reinforcement learning and MPC. A simulation model was developed and validated on a real physical platform. Simulations compared the control effects of on-off control and MPC on pump frequency, flow rate, stratified thermal storage tank temperature, component, and system energy consumption. The data results indicate that the GPS-MPC algorithm offers superior predictive accuracy, efficiency, and robustness in composite heating control systems compared to conventional methods. Under the GPS-MPC strategy, indoor temperature fluctuations, pump frequency response speed, and energy consumption were significantly improved, with temperature fluctuation limited to only 0.8 °C, and the system achieving energy savings of over 12 %.

Quantifying the operational flexibility and invocation costs of urban regional integrated energy systems for participation in demand response programs

As a typical aggregation platform, the regional integrated energy system (RIES) is required to pre-submit its flexibility for each time period when participating in demand response. However, existing studies fail to quantify the flexibility parameters necessary for RIES pre-reporting. Therefore, this paper introduces a flexibility quantification framework tailored for RIES engagement in demand response. The core principle of the framework is grounded in the RIES day-ahead dispatch model considering virtual energy storage, which transforms the flexibility quantification problem into dispatch optimization problems under baseline scenarios and flexibility invocation scenarios. In the flexibility invocation scenario, a control term is incorporated into the objective function to steer the system towards fulfilling flexibility requirements in the most economical strategy. The deviation of power and operating costs in both scenarios defines the system's flexibility and flexibility invocation cost, respectively. This framework holds broad applicability across various aggregation platforms and can measure flexibility across different time scales by adjusting the control interval of the day-ahead dispatch optimization model. Finally, the framework is validated through case studies, revealing the impact characteristics of system type and capacity on flexibility, and analyzing the sensitivity of flexibility to various uncertainty parameters.

Improving the Economic Feasibility of Small-Scale Biogas-Solid Oxide Fuel Cell Energy Systems through a Local Ugandan Biochar Production Method

A small-scale (up to 5 kWe) biogas-solid oxide fuel cell (SOFC) energy system is an envisioned system, which can be used to meet both electrical and thermal energy demand of off-grid settlements. SOFC systems are reported to be more efficient than alternatives like internal combustion engines (ICE). In addition to energy recovery, implementation of biogas-SOFC systems can enhance sanitation among these settlements. However, the capital investment costs and the operation and maintenance costs of a biogas-SOFC energy system are currently higher than the existing alternatives. From previous works, H2S removal by biochar was proposed as a potential local cost-effective alternative. This research demonstrates the techno-economic potential of locally produced biochars made from cow manure, jackfruit leaves, and jack fruit branches in rural Uganda for purifying the biogas prior to SOFC use. Results revealed that the use of biochar from cow manure and jack fruit leaves can reduce H2S to below the desired 1 ppm and substitute alternative biogas treatments like activated carbon. These experimental results were then translated to demonstrate how this biochar would improve the economic feasibility for the implementation of biogas-SOFC systems. It is likely that the operation and maintenance cost of a biogas-SOFC energy system can in the long run be reduced by over 80%. Also, the use of internal reforming as opposed to external reforming can greatly reduce the system capital cost by over 25% and hence further increase the chances of system economic feasibility. By applying the proposed cost reduction strategies coupled with subsidies such as tax reduction or exemption, the biogas-SOFC energy system could become economically competitive with the already existing technologies for off-grid electricity generation, like solar photovoltaic systems.

Voltage Stacking: A First-Order Modelization of an m × n Asynchronous Array for Chip and Architectural Design Exploration

Voltage stacking is a technique in which multiple integrated circuits are stacked in series between the supply voltage instead of in parallel, thus improving the energy efficiency of the power distribution network. Unfortunately, voltage stacking presents stability challenges for integrated circuits within the stack. A first-order model to quantify variability, stability, and power metrics for an array of voltage-stacked asynchronous integrated circuits is presented. Voltage variability and power consumption are accounted for and discussed. Limitations of the model are identified outside of the nominal behavior. The number of columns in the architecture, chip leakage, and supply voltage are shown to be the key contributors to the stability, performance, and energy efficiency of a system of voltage-stacked asynchronous processors. A higher leakage to active power ratio, though usually avoided by chip designers, is shown to improve stability and be key in designing stacks without external balancing. Outputs of the model enable system and chip designers to evaluate first-order trade-offs in energy efficiency, performance, and system cost. These fundamental data allow designers to make informed design and optimization trade-offs between asynchronous voltage-stacked architectures and the integrated circuits used therein. Analysis of this model shows that various voltage-stacked configurations, such as one with a 48 V supply using 100 rows and 11 columns, can be designed with less than 10% voltage variation per chip, mitigating the need for external voltage balancing.

Flexibility from industrial demand-side management in net-zero sector-coupled national energy systems

National energy systems require flexibility to accommodate increasing amounts of variable renewable energy. This flexibility can be provided by demand-side management (DSM) from industry. However, the flexibility potential depends on the characteristics of each industrial process. The enormous diversity of industrial processes makes it challenging to evaluate the total flexibility provision from industry to sector-coupled energy systems. In this work, we quantify the maximum cost reductions due to industrial DSM in the net-zero sector-coupled Swiss energy system, and the relationship between cost reductions and various industrial process characteristics. We analyze the flexibility of industrial processes using a generic, process-agnostic model. Our results show that industrial DSM can reduce total energy system costs by up to 4.4%, corresponding to 20% of industry-related energy costs. The value of flexibility from industrial DSM depends not only on the process characteristics but also on the system’s flexibility alternatives, particularly for flexibility over seasonal time horizons. As one specific option for industrial DSM, we find that thermal energy storage (TES) technologies available today could realize between 28% and 61% of the maximum cost reductions from industrial DSM, making TES a promising DSM solution and showing that industrial DSM is an accessible and cost-effective flexibility option.

Co-optimization of the operation and energy for AGVs considering battery-swapping in automated container terminals

Since the number of containers in automated container terminals (ACTs) has been growing, the operation of ACT relies on a large amount of electric power, which comes from the terminal energy system. As the main horizontal transportation equipment in ACTs, the scheduling of automated guided vehicles (AGVs) has become increasingly important. Even though AGV scheduling has been studied, the battery-swapping procedure is often overlooked, which hinders operation efficiency and the usage of renewable energy. We propose a co-optimization problem of the operation and energy for AGVs considering battery-swapping in this paper. A multi-objective model is constructed to minimize the makespan of AGVs and the operation and maintenance cost of energy system. In addition, we create a discrete multi-objective whale optimization algorithm to address this co-optimization problem. A load-balancing-based method for population initialization is developed to make the distribution of solutions more uniform in this algorithm. With the two-dimensional matrix encoding scheme, an individual right-shifted method is proposed for decoding. The best solution is assessed and selected through a fitness evaluation mechanism based on fuzzy correlation entropy. The bubble-net attack based on opposition-based learning is implemented to enhance the capacity for local intensification. Numerous tests confirm that the proposed method can guarantee the operation efficiency, lower the total cost of the energy system, and promote sustainable development in ACTs.

The management of an energy system in the realm of rapid energy transition and degasification as a consequence of energy crisis, examination in H2RES energy model

The energy system in Europe faces significant challenges. Though, the transition to renewable energy is under way, still a significant portion of energy is supplied from fossil fuels, mainly natural gas. In the second half of 2021, the supply of natural gas and consequently the rate of replenishment of the reserves dwindled. The scarcity of natural gas has resulted in record-high energy prices as well as an increase in goods prices. Especially hit are the industry sectors highly dependent on natural gas such as the petrochemical industry.This research investigates the strategies to mitigate the crisis in the European Union energy system while simultaneously ensuring low energy system cost and fulfilment of energy transition goals. The system analysis and simulations are carried out in the liner optimization model − H2RES.The results display that the system opts for accelerated fulfilment of energy transition goals. Therefore, the dependency on the stable supply of natural gas is decreased. Also, the total cost of an energy system that undergoes transition as compared to the business as usual scenario is 33 % lower even when accounting for the necessary investments by 2050.

Optimising robotic operation speed with edge computing via 5G network: Insights from selective harvesting robots

AbstractSelective harvesting by autonomous robots will be a critical enabling technology for future farming. Increases in inflation and shortages of skilled labor are driving factors that can help encourage user acceptability of robotic harvesting. For example, robotic strawberry harvesting requires real‐time high‐precision fruit localization, three‐dimensional (3D) mapping, and path planning for 3D cluster manipulation. Whilst industry and academia have developed multiple strawberry harvesting robots, none have yet achieved human–cost parity. Achieving this goal requires increased picking speed (perception, control, and movement), accuracy, and the development of low‐cost robotic system designs. We propose the edge‐server over 5G for Selective Harvesting (E5SH) system, which is an integration of high bandwidth and low latency Fifth‐Generation (5G) mobile network into a crop harvesting robotic platform, which we view as an enabler for future robotic harvesting systems. We also consider processing scale and speed in conjunction with system environmental and energy costs. A system architecture is presented and evaluated with support from quantitative results from a series of experiments that compare the performance of the system in response to different architecture choices, including image segmentation models, network infrastructure (5G vs. Wireless Fidelity), and messaging protocols, such as Message Queuing Telemetry Transport and Transport Control Protocol Robot Operating System. Our results demonstrate that the E5SH system delivers step‐change peak processing performance speedup of above 18‐fold than a standalone embedded computing Nvidia Jetson Xavier NX system.

Challenges Faced by The Chemical Industry in Strengthening the Bioeconomy in Brazil

Objective: This study aims to discuss the chemical industry's role in developing new bioeconomic systems in Brazil. Theoretical Framework: The bioeconomy provides significant opportunities for Brazil. By exploring the country's biodiversity and environmental resources, bioeconomic systems can drive the development of innovative technologies and high-value products across various sectors. Additionally, the bioeconomy plays a crucial role in generating quality employment and preserving ecosystems. Achieving this optimistic scenario depends on the efforts of the Brazilian chemical industry in research and development (R&D). Method: This research is documental and descriptive. We reviewed several sectoral reports focusing on the obstacles to using renewable inputs from Brazilian biodiversity in the chemical sector's value chains. Results and Discussion: Brazil's bioeconomy systems face limited R&D investments due to structural bottlenecks related to the national tax system, logistical infrastructure, and high energy costs. These issues have reduced the competitiveness of the Brazilian chemical industry. Furthermore, increased imports of Chinese chemical products following the Ukraine War and the subsequent industry idleness can exacerbate these structural constraints and further erode the chemical sector's R&D funds. Research Implications: The structural problems described in this article can delay the development of renewable energy sources and innovative bioproducts. Originality/Value: We show that the chemical industry possesses the technology and scale necessary to integrate into Federal Government programs in the bioeconomy field. However, the sector's structural problems and low R&D investments tend to inhibit the development of new bioeconomic systems. Brazil must overcome these challenges to establish itself as a global bioeconomy powerhouse.

Optimization of energy and hydrogen storage systems in geothermal energy sourced hybrid renewable energy system

Research on renewable energy systems has accelerated in recent years due to the exhaustibility of fossil fuels and the damage they cause to the environment. Renewable energy sources such as wind, solar, and geothermal are used for energy production. In this study, a hybrid system design was created using wind, solar, and geothermal energy. The study offered the opportunity to produce electricity from low-temperature geothermal energy. Optimization of a hybrid system with geothermal energy in this study presents an innovative approach. Additionally, an energy storage system was added to the system for hydrogen production from the excess energy. The system met the required electrical load of 1983.040 MWh/yr and excess electricity was 362.077 MWh/yr in total electricity of 2402.572 MWh/yr. The excess electricity produced was used in hydrogen production, and hydrogen production was 3423 kg/yr. The levelized energy cost of the hybrid system was obtained as $0.561. In a rural area with geothermal resources, this new hybrid system is quite reasonable in terms of both meeting the electricity needs using renewable energy and uninterrupted use by storing excess energy and hydrogen.

Cost and system effects of nuclear power in carbon-neutral energy systems

Moving towards carbon-neutral societies, both nuclear and renewable energy can potentially supply CO2-free electricity. While the cost of renewable energy has decreased significantly, the cost of nuclear has, however, increased in the past decades and now in general exceeds the cost of renewables. However, one cannot compare directly the per unit cost of electricity since temporal behavior in the electricity production differs substantially between the two groups of technologies. Nuclear power inherently aims to provide a constant base load supply of electricity, while renewables generally depend on weather patterns. Thus, the two have different requirements and impact the overall system costs differently regarding flexibility and system design. Focusing on the case of Denmark, this article investigates a future fully sector-coupled energy system in a carbon-neutral society and compares the operation and costs of renewables and nuclear-based energy systems. The study finds that investments in flexibility in the electricity supply are needed in both systems due to the constant production pattern of nuclear and the variability of renewable energy sources. However, the scenario with high nuclear implementation is 1.2 billion EUR more expensive annually compared to a scenario only based on renewables, with all systems completely balancing supply and demand across all energy sectors in every hour. For nuclear power to be cost competitive with renewables an investment cost of 1.55 MEUR/MW must be achieved, which is substantially below any cost projection for nuclear power.

Integrating relational values in social acceptance of photovoltaic energy storage systems: A consumers' perspective assessment using structural equation modeling

Photovoltaic (PV) energy sources are considered potential sources of renewable energy for combating climate change. However, consumer acceptance of PV-based energy storage systems must be studied comprehensively and psychologically beyond mere awareness and affordability. This study explores consumer acceptance of PV energy storage systems, along with an added relational value context that demonstrates the conducive human-nature relationship among energy consumers. An online survey of 370 respondents was used to examine consumers’ willingness to prefer PV energy storage systems over non-renewable grid-connected energy storage systems. Results of the study were analyzed using a comprehensive structural equation model to estimate and test hypotheses. The findings indicate the favorable influence of several elements on PV energy system social acceptability, including PV energy system awareness, PV energy system beliefs, environmental knowledge, self-effective perception, and relational values. Conversely, the cost of PV energy systems has a negative effect. This study suggests that relational values may offset the negative impacts of energy costs. The inherent link between relational values and the prioritization of sustainable energy storage options serves as a source of information for energy policymakers and stakeholders to regulate energy consumption by influencing consumers to express their love for nature.

Thermal, Electrical, and Economic Performance of a Hybrid Solar-Wind-Geothermal System: Case Study of a Detached House in Hamburg and Sylt, Germany

Germany is undergoing an energy transition. By 2045, fossil fuels will be gradually replaced by clean energy. An alternative option is to use geothermal, solar and wind energy to generate heat or electricity. Currently, an economic model that considers these three energy sources and incorporates the design and installation of the energy system as well as operational costing focusing on the local market is lacking. In this study, we present a concept for a hybrid energy system combining solar, wind and geothermal energy for small, detached houses. We also develop a simplified economic model for the German market and local energy subsidy policies. The model was applied to two different cities in northern Germany, calculating the installation and long-term operating costs of different energy systems and combinations over a period of 100 years, including the consideration of the lifespan of variable equipment. The calculations show that for this small hybrid energy system the initial installation costs can vary from EUR 20,344 to EUR 70,186 depending on different portfolios. Long-term operating costs come mainly from electricity purchased from the grid to compensate for periods of low solar or wind production. In addition, the study included a calculation of the payback period for retrofitting a natural gas heating system. Results show that combining a photovoltaic system with a ground source heat pump, especially in the form of a near-surface heat exchanger, yields a shorter payback period (5 to 10 years). However, the incorporation of on-roof wind turbines into the hybrid energy system may significantly prolong the payback period and is therefore not recommended for use in low wind speed areas.

Energy Cost Optimization for Incorporating Energy Hubs into a Smart Microgrid with RESs, CHP, and EVs

The energy carrier infrastructure, including both electricity and natural gas sources, has evolved and begun functioning independently over recent years. Nevertheless, recent studies are pivoting toward the exploration of a unified architecture for energy systems that combines Multiple-Energy Carriers into a single network, hence moving away from treating these carriers separately. As an outcome, a new methodology has emerged, integrating electrical, chemical, and heating carriers and centered around the concept of Energy Hubs (EHs). EHs are complex systems that handle the input and output of different energy types, including their conversion and storage. Furthermore, EHs include Combined Heat and Power (CHP) units, which offer greater efficiency and are more environmentally benign than traditional thermal units. Additionally, CHP units provide greater flexibility in the use of natural gas and electricity, thereby offering significant advantages over traditional methods of energy supply. This article introduces a new approach for exploring the steady-state model of EHs and addresses all related optimization issues. These issues encompass the optimal dispatch across multiple carriers, the optimal hub interconnection, and the ideal hub configuration within an energy system. Consequently, this article targets the reduction in the overall system energy costs, while maintaining compliance with all the necessary system constraints. The method is applied in an existing Smart Microgrid (SM) of a typical Greek 17-bus low-voltage distribution network into which EHs are introduced along with Renewable Energy Sources (RESs) and Electric Vehicles (EVs). The SM experiments focus on the optimization of the operational cost using different operational scenarios with distributed generation (DG) and CHP units as well as EVs. A sensitivity analysis is also performed under variations in electricity costs to identify the optimal scenario for handling increased demand.