Smart management of distinct plug‐in hybrid electric vehicle charging stations considering mobility pattern and site characteristics

Management of plug‐in hybrid electric vehicles (PHEVs) is an important alternative energy solution to accord the prevailing environmental depletion. However, adding PHEVs to the existing distribution network may stimulate issues such as increase in peak load, power loss, and voltage deviation. Addressing the aforementioned issues by incorporating distinct mobility patterns together will develop an attractive energy management. In this paper, suitable location of the charging station is presented for a novel 2‐area distribution system following distinct mobility patterns. A comprehensive study by considering the optimal, midst, and unfit site for placing the charging station is incorporated. For managing the charging sequence of PHEVs, a meta‐heuristic solving tool is developed. The main contribution of this programming model is its ability to schedule the vehicles simultaneously in both the areas. The efficiency of the proposed energy management framework is evaluated on the IEEE 33‐bus and IEEE 69‐bus distribution systems. The test system is subjected to different scenarios for demonstrating the superior performance of the proposed solving tool in satisfying the convenience of vehicle owner along with reducing the peak demand. The results show that charging at low electricity price period and discharging at high electricity price period enables the minimum operational cost.     

Smart grid adds to renewable resources hosting capacity: Collaboration of plug‐in hybrid electric vehicles in Volt/VAr control process

This paper develops an efficient energy management approach to increase the renewables share in energy provision of smart distribution grids (SDGs). Voltage violation ends in curtailment of renewables generations and, hence, decreases the economic success of distribution companies. To avert such deficits, this study fosters the collaboration of SDG components in an intelligent Volt/VAr control process. The investigated SDG is characterized with high penetration of photovoltaics (PVs), dispatchable distributed generations (DDGs), plug‐in hybrid electric vehicles (PHEVs), and infield control devices say as under‐load tap‐changing transformers (ULTCs). In charge stations, PHEVs are coupled to the SDG through bidirectional inverters which are offering simultaneous exchanges of active and reactive powers. Thus, regarding the PHEV aggregators, optimal schedules of active power charge/discharge signals with their inductive/capacitive reactive power provisions are determined. This notion effectively increases PV power injections and, consequently, provides significant monetary savings. Besides, this mechanism reduces ULTC tap operations in Volt/VAr control process maintaining its nominal lifetime. The proposed approach is formulated as a mixed‐integer non‐linear programming (MINLP) and solved based on DICOPT solvers in general algebraic modeling system (GAMS). Effectiveness of the proposed approach is explored on a typical distribution test system. The obtained results show 8.94% increment in harvested PVs power and hence 5.24% reduction on daily operation cost of SDG.     

A comprehensive optimized model for on‐board solar photovoltaic system for plug‐in electric vehicles: energy and economic impacts

Environmental concerns along with high energy demand in transportation are leading to major development in sustainable transportation technologies, not the least of which is the utilization of clean energy sources. Solar energy as an auxiliary power source of on‐board fuel has not been extensively investigated. This study focuses on the energy and economic aspects of optimizing and hybridizing, the conventional energy path of plug‐in electric vehicles (EVs) using solar energy by means of on‐board photovoltaic (PV) system as an auxiliary fuel source. This study is novel in that the authors (i) modeled the comprehensive on‐board PV system for plug‐in EV; (ii) optimized various design parameters for optimum well‐to‐tank efficiency (solar energy to battery bank); (iii) estimated hybrid solar plug‐in EVs energy generation and consumption, as well as pure solar PV daily range extender; and (iv) estimated the economic return of investment (ROI) value of adding on‐board PVs for plug‐in EVs under different cost scenarios, driving locations, and vehicle specifications. For this study, two months in two US cities were selected, which represent the extremities in terms of available solar energy; June in Phoenix, Arizona and December in Boston, Massachusetts to represent the driving conditions in all the US states at any time followed by assessment of the results worldwide. The results show that, by adding on‐board PVs to cover less than 50% (around 3.2 m²) of the projected horizontal surface area of a typical passenger EV, the daily driving range could be extended from 3.0 miles to 62.5 miles by solar energy based on vehicle specifications, locations, season, and total time the EV remains at Sun. In addition, the ROI of adding PVs on‐board with EV over its lifetime shows only small negative values (larger than ?45%) when the price of electricity remains below Environmental concerns along with high energy demand in transportation are leading to major development in sustainable transportation technologies, not the least of which is the utilization of clean energy sources. Solar energy as an auxiliary power source of on‐board fuel has not been extensively investigated. This study focuses on the energy and economic aspects of optimizing and hybridizing, the conventional energy path of plug‐in electric vehicles (EVs) using solar energy by means of on‐board photovoltaic (PV) system as an auxiliary fuel source. This study is novel in that the authors (i) modeled the comprehensive on‐board PV system for plug‐in EV; (ii) optimized various design parameters for optimum well‐to‐tank efficiency (solar energy to battery bank); (iii) estimated hybrid solar plug‐in EVs energy generation and consumption, as well as pure solar PV daily range extender; and (iv) estimated the economic return of investment (ROI) value of adding on‐board PVs for plug‐in EVs under different cost scenarios, driving locations, and vehicle specifications. For this study, two months in two US cities were selected, which represent the extremities in terms of available solar energy; June in Phoenix, Arizona and December in Boston, Massachusetts to represent the driving conditions in all the US states at any time followed by assessment of the results worldwide. The results show that, by adding on‐board PVs to cover less than 50% (around 3.2 m²) of the projected horizontal surface area of a typical passenger EV, the daily driving range could be extended from 3.0 miles to 62.5 miles by solar energy based on vehicle specifications, locations, season, and total time the EV remains at Sun. In addition, the ROI of adding PVs on‐board with EV over its lifetime shows only small negative values (larger than ?45%) when the price of electricity remains below $0.18/kWh and the vehicle is driven in low‐solar energy area (e.g. Massachusetts in the US and majority of Europe countries). The ROI is more than 148% if the vehicle is driven in high‐solar energy area (e.g. Arizona in the US, most Africa countries, Middle East, and Mumbai in India), even if the electricity price remains low. For high electricity price regions ($0.35/kWh), the ROI is positive and high under all driving scenarios (above 560%). Also, the reported system has the potential to reduce electricity consumption from grid by around 4.5 to 21.0 MWh per EV lifetime. A sensitivity analysis has been carried out, in order to study the impacts of the car parked in the shade on the results. Copyright © 2016 John Wiley & Sons, Ltd.     

A novel real‐time energy management strategy for plug‐in hybrid electric vehicles based on equivalence factor dynamic optimization method

The universal adaptive equivalent consumption minimization strategy (A‐ECMS) has the potential of being implemented in real‐time for plug‐in hybrid electric vehicles (PHEVs). However, the imprecise prediction of a long‐term future driving cycle and biggish computation burdens remain the barriers for further real vehicle application. Thus, it is of great significance to develop a real‐time optimal energy management strategy for PHEVs by weakening the influence of future driving cycle to the control accuracy and improving its computation efficiency. In this paper, a novel real‐time energy management strategy for PHEVs based on equivalence factor (EF) dynamic optimization method is proposed. Firstly, a novel proportional plus integral adaption law for calculating the dynamic optimal EF is established for A‐ECMS using only instantaneous information of current vehicle speed and battery state of charge. Second, three key coefficients are obtained and converted into a three‐dimensional look up tables, so as to determine the dynamic optimal EF. Finally, the method of fast searching the optimal engine torque is proposed, which significantly enhances the computational efficiency. Compared with A‐ECMS, the computational time of A‐ECMS2 is decreased near 94.8% and the deviation of fuel consumption is controlled within 4.4%. Both the numerical results and hardware‐in‐loop results prove that the proposed novel energy management strategy A‐ECMS2 has better real‐time performance and less computing burden than the general A‐ECMS.     

Modeling,design, and optimization of a cost‐effective and reliable hybrid renewable energy system integrated with desalination using the division algorithm

People in the Middle East are facing the problem of freshwater shortages. This problem is more intense for a remote region, which has no access to the power grid. The use of seawater desalination technology integrated with the generated energy unit by renewable energy sources could help overcome this problem. In this study, we refer a seawater reverse osmosis desalination (SWROD) plant with a capacity of 1.5 m³/h used on Larak Island, Iran. Moreover, for producing fresh water and meet the load demand of the SWROD plant, three different stand‐alone hybrid renewable energy systems (SAHRES), namely wind turbine (WT)/photovoltaic (PV)/battery bank storage (BBS), PV/BBS, and WT/BBS are modeled and investigated. The optimization problem was coded in MATLAB software. Furthermore, the optimized results were obtained by the division algorithm (DA). The DA has been developed to solve the sizing problem of three SAHRES configurations by considering the object function's constraints. These results show that this improved algorithm has been simpler, more precise, faster, and more flexible than a genetic algorithm (GA) in solving problems. Moreover, the minimum total life cycle cost (TLCC = 243 763$), with minimum loss of power supply probability (LPSP = 0%) and maximum reliability, was related to the WT/PV/BBS configuration. WT/PV/BBS is also the best configuration to use less battery as a backup unit (69 units). The batteries in this configuration have a longer life cycle (maximum average of annual battery charge level) than two other configurations (93.86%). Moreover, the optimized results have shown that utilizing the configuration of WT/PV/BBS could lead to attaining a cost‐effective and green (without environmental pollution) SAHRES, with high reliability for remote areas, with appropriate potential of wind and solar irradiance.     

Impact of electric vehicles on a future renewable energy‐based power system in Europe with a focus on Germany

Electric mobility is expected to play a key role in the decarbonisation of the energy system. Continued development of battery electric vehicles is fundamental to achieving major reductions in the consumption of fossil fuels and of CO₂ emissions in the transport sector. Hydrogen can become an important complementary synthetic fuel providing electric vehicles with longer ranges. However, the environmental benefit of electric vehicles is significant only if their additional electricity consumption is covered by power production from renewable energy sources. Analysing the implications of different scenarios of electric vehicles and renewable power generation considering their spatial and temporal characteristics, we investigate possible effects of electric mobility on the future power system in Germany and Europe. The time horizon of the scenario study is 2050. The approach is based on power system modelling that includes interchange of electricity between European regions, which allows assessing long‐term structural effects in energy systems with over 80% of renewable power generation. The study exhibits strong potential of controlled charging and flexible hydrogen production infrastructure to avoid peak demand increases and to reduce the curtailment of renewable power resulting in reduced system operation, generation, and network expansion costs. A charging strategy that is optimised from a systems perspective avoids in our scenarios 3.5 to 4.5 GW of the residual peak load in Germany and leads to efficiency gains of 10% of the electricity demand of plug‐in electric vehicles compared with uncontrolled loading.     

Optimising residential electric vehicle charging under renewable energy: Multi‐agent learning in software simulation and hardware‐in‐the‐loop evaluation

The integration of intermittent renewable energy sources coupled with the increasing demand of electric vehicles (EVs) poses new challenges to the electrical grid. To address this, many solutions based on demand response have been presented. These solutions are typically tested only in software‐based simulations. In this paper, we present the application in hardware‐in‐the‐loop (HIL) of a recently proposed algorithm for decentralised EV charging, prediction‐based multi‐agent reinforcement learning (P‐MARL), to the problem of optimal EV residential charging under intermittent wind power and variable household baseload demands. P‐MARL is an approach that can address EV charging objectives in a demand response aware manner, to avoid peak power usage while maximising the exploitation of renewable energy sources. We first train and test our algorithm in a residential neighbourhood scenario using GridLAB‐D, a software power network simulator. Once agents learn optimal behaviour for EV charging while avoiding peak power demand in the software simulator, we port our solution to HIL while emulating the same scenario, in order to decrease the effects of agent learning on power networks. Experimental results carried out in a laboratory microgrid show that our approach makes full use of the available wind power, and smooths grid demand while charging EVs for their next day's trip, achieving a peak‐to‐average ration of 1.67, down from 2.24 in the baseline case. We also provide an analysis of the additional demand response effects observed in HIL, such as voltage drops and transients, which can impact the grid and are not observable in the GridLAB‐D software simulation.     

Review on use of phase change materials in battery thermal management for electric and hybrid electric vehicles

The battery electric vehicle is evolving and has the potential to replace conventional internal combustion‐based vehicles in the future. Batteries are the major power source of these vehicles. A thermal management system is required for a battery to attain effective operation and long life in all environmental conditions. Although several types of thermal management system are available, there remains a need to address various issues like high power consumption, narrow optimum temperature range and operation in varying climates. Phase change materials can assist in resolving these issues. In this paper, battery thermal management systems for electric and hybrid electric vehicles are reviewed, and challenges and opportunities for battery electric vehicles are discussed. Cooling strategies used in various thermal management systems are explained. Applications of and issues regarding the use of phase change materials in thermal management systems are also reviewed. Potential bottlenecks that need to be addressed in electric vehicle technology are explained, as are important achievement milestones and trends regarding the growth of the electric vehicle industry. It is shown that using graphite can increase thermal conductivity of PCMs by up to 70 W m^{‐ 1}K^{‐ 1}. Some commercially available passive thermal management systems for batteries use wax and graphite, which can increase the driving range of an electric scooter from 30 km to 55 km. Copyright © 2016 John Wiley & Sons, Ltd.     

A comprehensive review on energy management strategies of hybrid energy storage system for electric vehicles

The attention on green and clean technology innovations is highly demanded of a modern era. Transportation has seen a high rate of growth in today's cities. The conventional internal combustion engine‐operated vehicle liberates gasses like carbon dioxide, carbon monoxide, nitrogen oxides, hydrocarbons, and water, which result in the increased surface temperature of the earth. One of the optimum solutions to overcome fossil fuel degrading and global warming is electric vehicle. The challenging aspect in electric vehicle is its energy storage system. Many of the researchers mainly concentrate on the field of storage device cost reduction, its age increment, and energy densities' improvement. This paper explores an overview of an electric propulsion system composed of energy storage devices, power electronic converters, and electronic control unit. The battery with high‐energy density and ultracapacitor with high‐power density combination paves a way to overcome the challenges in energy storage system. This study aims at highlighting the various hybrid energy storage system configurations such as parallel passive, active, battery–UC, and UC–battery topologies. Finally, energy management control strategies, which are categorized in global optimization, are reviewed. Copyright © 2017 John Wiley & Sons, Ltd.     

Integration of plug-in hybrid electric vehicles in a regional wind-thermal power system

This study investigates consequences of integrating plug-in hybrid electric vehicles (PHEVs) in a wind-thermal power system supplied by one quarter of wind power and three quarters of thermal generation. Four different PHEV integration strategies, with different impacts on the total electric load profile, have been investigated. The study shows that PHEVs can reduce the CO₂-emissions from the power system if actively integrated, whereas a passive approach to PHEV integration (i.e. letting people charge the car at will) is likely to result in an increase in emissions compared to a power system without PHEV load. The reduction in emissions under active PHEV integration strategies is due to a reduction in emissions related to thermal plant start-ups and part load operation. Emissions of the power sector are reduced with up to 4.7% compared to a system without PHEVs, according to the simulations. Allocating this emission reduction to the PHEV electricity consumption only, and assuming that the vehicles in electric mode is about 3 times as energy efficient as standard gasoline operation, total emissions from PHEVs would be less than half the emissions of a standard car, when running in electric mode.     

Optimal sizing of autonomous hybrid energy system using supply‐demand‐based optimization algorithm

Hybrid energy systems (HESs) comprising photovoltaic (PV) arrays and wind turbines (WTs) are remarkable solutions for electrifying remote areas. These areas commonly fulfil their energy demands by means of a diesel genset (DGS). In the present study, a novel computational intelligence algorithm called supply‐demand‐based optimization (SDO) is applied to the HES sizing problem based on long‐term cost analysis. The effectiveness of SDO is investigated, and its performance is compared with that of the genetic algorithm (GA), particle swarm optimization (PSO), gray wolf optimizer (GWO), grasshopper optimization algorithm (GOA), flower pollination algorithm (FPA), and big‐bang‐big‐crunch (BBBC) algorithm. Three HES scenarios are implemented using measured solar radiation, wind speed, and load profile data to electrify an isolated village located in the northern region of Saudi Arabia. The optimal design is evaluated on the basis of technical (loss of power supply probability [LPSP]) and economic (annualized system cost [ASC]) criteria. The evaluation addresses two performance indicators: surplus energy and the renewable energy fraction (REF). The results reveal the validity and superiority of SDO in determining the optimal sizing of an HES with a higher convergence rate, lower ASC, lower LPSP, and higher REF than that of the GA, PSO, GWO, GOA, FPA, and BBBC algorithms. The performance analysis also reveals that an HES comprising PV arrays, WTs, battery banks, and DGS provides the best results: 238.7 kW from PV arrays, 231.6 kW from WTs, 192.5 kWh from battery banks, and 267.6 kW from the DGS. The optimal HES exhibits a high REF (66.4%) and is economically feasible ($104 323.10/year) and environmentally friendly. The entire load demand of the area under study is met without power loss (LPSP = 0%).     

A new modeling and solution method for optimal energy flow in electricity‐gas integrated energy system

With the increasing interdependency of electricity and gas, it is necessary to simultaneously investigate electric power system and natural gas system from the perspective of an electricity‐gas integrated energy system (EGIES). As an extension and integration of both optimal power flow (OPF) and optimal gas flow (OGF), optimal energy flow (OEF) is regarded as the cornerstone of the EGIES and lays an essential foundation for further research on the EGIES's operation and analysis considering stochastic conditions and contingency states. The objective of this paper is to develop a generalized mathematical model and a universally applicable simulation tool for the OEF problem. First, natural gas system is modeled in a way similar to electric power system according to electricity‐gas analogy analysis, where gas admittance, gas nodal admittance matrix, and the nodal equation of gas flow conservation are derived. Then, a generalized accurate OEF model is formulated by simultaneously integrating the OPF model and the OGF model as well as their coupling constraints in a unified modeling framework. Furthermore, an available hybrid optimization approach consisting of whale optimization algorithm, MATPOWER, hydraulic calculation iterative program, and nonstationary penalty function method is put forward to solve the OEF problem. The accuracy, feasibility, and applicability of the proposed modeling and solution method is finally demonstrated by analyzing Belgian 20‐node gas system combined with IEEE 30‐bus test system.     

Modeling of integrated renewable energy system for electrification of a remote area in India

Over the years, renewable energy based power generation has proven to be a cost-effective solution in stand-alone applications in the regions where grid extension is difficult. The present study focused on the development of models for optimal sizing of integrated renewable energy (IRE) system to satisfy the energy needs in different load sectors of four different zones considered in Chamarajanagar district of Karnataka state in India. The objective of the study is to minimize the total cost of generation and cost of energy using genetic algorithm (GA) based approach. Considering optimization power factor (OPF) and expected energy not supplied (EENS), optimum system feasibility has been investigated. Based on the study, it has been found that IRES is able to provide a feasible solution between 1.0 and 0.8 OPF values. However, power deficit occurs at OPF values less than 0.8 and the proposed model becomes infeasible under such conditions. Customer interruption cost (CIC) and deficit energy (DE) for all zones were also computed to quantify the reliability of the systems.     

Techno‐economic optimization of hybrid power system using genetic algorithm

This paper presents an optimum sizing methodology to optimize the hybrid energy system (HES) configuration based on genetic algorithm. The proposed optimization model has been applied to evaluate the techno‐economic prospective of the HES to meet the load demand of a remote village in the northern part of Saudi Arabia. The optimum configuration is not achieved only by selecting the combination with the lowest cost but also by finding a suitable renewable energy fraction that satisfies load demand requirements with zero rejected loads. Moreover, the economic, technical and environmental characteristics of nine different HES configurations were investigated and weighed against their performance. The simulation results indicated that the optimum wind turbine (WT) selection is not affected only by the WT speed parameters or by the WT rated power but also by the desired renewable energy fraction. It was found that the rated speed of the WT has a significant effect on optimum WT selection, whereas the WT rated power has no consistent effect on optimal WT selection. Moreover, the results clearly indicated that the HES consisting of photovoltaics (PV), WT, battery bank (Batt) and diesel generator (DG) has superiority over all the nine systems studied here in terms of economical and environmental performance. The PV/Batt/DG hybrid system is only feasible when wind resource is very limited and solar energy density is high. On the other hand, the WT/Batt/DG hybrid system is only feasible at high wind speed and low solar energy density. It was also found that the inclusion of batteries reduced the required DG and hence reduced fuel consumption and operating and maintenance cost. Copyright © 2014 John Wiley & Sons, Ltd.     

Long-term optimization based on PSO of a grid-connected renewable energy/battery/hydrogen hybrid system

This paper presents and evaluates three energy management systems (EMSs) based on Particle Swarm Optimization (PSO) for long-term operation optimization of a grid-connected hybrid system. It is composed of wind turbine (WT) and photovoltaic (PV) panels as primary energy sources, and hydrogen system (fuel cell –FC–, electrolyzer and hydrogen storage tank) and battery as energy storage system (ESS). The EMSs are responsible for making the hybrid system produce the demanded power, deciding on the energy dispatch among the ESS devices. The first PSO-based EMS tries to minimize the ESS utilization costs, the second one to maximize the ESS efficiency, and the third one to optimize the lifetime of the ESS devices. Long-term simulations of 25 years (expected lifetime of the hybrid system) are shown in order to demonstrate the right performance of the three EMSs and their differences. The simulations show that: 1) each EMS outperforms the others in the designed target; and 2) the third EMS is considered the best EMS, because it needs the least ESS devices, and presents the lowest total acquisition cost of hybrid system, whereas the rest of parameters are similar to the best values obtained by the other EMSs.     

Fuzzy energy management of hybrid renewable power system with the aim to extend component lifetime

In this paper, a fuzzy energy management algorithm for a hybrid renewable power system based on lifetime extending is presented. When the system contains two storage elements or more, the selection of the suitable element to be charged or discharged becomes of paramount importance. When the storage elements are of different types, the decision will be difficult. Conventional algorithms that make series of tests to select the storage element choose always the first available element. This way of testing affects badly the most used element and may affect the other storage elements too as they rarely operate under hard load scenarios. In this study, and in order to solve this problem, two fuzzy controllers have been used to manage the energy flow for a hybrid renewable power system. It is composed of: a photovoltaic generator as a main source, a fuel cell and batteries as a storage elements. The controllers operate as master and slave. The master controller gives orders to all the system power converters and to the slave controller as well. The latter is activated only when the storage elements are at the same state of charge. It is charged, instead of the master's, to select the suitable element to be charged or discharged. Its orders are given based on lifetime functions for each element. To examine the proposed algorithm, simulations have been performed under Matlab /Simulink (The MathWorks, Inc., Massachusetts, USA). Comparison and statistics have been carried out to give the percentage of the worked hours for each element in each operating mode. The obtained results show the high performance of the proposed algorithm. Copyright © 2017 John Wiley & Sons, Ltd.     

Economic-environmental energy supply of mobile base stations in isolated nanogrids with smart plug-in electric vehicles and hydrogen energy storage system

The mobile base stations (MBS) are fundamental communication devices that ensure the constant stream of interconnectivity. However, they are mostly installed in off-grid regions. This study investigates the economic-environmental energy supply of a MBS in an isolated nanogrid (ING) that also includes a hydrogen energy storage system (HES), photovoltaic (PV) system, controllable plug-in electric vehicles (PEV) and a diesel generator (DG). A novel mixed-integer second-order cone programming (MISOCP) formulation is proposed to capture the nonlinearities of the various components through a convex optimization model. The study included different uncertainties including the traffic rate of the MBS, driving schedule of the PEVs, and PV generation via a hybrid stochastic programming (SP) and robust optimization (RO) methods. The influence of the coordinated PEV charging strategy, risk-averse RO and multi-objective optimization was studied through various case studies. The outcomes show that coordinated PEV-charging can have a significant contribution in reducing the risks and curtailing both cost and emission objective functions, while using economic-environmental operation model can cut the emissions by 17.70%.     

一种建筑新能源综合利用系统的探索研究

介绍了一种太阳能、地热能综合利用系统在建筑中的设计方案,并对方案进行了可行性分析。以减少能源浪费和温室气体排放为目的,本文针对建筑中可再生能源的收集、转换、补充、储存和系统控制等方面进行了探索研究。提出的综合利用系统由太阳能系统和地源热泵系统两部分组成,可实现供暖、制冷、供热水和供电等四种功能。对系统的可行性分析表明,与常规能源利用系统相比,本方案安全高效,并且无污染。     

Performance analysis of various hybrid renewable energy systems using battery,hydrogen, and pumped hydro‐based storage units

The aim of this research is to analyze the techno‐economic performance of hybrid renewable energy system (HRES) using batteries, pumped hydro‐based, and hydrogen‐based storage units at Sharurah, Saudi Arabia. The simulations and optimization process are carried out for nine HRES scenarios to determine the optimum sizes of components for each scenario. The optimal sizing of components for each HRES scenario is determined based on the net present cost (NPC) optimization criterion. All of the nine optimized HRES scenarios are then evaluated based on NPC, levelized cost of energy, payback period, CO₂ emissions, excess electricity, and renewable energy fraction. The simulation results show that the photovoltaic (PV)‐diesel‐battery scenario is economically the most viable system with the NPC of US$2.70 million and levelized cost of energy of US$0.178/kWh. Conversely, PV‐diesel‐fuel cell system is proved to be economically the least feasible system. Moreover, the wind‐diesel‐fuel cell is the most economical scenario in the hydrogen‐based storage category. PV‐wind‐diesel‐pumped hydro scenario has the highest renewable energy fraction of 89.8%. PV‐wind‐diesel‐pumped hydro scenario is the most environment‐friendly system, with an 89% reduction in CO₂ emissions compared with the base‐case diesel only scenario. Overall, the systems with battery and pumped hydro storage options have shown better techno‐economic performance compared with the systems with hydrogen‐based storage.     

Optimal design of an integrated renewable-storage energy system in a mixed-use building

The rise of mixed-use buildings contributes to the sustainable development of cities but are still met with challenges in energy management due to the lack of energy efficiency and sustainability guidelines. The use of integrated renewable-storage energy systems is a more beneficial solution to this problem over individual solutions; however, most design studies only focused on single-type buildings. Thus, this study aims to optimally design an integrated energy system for mixed-use buildings using HOMER Grid. The objective is to minimize the net present costs, subject to capacity limits, energy balances, and operational constraints. Economic metrics were used to evaluate and compare the proposed system to the varying design cases such as business-as-usual, stand-alone renewable source, and stand-alone energy storage. The case study considered a mixed-use building in a tropical area, with a solar photovoltaic system as the renewable energy source and lithium-ion battery as the energy storage system technology. The results show that the integrated system is the most financially attractive design case. It has a levelized cost of electricity of 0.1384 US$ kWh⁻¹, which is significantly less than the 0.2580 US$ kWh⁻¹ baseline. The system also provides electricity cost savings of 294 698 US$ y⁻¹, excess electricity of 35 746 kWh, and carbon emission reduction of 550 tons annually for a mixed-use building with daily average consumption of 4557-kWh and 763-kW peak demand.