Noubarya solar and wind data [30].
\r\n\t
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He has carried out a great deal of research and technical survey work, and has performed several studies in the above-mentioned areas. He has edited many international books and is an active member of many worldwide architectural associations. 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Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"69047",title:"Hybrid Wind and Solar Systems Optimization",doi:"10.5772/intechopen.89136",slug:"hybrid-wind-and-solar-systems-optimization",body:'It is currently observed that the rapid development of new electrical power sources is denominated by renewable sources for both cases: on-grid and off-grid. The main problem of off-grid stand-alone renewable energy systems (RES) is the fluctuation of power supply which can be avoided using hybrid solar/wind energy systems (HSWES) that allow improving the system efficiency, increasing power reliability, and reducing energy storage requirements for stand-alone applications [1].
In order to solve sustainability and power quality problems, the power transfer from the renewable sources to load must be managed in a proper way. Therefore an energy management process should be proposed to prevent power discontinuity or power wasting so that the loads operate properly.
A major aim of HSWES optimization is to reach the suitable size of each component and the control strategy that provide reliable, efficient, and cost-effective system. Optimization is performed by minimizing (or maximizing) an objective function using a suitable criterion such as net present cost (NPC) and/or the generated electricity price (EP). In this case the cost of avoided CO2 emissions should be taken into consideration [2].
Renewable energies are intermittent sources; hence, hybrid renewable energy system (HRES) is considered an appropriate solution to support electrical requirements especially for remote areas. HRES that incorporates more than one type of renewable energy technologies in a site can help to mitigate the effect of intermittent nature that some of them exhibit and to reach a sustainable source.
As mentioned before, wind and solar systems are considered favorable sources for energy generation due to their availability and being site-power generation. However, a drawback, common to solar and wind utilization, is their unpredictable nature and dependence on weather changes; both of these energy systems would have to be oversized to make them completely reliable. Fortunately, the problems caused by variable nature of these resources can be partially overcome by integrating these two resources or more in a proper combination to form a polygene ration energy system.
HRES is an energy system that includes a number of units and equipment. Different technologies usually exist for alternative types of each of these units. Selecting the optimal alternatives is challenging; to achieve a greater knowledge of how a HRES is designed and optimized, an optimization tool should be used.
The optimization process is followed through an objective function (OF) with respect to some variables in the presence of constraints on those variables. The objective function is either a cost function or energy function which is to be minimized, or a reward function or utility function, which is going to be maximized [3, 4].
An optimization technique is used to find an optimized solution for a particular optimization model. “Optimum” is the word that is used to demonstrate the meaning of best, either maximum or minimum [4]. Problems dealing with the cost will require the best cost to be as less as possible. On the other hand, problems dealing with profit will see the maximum value as the best answer. There are several well-known optimization techniques depending on the model type, deterministic or stochastic, such as:
Linear programming (LP), a type of convex programming, is applied in the cases in which the OF is linear and the constraints are specified using only linear equalities and inequalities.
Second-order cone programming (SOCP) is a convex program and includes certain types of quadratic programs.
Integer programming is applied in the case that some, or all, variables of the linear solution are constrained to have an integer value. This is generally more difficult than linear programming.
Quadratic programming permits the OF to have quadratic terms, while the feasible set of solution must be identified with linear equalities and inequalities. Some specific cases of the quadratic term lead to a type of “convex programming.”
Fractional programming is concerned with the optimization of ratios of two nonlinear functions. The special class of “concave fractional programs” can be transformed to a “convex optimization” problem.
Nonlinear programming is used for the general case of the objective function and/or the constraints containing nonlinear parts. Cases of convex program affect the difficulty of the solution.
Stochastic programming is applied when some of the constraints or parameters depend on random variables.
Combinatorial optimization is applicable when the set of “feasible solutions” is, or can be, reduced to a discrete one.
Infinite-dimensional optimization is applied in the case that the set of “feasible solutions” is a subset of an “infinite-dimensional space.”
Stochastic optimization is used in the case of random function measurements or random inputs.
Robust programming is, like stochastic programming, an attempt to capture uncertainty in the data underlying the optimization problem. Robust optimization targets to find solutions that are valid under all possible realizations of the uncertainties.
Heuristics and “metaheuristics” use limited or no assumptions concerning the problem being optimized. Usually, heuristics do not guarantee that an optimal solution has to be found. In fact, heuristics are used to find approximate solutions for complicated optimization problems.
In addition to these techniques a growing interest in the application of artificial intelligence (AI) techniques to power system engineering. AI techniques, unlike strict mathematical methods, have the ability to adapt to nonlinearities and discontinuities commonly found in power systems. The best known algorithms in this class include:
Evolution programming
Genetic algorithms
Simulated annealing
Tabu search
Neural networks
As power system must be sustainable, secure, and environmentally safe, the basic function of a HRES is to supply power with quality electrical energy, reliably and economically. As such, optimization plays an important role. This enables to minimize the cost of operation, initial investment, and environmental impacts and maximize reliability, quality, and efficiency.
The optimization of
To reach an appropriate HRES the system should be designed according to techno-economic and environmental measures to fulfill physical and operational constraints. For cost optimization, system design seeks the configurations and control strategies that achieve the lowest total cost over the system lifetime. The lifetime cost which subjects to the system typically consists of two or more components. The life of the system is usually considered to be the life of the PV panels—which are the elements that have a longer life-span.
The OF in this case is the system net present cost (NPC), which consists of initial investment cost in addition to the discounted present worth of all future costs over the system lifetime. The system cost is the sum of all its components, e.g., PV, WT, battery, converter, and any other necessary devices, in addition to installation cost. Component costs comprise all costs: capital, replacement, operational and maintenance, and fuel consumption. Some of these costs depend on the selected control strategy.
For PV/WT/DG/battery bank system, the objective is to minimize the net present cost (NPC) under load and power constraints [5]:
Subject to the constraints
where
The common current optimal sizing tool is the available software packages that can be helpful for real-time system integration.
The number of simulations and time required for calculation increases with the increase in number of optimization variables. Therefore, the selection of a quick and accurate optimization technique is very important.
Simulation software tools are the most common tools for evaluating performance of the hybrid solar/wind systems. Connolly [6] listed 67 software tools available for analysis of hybrid energy systems, studied 37 of them, and identified the suitable tools for different objectives. Some of the most widely used software tools for hybrid energy systems are summarized as follows:
Improved Hybrid Optimization using Genetic Algorithm (iHOGA) is a simulation and optimization software developed in C++ by the Electric Engineering Department of the University of Zaragoza, Spain [7]. This software is a tool for optimum sizing of hybrid renewable energy system. This tool uses double genetic algorithms for optimization. The main algorithm is used for the system components while a secondary algorithm is added for control strategy. The software can simulate and optimize system of any size (size from Wh to MWh even through GWh daily consumption). Optimization is achieved by minimizing total system costs through its useful lifetime. The program allows mono-objective as well as multi-objective optimization. The mono-objective and multi-objective optimizations are achieved for stand-alone and grid-connected hybrid renewable energy systems. The program modeling provides various outputs such as size of the PV generator in Wp and its ideal tilt, battery capacity in kWh, battery lifetime in years, initial investment, NPC with breakdown of the component, lowest cost of energy, and CO2 emissions of the system in CO2/kWh [7].
Dufo-López et al. used iHOGA software for the optimization of the electrical supply of a hospital existed far from the electric grid in Kalong (Democratic Republic of the Congo), which is presently powered by a diesel-battery system [8]. The results showed that adding solar photovoltaic (PV) to a diesel-battery system to supply the required load could obtain a 28% reduction in energy cost and 54% reduction in the fuel consumption reducing CO2 emissions lower than the current diesel-battery system.
Fadaeenejad et al. presented an analysis and optimization for a HRES (PV/WT/BAT), which are designed for rural electrification in Malaysia [9]. The evaluation of the performed optimization was accomplished using iHOGA software. The obtained results illustrate that the wind energy is used as a supportive source of energy for many locations in Malaysia, and the hybrid renewable energy systems are cost-effective for these rural areas.
Anita Gudelj et al. presented an optimal sizing model for hybrid energy system (HES) that aims to minimize the total cost through the useful life of the system and CO2 emissions to meet the desired consumption [10]. The iHOGA program was used to simulate the system operation and calculate technical economic parameters for each configuration. The results showed that the hybrid energy systems have considerable reductions in CO2 emission and cost of the system. Using a diesel generator as a backup source, for the PV/WT/battery system, was found to be the best solution to guarantee the reliable supply without any shortage of the required load under the weather data change.
The National Renewable Energy Laboratory (NREL) introduced a Hybrid Optimization Model for Electric Renewable (HOMER) package. HOMER uses hourly load and weather data inputs to perform hourly simulations for techno-economic analysis of hybrid energy systems [11]. HOMER performs three tasks: simulation, optimization, and sensitivity analysis. It facilitates the optimization of simulated renewable energy systems to minimize NPC for a given set of constraints.
Mustafizur Rahman et al. [12] suggested seven scenarios of combining hybrid renewable energy technologies with diesel generator to minimize the economic and environmental concern effects of its use. The suggested scenarios were (100, 80, 60, 50, 35, 21, and 0%) renewable resource penetration. A case study for the remote community of Sandy Lake, Ontario, was conducted. The different scenarios modeled are developed by using HOMER software. The aim of this study was to find the best combination of hybrid renewable energy systems from the available resources for a particular off-grid location in Canada. The results showed that using 80% renewable energy scenario can achieve the demand with 72% higher COE but 83% lower CO emissions than 0% renewable fraction—100% diesel-battery scenario.
In a similar study, Ngan et al. [13] focused on the technical and economic feasibility of “the hybrid energy systems (PV/WT/DG)” in a southern city of Malaysia using HOMER simulation software. They considered seven different system configurations: stand-alone diesel generator system, hybrid PV-diesel system, PV-diesel system with battery storage, hybrid wind-diesel system, wind turbine-diesel system with battery storage, wind-solar-diesel system, and wind-solar-diesel system with battery storage.
Targeting to study technical and economic performance of wind/diesel/battery (W/D/B) system supplying a remote small gathering of six families, HOMER package was used [14]. Net present cost (NPC) and cost of energy (COE) are used as economic criteria, while % of power shortage is the measure of performance. Optimum system configurations are estimated for two sites. Simulation results showed that W/D/B systems are economical for the assumed community sites as the price of generated electricity was about 0.308 $/kWh, without taking external benefits into considerations. W/D/B systems were found to be more economical than diesel-alone system.
A case study of the performance and optimization of a HRES supplying a water desalination system for irrigating a small greenhouse hydroponic cultivation was presented by Khatab et al. [15]. The study presented optimization of two hybrid systems: photovoltaic/wind turbine (PV/WT) with and without backup diesel generator. The results showed that COE of PV/WT system is less than that of PV/WT/diesel, while there is no capacity shortage in the case of PV/WT/diesel.
It is developed by Natural Resources Canada to evaluate the energy production, costs, emission reduction, and financial viability for various types of renewable and nonrenewable energy systems [16]. It performs economical comparison between conventional system and proposed system. A study examined the potential for a 10-MW PV power plant in Abu Dhabi using RETScreen modeling software to forecast the produced energy, financial feasibility, and GHG emissions reductions [17]. Initial results showed high energy production potential and saving a high amount of tons of GHG emissions annually.
This software package is developed by the National Renewable Energy Laboratory (NREL) of the United States Department of Energy in cooperation with the University of Massachusetts. This hybrid simulation software can run simulation for time intervals from 10 mintues to 1 hour. The NREL recommends the HYBRID2 for the thermal loads [18].
TRNSYS is a transient system simulation program developed by the Solar Energy Laboratory, University of Wisconsin-Madison, USA. It has a modular structure in which components of the system are specified by the user [19]. It can simulate almost all thermal and renewable power generation systems.
A review of the optimization techniques used to select HES that minimize initial and operating cost was presented by Erdinc and Uzunoglu [20]. The compared techniques were (GA), “simulated annealing” (SA),“particle swarm optimization” (PSO), and HOMER. The approach for component sizing is based on demanded load, renewable resources availability, and climatic conditions.
For a touristic resort in Malaysia, Hossaina et al. [21] suggested a stand-alone HRES that includes WT, PV, DG, converter, and battery as energy sources to replace the existing diesel generators. The estimated daily average and peak load were 13,048 and 1185 kW, respectively. The system techno-economic was achieved using HOMER software, and the results exhibited that the hybrid system has lower NPC and COE than the existing diesel system.
In a similar study, Olatomiwa et al. [22] investigated different (PV, WT, and DG) power configurations in six “geopolitical” zones of Nigeria, also by HOMER [23]. The result denoted that the PV/DG/battery system configuration is the optimum configuration in the cases of diesel fuel price of $1.1–1.3/l, exhibited lower fuel consumption, and reduced CO2 emission.
“HOMER” was also applied to investigate the possibility of providing 200 households in remote area in Ethiopia with electricity using HSES [24]. The results revealed that PV/DG/battery system is the most “cost-effective” using load following strategy. The authors concluded that this study could be considered applicable for similar climatic condition regions.
Another comparable feasibility analysis of (HRES) supplying load requirements of a rural village, of 50 families, in Bangladesh, far from the grid was performed using “HOMER package” [25]. The “annual average load” was 213 kWh/day; the results indicated that for this location, load profile of the feasible system is also PV/WT/battery where the NPC had a total of $224,345 and COE of 0.161 $/kWh with no CO2 emission.
A fuzzy logic power controller was proposed by Alam et al. [26] to provide continuous power supply, from a hybrid WT/PV/fuel cell power system with battery, for remote area. The simulated system configuration was 20 kW WT, 80 kW PV array, and 10 kW fuel cell. Excess power was directed to the batteries first and then to the electrolyzer. In the case the optimum system, the results showed that power shortage had reached 254 kWh/year, which represented a high percentage of the total load. Also the estimated cost of energy was very high (1.045 $/kWh).
Ajao et al. [27] performed an economic analysis of PV/WT system for a Nigerian area. The authors concluded that the proposed system is expensive because of high capital and installation costs. The authors did not take into consideration the reduction of greenhouse gas emission which would improve the cost-effectiveness of the system.
Seeking the optimum design of economically feasible HES to feed a load which had seasonal variations, Fulzele et al. used iHOGA to simulate and optimize the system [28]. The results showed that 99% of the required load was covered by the system, subject to operational constraints and control strategies. Nevertheless, the results did not take into consideration that the excess energy was about 25% and minimizing this value would reduce the cost of energy (COE).
Energy management is considered as an optimization action. Energy management increases usable energy, decreases wasted energy, and has the additional benefits of optimizing energy systems and improving their reliabilities.
The electrical power generated by renewable sources such as wind and solar power is affected by environmental conditions resulting in problems in load side. When there is no sun or the weather is cloudy, the power amount to be generated by solar energy changes. Accordingly, wind does not blow at the same speed all the time; it is discontinuous. Henceforth, energy amount to be generated from these sources is variable. Energy management processes are developed to prevent problems like discontinuities that occur due to either weather changes or sudden load changes.
Different methodologies and techniques used to develop a successful energy management strategy, for both stand-alone hybrid renewable energy systems and the grid-connected hybrid renewable systems, were investigated by Olatomiwa et al. [22]. The authors focused on energy management based on “linear programming,” “intelligent techniques,” as well as energy management by “fuzzy logic controller.” The authors emphasized that selecting the suitable energy management strategy is necessary to control the energy flow in the system that increases reliability, decreases electricity shortage, reduces the “COE,” and increases the system lifetime.
In a study that investigated the performance of various possible configurations using iHOGA software, the achieved optimum configuration was further improved by adapting the daily load pattern to the periods of high renewable generated energy to increase direct energy utilization rather than charging batteries [29]. This will result in effective minimization of battery bank size.
The study objective was to design and simulate HSES for remote area in Egypt. The study was performed using iHOGA simulation and optimization package to decide on the optimal size of each component and control strategy. The input data for the optimization are weather data of the selected location, nominated system component cost, and technical parameters. Financial parameters, interest and inflation rates, installation, and operational costs are also included. The proposed system components are PV/WT/batteries/DG/inverter and charge regulator. The suggested load in this case study is energy required for a desalination unit (DU). The system is installed in NRC farm in Noubarya. The considered system configuration is shown in Figure 1.
The system configuration.
NRC farm is in a remote area that is located between 30°40′0” N and 30°4′0″ E. The average temperatures for winter and summer are about 14 and 28°C, respectively. The farm is a research pilot plant for agriculture, animal, and fish production. Frequent electricity shortage is observed due to instability of low-voltage grid power in the area.
In this case study, the HSES provides a reverse osmosis desalination unit (DU) with electricity. The required daily desalinated water is about 60–65 m3. The required power for the DU is the sum of powers required for three types of pumps included in the DU: “5 HP high-pressure pump” (3728 W), “distribution pump” (1000 W), and a “feed pump” (1870 W in the case of feeding rate is 7 m3/hour). To produce 60–65 m3/day, the DU should be fed by 110 m3 of brackish water per day. The first suggested load profile in this study; referred to as base case, the high-pressure pump, the distribution pump, and feed pump are designated to work simultaneously. Hence, the peak load requirement is about 6.6 kW continuously from 00:00 to 16:00, and the average estimated daily energy consumption is 105.6 kWh (Figure 2).
DU base case load profile (base case).
The input climatic data for the proposed site are obtained from NASA Surface Meteorology and Solar Energy [30]. Table 1 represents the monthly average of solar radiation and wind speed data for the selected area.
Month | Solar radiation (kWh/m2) | Wind speed (m/s) |
---|---|---|
Jan | 2.92 | 3.8 |
Feb | 3.78 | 4.1 |
Mar | 5.10 | 4.1 |
Apr | 6.40 | 3.9 |
May | 7.40 | 3.9 |
Jun | 8.13 | 3.8 |
Jul | 7.92 | 3.8 |
Aug | 7.24 | 3.8 |
Sep | 5.93 | 3.8 |
Oct | 4.38 | 3.7 |
Nov | 3.22 | 3.6 |
Dec | 2.69 | 3.8 |
Average | 5.43 | 3.8 |
Noubarya solar and wind data [30].
As mentioned above, the system components are PV, WT, inverter, batteries, and DG. A number of monocrystalline and polycrystalline PV modules (in the range of 100–280 W/panel) were used in the simulation to select the suitable size. The initial panel cost is in the range of $143–455, while O&M cost of each panel is 1.43–4.55$/year. The panels’ lifetime is considered to be 25 years. The WT types used in simulation are “Bornay” and “Hummer,” both of 3–30 kW power range, and its hub height is considered to be between 15 and 18 m. The initial cost of WT is between $9821 and 44,200, its replacement costs $7800–33,800, and O&M cost $196–884/year. The lifetime of “Bornay” and “Hummer” are assumed to be 15 and 20 years, respectively. A backup DG is 3–4 kVA, and the battery bank is in the range of 180–3360 Ah with 80% depth of discharge used. The system also comprises an inverter which is scaled according to the maximum peak load. The inverter type is ACME: 8000VA CARG. The above-stated values are attained from iHOGA database.
The software package used in this study is
In “cycle charging strategy,” if the total (PV&WT) generated energy is greater than the load requirements, the excess energy charges the batteries. When batteries’ state of charge (SOC) reaches its maximum value, the charging process is set off, while if (PV&WT) energy is lower than the load, the rest is covered by the battery bank. If the battery charge drops to its minimum SOC, the controller unit sets off discharging process and turns DG on to cover the unmet load. As it is well known, it is better to run the DG at its rated power to reach higher efficiency of fuel consumption; DG will serve the load and the extra power and, if any, will be used to charge the batteries to its maximum SOC.
Both of the above strategies are examined to select the optimal strategy for the given system constraints.
The main target of the suggested system design is to reach the optimum solution of a HRES in terms of economic and technical conditions subject to the operational strategies and physical constraints. In this method, the possible optimum system configuration is the one that satisfies the user-defined constraints in accordance with the objective function. The objective function is to minimize NPC which consists of initial cost, replacement cost, maintenance, and running cost of system components like PV, WT, DG, batteries, converter, and etc. [10, 12, 31].
Objective function:
where
There are many constraints that are considered to ensure that the generated electricity would cover the load such as the minimum renewable fraction (75%), levelized cost of energy (5 $/kWh), and the maximum percentage of annual unmet load which is defined to be 5%.
The suggested system is simulated to reach the optimum value of the selected objective function under the following constraints: minimum renewable fraction (RF) 75%, levelized cost of energy 5 $/kWh, and the maximum percentage of annual unmet load 5%. The simulated optimization results (for base case load) are shown in Figure 3.
Results of NPC as a function of generations.
Figure 3 exhibits the estimated optimum NPC and CO2 emission of a number of simulation runs. The optimization results of the base case showed a minimum NPC of $162,034, COE of 0.17 $/kWh, and unmet required load of 1.3%. HSES optimum configuration is 53 parallel series of PV panels, 4 modules each of 100Wp rated power, 24 batteries connected in series each of 1340 Ah, 1 WT of 14.7 kW at 14 m/s, 8 kVA inverter, and 3 kVA (AC) diesel generator. Figure 4 illustrates the annual distribution of energy generation.
Annual distribution of energy.
It is observed in Figure 4 that almost all the yearly demand, except 495.8 kWh/year, is fulfilled by the HSES generated energy, which account for less than 1.5% of total load, the CO2 emissions of 11,950 kg/year. It could be also observed that the energy charging batteries are 10,768 kWh/year, (about 21% from the total generated power), and the excess energy is 8278 kWh/year, (about 16%).
The total generated energy is about 50,800 kWh/year, while the total load that is directly supplied by energy sources is 28,386 kWh/year, so the utilization of the energy sources is about 55.9%. As the efficiency of both inverter and battery charger is high, 98 and 95%, respectively, then the main losses result from battery charging and discharging efficiency which is 85%. As the charging and discharging energy amounts to 20,407 kWh/year, then energy losses are about 3000 kWh. The cost of different HSES simulated components are shown in Table 2.
Cost element | Initial cost ($) | Percentage (%) |
---|---|---|
PV panel cost | 37,282 | 23 |
WT cost | 27,246 | 16.8 |
DG cost | 21,667 | 13.3 |
Battery bank cost | 36,940 | 22.8 |
Inverter cost | 10,952 | 6.7 |
DG fuel cost | 16,687 | 10.3 |
Charge reg. cost and AUX | 11,256 | 6.4 |
Component costs of the optimized HSES.
It could be seen from the above table that the major cost items are PV panels and “battery bank” which represent about 23 and 22.8% of the total NPC. The high cost of the “battery bank” indicates that power generation profile does not match the load pattern; therefore a considerable part of generated energy has to be stored to cover the load when generated energy is not enough.
Regarding energy management, the main objective is to cover the DU load while minimizing the NPC and accordingly the cost of water desalination. From the results of the base case simulation, it is clear that, at some periods, a considerable amount of the generated energy does not match the load profile; therefore, it is directed to charge batteries. Hence, the amounts of energy charging the batteries and “excess energy” are excessive in some months as exhibited in Figures 5 and 6. Reducing these values would improve the system performance; hence, the configuration should be further adapted by means of load pattern managing. Load profile management could be achieved through matching its pattern with the power generation profiles. This would decrease the number and cost of batteries and consequently the total NPC.
Monthly average energy charging battery.
Monthly average excess energy.
Considering the hourly simulation results for the highest months of excess energy and energy charging battery amounts, different load patterns were proposed and simulated, of which the following four arrangements represented the most promising patterns to increase the direct utilization of the generated energy as shown in Figure 7.
Suggested load profiles.
As exhibited in Figure 7, the suggested load profile 1 proposed that all pumps are turned on from 05:00 to 21:00 requiring 6600 kWh. Load profile 2 schedule is based on the assumption that the “feed pump” is running from 00:00 to 08:00, while the “high-pressure” and “distribution” pumps are scheduled from 08:00 to 16:00. Finally all pumps work simultaneously from 16:00 to 24:00 as shown in Figure 8. This pattern is scheduled to fit high power period that is generated from the PV panel in the middle of the day and also the wind power at the night which is the period of high wind speed.
Effect of suggested load profiles on NPC, battery cost, and energy battery charging.
“Load profile 3” is arranged as feed pump running from 00:00 to 06:00 and from 22:00 to 24:00, while “high-pressure” and “distribution” pumps begin to work simultaneously, along with the “feed pump,” from 08:00 to 16:00. The feed pump is switched off from 06:00 to 08:00 and 16:00 to 22:00, while the other two pumps remain operating. In the case of proposed “load profile 4,” the feed pump schedule is running three periods: from 00:00 to 04:00, from 20:00 to 24:00, and from 04:00 to 08:00. The “high-pressure” and “distribution” pumps start working when the feed pump is switched off except from 08:00 to 16:00 when all the pumps are working simultaneously. This profile is supposed to be fitting the period of high PV and WT energy generation to increase direct energy utilization.
The simulated optimization results of base case and the suggested four load profiles are exhibited in Tables 3 and 4.
Case no. | NPC ($) | COE ($/kWh) | PV cost ($) | WT cost ($) | Battery cost ($) |
---|---|---|---|---|---|
Base case | 162,034 | 0.17 | 37,282 | 27,246 | 36,940 |
Profile 1 | 138,249 | 0.15 | 34,545 | 27,246 | 21,274 |
Profile 2 | 149,266 | 0.16 | 31,808 | 27,246 | 28,692 |
Profile 3 | 137,694 | 0.15 | 30,440 | 27,246 | 18,444 |
Profile 4 | 137,011 | 0.15 | 30,440 | 27,246 | 18,451 |
Cost results of the suggested load profiles ($).
Case no. | NPC ($) | Charge battery (kWh/year) | Excess energy (kWh/year) |
---|---|---|---|
Base case | 162,034 | 10,768 | 8278 |
Profile 1 | 138,249 | 6231 | 6312 |
Profile 2 | 149,266 | 8768 | 4544 |
Profile 3 | 137,694 | 5294 | 3590 |
Profile 4 | 137,011 | 5241 | 3665 |
Results of the suggested load profiles.
It is noticed from the summarized results in the above tables that load profile 4 has the lowest NPC and COE (137,011$ and 0.15 $/kWh) among the four suggested profiles in addition to minimum value of energy charging the batteries (5241 kWh/year). The suggested load profiles also demonstrate the effect of decreasing the energy charging the batteries on the NPC as illustrated in Figure 8.
It is clear from Figure 8 that the lowest battery charging energy is that of load profile 4 (5241 kWh) which is the case of lowest NPC configuration (137,011 $). Table 5 exhibits the energy utilization, battery charging energy, and energy loss as a percentage of the total energy. It also exhibits battery cost as a percentage of the total energy system costs.
Case no. | Utilization (%) | Energy charging batteries (%) | Energy loss (%) | Battery cost (%) |
---|---|---|---|---|
Base case | 55 | 27 | 22 | 22.8 |
Profile 1 | 66 | 16 | 18 | 15.4 |
Profile 2 | 65 | 22 | 13 | 19.2 |
Profile 3 | 73 | 13 | 14 | 13.4 |
Profile 4 | 73 | 13 | 14 | 13.4 |
Load profiles’ results (percentages).
The above tables showed that decreasing excess energy and energy charging batteries reduced NPC cost. Increased utilization of the location resources is achieved through fitting the peaks of demanded load with the periods of high power generation which affected energy components, reducing generation and storage components’ sizes.
The optimization results for HRES under study, considered as the base case, are NPC is $162,034, COE is 0.17 $/kWh, and the unmet load (energy shortage) is 1.3% of the total required energy, while the renewable fraction is about 75%. However, this optimum configuration showed high values of energy charging batteries (which means higher battery bank capacity) and excess energy which represented 21 and 16%, respectively. At the same time, the total load that is directly supplied by energy sources was only 55% of total generated energy. This indicated that the load profile does not match the renewably generated energy; hence, different load scenarios were investigated. The simulation results of the best reached load pattern, referred to as “load profile 4,” are as follows:
Maximizing direct use of renewable generated energy causes reduction in system component sizes. The results showed that “load profile 4” has the lowest NPC and COE values (137,011 $ and 0.15 $/kWh) and minimum energy charging batteries (5241 kWh/year), which suggests that NPC and COE are directly proportional to energy charging battery.
Managing load pattern to reach the best fitted profile has decreased NPC by 15.4%, charging energy battery by 51.3%, the cost of batteries by 50%, COE by 11.7%, and the excess energy by 55.7%, while the utilization of the energy sources is increased by 18%, compared to the base case configuration.
In short, “load profile 4” caused significant improvement on the following parameters:
NPC has decreased by 15.4%.
Battery charging energy has decreased by 51.3%.
The cost of batteries has decreased by 50%.
The cost of energy has decreased by 11.7%.
The excess energy has decreased by 55.7%.
The utilization of the energy sources is increased by 18%.
Taking environmental impacts of CO2 into consideration will further decrease the cost of system generated energy.
Kidney is one of the tissues affected by age. Aging involves cellular and structural changes inside the kidney and notably implicates with comorbidity, related to cardiovascular disease aging [1, 2]. The Centers for Disease Control and Prevention estimated that 72 million people in the United States (20% of population) will be 65 years old or more at 2030. Eurostat estimated, around 28% Europeans will be 65 years old at 2060. The increasing number of older adults will be predicted to increase aging related kidney disorders as well [3].
\nAging kidney causes the elderly susceptible to clinical deterioration from ordinary stimulation that younger individual can compensate, including acute renal injury, volume depletion or overload, sodium and potassium level disorders, and toxic reaction against kidney excreted drugs [4]. Deficiency of kidney function is associated with death in all populations [2]. Several studies have shown that the decrease of function related to be structural (glomerulosclerosis, tubular atrophy and interstitial fibrosis) and functional (reduced glomerular filtration rate, proteinuria, decreased ability to concentrate or dilute urine, electrolyte imbalance and ion transport disorders, changes in hormonal function, reduced drug excretion) [1, 5].
\nAs one of the organs with the fastest aging rate, kidney shows several age-related decline in both structural and functional. The renal parenchyma decreases about 1%, and creatinine clearance or GFR decrease is about 1.0 mL/minute per 1.73 m2 per year in elderly subjects [6]. In normal aging kidneys, 30% of the glomerulus are damaged and represent diffuse glomerular sclerosis by age 75. Meanwhile the remaining glomerulus denote impaired filtration ability [2]. This could explain why the prevalence of chronic kidney disease (CKD) and end-stage renal disease are very common in the elderly [6]. Chronic kidney disease is a major growing health and economic burden. About 8–13% of the world’s population suffers from CKD [7].
\nAge-related loss of kidney function has been recognized for decades. The cross-sectional population-based study by The National Health and Nutrition Examination Survey supports the theory of age-related decline in kidney function, although some other subjects did not have an absolute decline in kidney function. Although the rate of this decline is low, the process may have negative effects on many organ systems and thus reduce overall health and physical function in the elderly individual [6]. Epidemiological, clinical, and molecular evidence suggest that aging is a major contributor to the increased incidence of acute kidney injury and CKD. Renal function recovery after an episode of acute kidney injury is significantly worse in elderly patients. Reduction of regenerative potential, which is a feature of the aging process, may be caused by aging cells [8].
\nWith increasing age, many individuals show progressive reductions in glomerular filtration rate (GFR), renal blood flow (RBF), and loss of nephron function with wide variability between individuals [9]. The aging kidney undergoes complex changes that affect the pathology of the kidney. The underlying molecular mechanisms could be the target of future therapeutic strategies [8].
\nThe accumulation of old cells explains the ineffectiveness of cell repair and the loss of functional ability wherein studies have shown that the removal of old cells results in delayed kidney aging. Other potential mechanisms are autophagic changes responding to renal stress and the inflammatory response [8].
\nAging is a natural biological process, progressive and inevitable characterized by a gradual decline in cellular function as well as progressive structural change of organ systems. This reduction means a reduced capacity to maintain homeostatic control over essential functions and ultimately results in the death of the organism [5, 10]. These anatomical and physiological changes are called senescence, a term that describes changes more related to age than changes caused by disease [11].
\nThe aging mechanism does not stand alone but involves the interaction of several factors. These factors were put forward by Fontana and Klein: (1) Oxidative stress will damage protein and DNA, this condition is also followed by a decrease in the ability to repair DNA, and an imbalance in the mitochondrial and nucleus genome; (2) The existence of chronic inflammation which increases with age; (3) Changes in fatty acid metabolism, which acid is associated with insulin resistence. So that there is excess free fatty acids in plasma; (4) There is an interruption of normal cell physiology due to excessive excess of metabolic and product such as Advanced glycolysis end products (AGE), amyloid, and other proteins; (5) the sympathetic nervous system and the activation of angiotensin system and changes in the neuroendocrine system; (6) post-mitotic cell loss, resulting in a decrease in the number of neurons and muscle cells and damage to cell structure and function in all tissues and organs [12].
\nThe kidneys have higher activity (highly energetic) than other organs, thus they produce a lot of free radicals as intermediate substances (as free radical stores) which cause oxidative stress which results in impaired signal delivery, increased apoptosis, decreased ability to regenerate cells, and fibrosis in the kidneys. This characteristics make kidney one of the organs that ages more rapidly than the other organs [13].
\nIn aging kidney, there are interactions of genetic factors, environmental changes, and cellular dysfunction that lead to the typical structural and functional changes. The exact biological and cellular mechanisms responsible for aging are not yet known. One of the most popular theories of aging is the theory of free radicals or oxidative stress. This theory is based on the fact that cells are under chronic oxidative stress due to an imbalance between pro oxidants and antioxidants [14, 15].
\nThis theory states that aging is caused by accumulated oxidative damage. As organisms getting older, they produce more free radicals, some of which are not completely neutralized by the endogenous antioxidant defense mechanisms. These free radicals then react with biomolecules and cause the accumulation of toxic oxidative products that lead to oxidative stress and accumulation of toxic oxidative products [10].
\nThe kidneys depend on aerobic metabolism and oxidative phosphorylation for the production of the energy required for tubular reabsorption [16]. Due to their high metabolism, the kidneys are very susceptible to oxidative damage, and several trials have shown that oxidative stress can cause or accelerate the progression of kidney disease and its complications [17]. Most of the age-dependent renal changes such as excessive fibrosis, general lack of regenerative ability, and increased apoptosis in cells that determine healthy kidney function are often associated with excess OS [18].
\nThe mechanism of free radicals damaging cells can be in several ways: (1) free radicals will damage the double layer lipid membrane causing disruption of the fluidity and permeability properties of the membrane. (2) Free radicals enhance protein cross-linking processes, especially those mediated by sulfhydryl groups, resulting in degradation and loss of cell activity. (3) DNA damage induced by free radicals causes the breakdown of DNA strands. (4) Impaired function of cellular receptors, neurotransmitters and hormonal responses due to oxidative damage to carbohydrates [17].
\nResearch shows that in the aging process of organisms, excessive oxidative stress can activate many pro inflammatory pathways, including the NF-κB signaling pathway. Oxidative stress also induces ongoing regulation (chronic) of pro inflammatory mediators (such as TNF-α, IL1-β, IL-6, COX-2, iNOS) which lead to tissue and organ aging [19]. Besides, oxidative stress is also involved in various disease processes and degenerative syndromes. In mild oxidative stress due to normal metabolism results; The resulting biomolecular damage cannot be completely repaired or eliminated by cell degradation systems, such as lysosomes, proteasomes, and cytosol and mitochondrial proteases [12].
\nIn oxidative stress, oxygen-free radicals are formed excessively as well as H2O2, so that the body’s protection system such as catalase and glutathione peroxidase enzymes can no longer neutralize all oxygen free radicals that are formed. Furthermore, if H2O2 reacts with Fe2+ and Cu2+, hydroxyl free radicals are formed through the Fenton and Haber-Weiss reaction. The hydroxyl radical is a very reactive species.
\nReactive oxygen species and reactive nitrogen species can attack all types of biomolecules, especially unsaturated fatty acids which are important components of phospholipids making up cell membranes. This accumulation of oxidative damage is considered to be an important mechanism underlying aging and an increase in age-related pathology, as well as a progressive decrease in the functional efficiency of various cellular processes [14, 15]. Among the ROS, the most widely known, are superoxide ion (O2• −), hydrogen peroxide (H2O2), and peroxyl radicals (OH•), while RNS is nitric oxide (NO) and peroxy nitrite (ONOO−) (\nFigure 1\n) [18]. At the molecular level, increased oxidative damage and its products have been reported. This contributes to a chronic inflammatory response with the accumulation of macrophages and lymphocytes in the interstitium [1].
\nSchematic mechanism of cell damage according to the theory of free radical aging. In this case the kidney.
In certain conditions that are triggered by oxidative stress, there is an insufficiency of endogenous antioxidants, both enzymatic and non enzymatic. Enzymatic antioxidants include glutathione peroxidase, catalase, and superoxide dismutase. Non enzymatic antioxidants include vitamin E, vitamin C, thiol antioxidants (glutathione, thioredoxin, and lipoic acid), melatonin, carotenoids, natural flavonoids, and others [7, 20]. Studies on old mice kidneys support the theory that there is a decreased antioxidant capacity and decreased levels of Cu/Zn-SOD, catalase, and GSH reductase.
\nSeveral processes can suppress oxidative stress: (1) reduce radiation and pollutant that affect the environment; (2) enhance antioxidant both endogen and exogen to neutralize ROS before it oxidizes cells; (3) suppress oxidative stress formation through stabilization of production and efficiency of energy in mitochondria [12].
\nReactive oxygen species are oxygen-derived oxidizing compounds that are highly reactive, consisting of free radicals and non-radicals. Apart from oxygen derivates, free radicals also come from nitrogen derivatives. Free radicals are atoms or molecules with unpaired electron, which are very unstable and very reactive. Therefore, they try to pair up with the free electrons around them, so that they will form other free radicals or paired electrons, where their radical characteristics may be lost. If a new free radical is formed, it is also unstable and react with other molecules to produce other free radicals or non-radical molecules due to the electron pair of the new formed molecule. Thus, a free radical chain reaction will form excessive free radicals.
\nFree radicals include superoxide anion (O2), Hydroxyl radicals (OH−), hydroperoxyl (HO2) Peroxyl radicals (RO2), alkoxyl (RO), carbonate (CO3−), nitric oxide (NO), nitrogen dioxide (NO2), peroxy nitrite (ONOO−), nitroxyl ion, purine radical. Meanwhile, non-radicals are hydrogen peroxide (H2O2), organic peroxides (ROOH), ozone, oxygen siglet, and lipidperosides.
\nFree radicals are formed in cells through oxidation and reduction of one electron. If the formed free radicals are the oxygen’s derivative, they are called reactive oxygen species (ROS). Reactive nitrogen species (RNS) are those derived from nitrogen. All of aerobic cells will produce reactive oxygen and nitrogen species (RONS) which have critical role in cell’s aging and age-related diseases. The formation of RONS is not only limited to its deleterious effects but also plays a role in energy extraction from organic molecules, immune defense, and signaling processes [18, 20].
\nSources of RONS can be endogenous and exogenous. Endogenous sources of RONS are generated by several major enzymatic processes such as cellular respiration (by the mitochondrial electron transport chain) or the activity of the nicotinamide adenine dinucleotide phosphate (NADPH) oxidase enzyme complex [7, 20]. Approximately 1–4% of the oxygen metabolized by mitochondria is converted to superoxide ions which in turn produce free radicals capable of damaging structural proteins and DNA [12]. Exogenous generated RONS are induced by external factors, such as chemical pollutants, environmental pollutants, such as cigarette smoke, some drugs, exposure to UV rays, alcohol, ionizing radiation, pesticides, ozone [12, 20]. X-rays and ultraviolet rays can break down water to form radicals (OH−). Metal ions such as Fe2+, Ca2+, Cu+, can also react with oxygen or hydrogen peroxide to produce OH radicals.
\nAmong the ROS, the most widely known are superoxide ions (O2\n•−), hydrogen peroxide (H2O2), and peroxyl radicals (OH•), while RNS is nitric oxide (NO) and peroxy nitrite (ONOO−) [18]. In general, free radicals are needed for the continuity of physiological processes, especially for electron transport, cell development, as well as helping leukocytes in destroying germs in the immune system. When free radical products are balanced with the antioxidant capacity, it directs cells into growth, signaling and survival.
\nIf these free radical products exceed the antioxidant capacity, cells will lead to oxidative stress, apoptosis and necrosis [18]. Mitochondria play an important role in the aging process because these organelles are the main source of free radical production as a side effect of energy formation. Mitochondrial dysfunction has long been considered a major contributor to aging and age-related diseases. In young mitochondria, this condition can be overcome by the presence of antioxidants in the body. However, the old mitochondria do not produce enough of the antioxidants needed to neutralize the free radicals that are formed, so there is an imbalance between the production of free radicals and antioxidants. Apart from that, mitochondria also play an important role in amino acid and lipid metabolism, calcium homeostasis, apoptosis regulation, cell cycle regulation, and thermogenesis [15, 21]. Renal fibrosis results from loss of ATP production due to mitochondrial dysfunction, which is associated with increased free radical formation and oxidative stress. Thus, oxidative stress, which involves various reactive oxygen and nitrogen species, has an important role in the pathophysiology of CKD [16].
\nDysfunction of the mitochondrial electron-transport system leads to increased production of ROS, which results in mtDNA damage followed by mutations that lead to impaired mitochondrial protein function and a further increase in RONS production [21]. Electron leakage causes the reduction of one oxygen electron to form superoxide anions which are the precursors of most ROS and mediators in oxidative chain reactions. The superoxide anion then undergoes dismutation which is catalyzed by superoxide dismutase to produce durable hydrogen peroxide and a permanent membrane. This molecule can be reduced entirely or partially to water or hydroxyl radicals. Under normal conditions, ROS is maintained at a physiological level by several systems of endogenous antioxidant enzymes, such as SOD, catalase, glutathione peroxidase and glutathione reductase [17].
\nOxidative stress causes tissue damage including the kidneys by a variety of different mechanisms, specifically damaging cell membranes, DNA damage, and protein modification. The term lipid peroxidation is a process of oxidative degradation of lipids. Lipid peroxidation is a process by which free radicals bind to lipid electrons in the cell membrane resulting in direct cell damage. The kidneys are highly susceptible to damage caused by ROS, possibly due to the large number of long-chain polyunsaturated fatty acids (PUFAs) in the lipid composition of the kidneys [18]. Allylic hydrogen in PUFA is very sensitive to free radical attack [14].
\nIn response to membrane lipid peroxidation, depending on the specific cellular metabolic state and repair capacity, cells may increase cell survival or cause cell death. In conditions where the level of lipid peroxidation is physiological or low (called the subtoxic state), cells stimulate their maintenance and survival through constitutive antioxidant defense systems or activation of signaling pathways that regulates antioxidant proteins resulting in adaptive stress responses. Conversely, under moderate or high levels of lipid peroxidation (toxic conditions), the rate of oxidative damage exceeds the repair capacity. This causes programmed cell death, apoptosis or necrosis; both processes eventually cause molecular cell damage which can facilitate the development of various pathological conditions, and also accelerate aging [22].
\nLipid peroxidation plays an important role physically because it decreases membrane fluidity, thereby facilitating the exchange of phospholipids between two monolayers, and increases the leakage of the bilayer membrane to substances that do not normally cross the membrane other than through certain channels [14]. Lipid peroxidation is followed by oxygen release, which is reduced into water via the mitochondrial respiratory chain. At the same time, lipids can be oxidized by efficient ROS initiators, particularly hydroxyl radicals and dihydroxyl radicals (HO2•), forming water and lipid radicals. This process leads to initiation of lipid peroxidation reactions, which are constantly occurring in the cell [23].
\nLipid peroxidation is an autocatalytic radical process which consists of three stages; the first is initiation, followed by propagation, and the last is the cessation of peroxidation which is the result of lipid radical interactions and/or the formation of non-radical species due to the action of peroxyl radicals. Lipid peroxidation is primarily initiated by hydroxyl radicals, which are generated through reactions catalyzed by transition metals, such as the Fenton reaction [24].
\nAt the initiation step of lipid peroxidation, prooxidants such as hydroxyl radicals (OH−) abstract allylic hydrogen to form carbon-centered lipid radicals (L∙). In the propagation phase, the lipid radical (L∙) rapidly reacts with oxygen to form peroxy lipid radical (LOO∙). Lipid peroxyl radicals are unstable molecules and can combine with other fatty acids (LH) nearby to form different lipid hydroperoxides (LOOH) and lipid radicals. Lipid peroxyl radicals can also react with themselves. The lipid hydroperoxides can then be broken down into alhoxyl lipid radicals (LO•) and hydroxyl radicals (OH•). The lipid radicals formed in the previous stage can react with oxygen to produce other lipid peroxyl radicals, and so on. So, this process is called the “lipid peroxidation chain reaction” (\nFigure 2\n) [22, 23, 24].
\nSchematic reaction of the lipid peroxidation chain. LH− lipid molecules, L•− lipid radicals, LOO•− lipid peroxyl radical, LOOH− lipid hydroperoxide, LO•− lipid alhoxyl radical, OH•− hydroxyl radical, HO2•− Perhydroxyl radical. The stopping phase occurs until the substrate runs out or the process is stopped by antioxidants.
Once formed, LOOH can undergo reductive degradation which reduces or increases the cytotoxic potential, depending on the underlying conditions. In addition, LOOH or other intermediate peroxidation products can trigger signal transduction pathways requiring greater cytoprotection shelter (eg. upregulation of detoxification enzymes) or planned termination (apoptotic death) [25]. Lipid hydroperoxides are fairly stable compounds, but their decomposition can be catalyzed by transition metals and metal complexes, giving rise to new radicals capable of stimulating further lipid peroxidation or the formation of oxidation end products with various toxicities, such as malondialdehyde (MDA), hydroxynonenal and hexanal. This reaction will continue until the substrate runs out, or the process is interrupted by antioxidants [24].
\nIn the stopping phase, an antioxidant such as vitamin E donates a hydrogen atom to the LOO∙ species and forms the associated vitamin E radical which reacts with other non-radical LOO∙ forming products. After lipid peroxidation has started, a multiplication chain reaction will continue until a stopping product is produced [22, 23].
\nLipid peroxidation produces a wide variety of oxidation products. The primary product of lipid peroxidation is lipid hydroperoxide (LOOH). Secondary products of lipid peroxidation include malondialdehyde (MDA), propanal, hexanal and 4-hydroxynonenal (4-HNE). Among these secondary products, MDA appears to be the most mutagenic lipid peroxidation product, whereas 4-HNE is the most toxic [22]. Peroxidation products are involved in many cellular processes including cell metabolism, signaling, and cell survival. Lipid molecules, particularly PUFAs and cholesterol, undergo variable oxidation rate initiated by RONS [26].
\nAging is associated with increased oxidative stress. Most of the changes in the kidneys are age dependent, such as excessive fibrosis, lack of regenerative ability in general, and increased apoptosis. At the molecular level there is an increase in mutations in both nuclear and mitochondrial DNA (mtDNA), increased lipofuscin, AGEs, oxidative stress and apoptosis. Proximal tubular cells contain a large number of mitochondria and are most dependent on oxidative phosphorylation and are most susceptible to apoptosis, oxidant-induced mutations. Recent studies have shown that the anti-aging gene, klotho, is an important factor in kidney aging and kidney damage due to oxidative stress [18].
\nLipid peroxidation can cause cellular damage in several ways. First, the integrity of the plasma membrane and subcellular organelles are impaired by peroxidation. Second, the interaction of ROS with PUFAs leads to the formation of additional radicals (hydroperoxide and hydroperoxide metabolites) which result in a “chain reaction” of ROS production. This process increases the production of ROS which can cause cell damage by interacting with cellular proteins and DNA. Finally, lipid peroxidation causes activation of phospholipase A2 (PLA2). Fatty acids and other PLA2 metabolites (such as lysophospholipids) are known to damage cell membranes. So PLA2-induced free fatty acid release can cause additive injury to the cell membrane [27].
\nIn the development of kidney damage, the process of lipid peroxidation plays an important role [28]. The kidneys are organs that are highly susceptible to damage caused by ROS. This is presumably due to the large number of long-chain polyunsaturated fatty acids (PUFAs) in the lipid composition of the kidneys [18]. There are substantial evidence to suggest that ROS is involved in the ischemic, toxic, and immunologically mediated pathogenesis of renal injury [29]. Experimentally it has been shown that ROS plays a role in the pathogenesis of kidney disease, but the cellular mechanisms that result in cell injury and death are still being studied [27].
\nIn general, human and animal studies suggest that lipid oxidation plays an important role in predicting the development of cardiovascular and renal disease and the response to therapy. In kidney disease, the use of biomarkers such as malondialdehyde (MDA), isoprostanes (IsoPs) or isolevuglandins (IsoLGs) has been reported. Malondialdehyde can interact with proteins and can potentially cause atherogenicity [7].
\nROS causes lipid peroxidation in cell membranes and organelles, thereby impairing structural integrity and capacity for cell transport and energy production, particularly in the proximal tubular segment. In addition, lipid peroxidation co-mediates decreased glomerular blood flow and glomerular filtration through release of vasoconstrictive bioactive lipids (prostaglandins, thromboxanes, and platelet activating factors) and, possibly, nitric oxide relaxation inactivation [29].
\nLi et al’s study of 2,169 adult patients stated that oxidative stress can be demonstrated by plasma MDA levels associated with the prevalence of mild acute renal insufficiency and/or CKD. Although MDA increases the load on the kidneys and/or causes oxidative stress cycle in the body, high levels of MDA in plasma may be associated with age-related decline in kidney function as well [6].
\nGomes et al. examined renal oxidative stress status in old Winstar Kyoto (WKY) mice. This research was conducted by measuring H2O2 levels in the kidneys. In this study, it was found that the renal and medullary cortical H2O2 production increased sharply in old WKY. This suggests that the dramatic increase in the rate of H2O2 production in the old WKY kidney is indicative of a significant increase in oxidative stress in this tissue. Moreover, increased renal ROS production and lipid-related oxidative damage may play a role in the pathogenesis of kidney disease. Overall, these data suggest that elderly WKY exhibits the first signs of renal oxidative damage [5].
\nLim et al’s study on old C57/BL6 mice showed that aging kidneys exhibited increased levels of reactive oxygen species and thiobarbiturate acid reactive substances, which are associated with oxidative lipid damage. In addition, other markers of oxidative stress and lipid peroxidation, such as isoprostane, AGE, and elevated heme oxygenase, were also found in old mice. This research also shows that Sirtuin 1 and Klotho also decrease with aging [30].
\nAntioxidant enzymes are able to remove reactive oxygen species and lipid peroxidation products. In aging, physiological functions change due to a decrease in endogenous antioxidants, such as SOD, CAT, and GSH-Px. In contrast MDA, a good indicator of lipid peroxidation showed an increase. Chen et al. in their study of old mice showed that the activity of SOD, CAT and GSH-Px in the liver and kidneys decreased compared to the group of young mice. In addition, MDA levels in the liver and kidneys of old rats were increased compared to the group of young mice [31].
\nBesides the endogenous antioxidants above, there is also a role for enzymes involved in oxidative stress response, aging and various metabolic regulation in the body, namely Sirtuin1. This enzyme is known as the master regulator. Starting with the discovery of the silent information regulator 2 (Sir2) gene in yeast in 1986. Where the long-lived yeast has an overexpression of this gene, while in the shorter-lived yeast it is found to have low expression. Since then Sir2 is believed to be a gene that plays a role in longer survival (longevity) [32, 33]. Homologous Sir2 gene in mammals, is sirtuin (SIRT).
\nSirtuin is a class III histone deacetylase protein group that has deacetylation activity against histones and non-histones that do not contain nicotinamide adenine dinucleotide (NAD) deacetylase and/or adenosine diphospate (ADP) ribosyltransferase. This enzyme works with the help of the coenzyme NAD to carry out its function [34]. Sirtuin 1 (SIRT1) is considered to be most homologous to yeast sir2. In the study of young mice, Sirtuin1 is expressed by various cells in organs such as kidneys, liver, lymph, skin, but mostly in the kidneys, especially the medulla [35, 36, 37, 38].
\nSirtuin 1 not only has deacetylation activity against histones but also on many transcription factors and cofactors, such as p53, FOXO, peroxisome proliferator activated receptor γ (PPAR-γ), co-activator-1α and NF-κB which play a role in crucial cellular activity including the response to stress, metabolism and longevity (cell senescence) [39, 40].
\nAs a function of redox regulator, sirtuin detects surrounding imbalance through NAD levels, sirtuin 1 will deacetylase the substrate so that it will activate antioxidant genes such as SOD2 (superoxide dismutase 2), GPX1 (glutathione peroxidase 1) which can anticipate free radicals. Under conditions of oxidative stress, sirtuin can produce O-acetyl-ADP ribose (OAADPR) which can turn into ADP ribose, both of which have a protective effect against oxidative stress [41].
\nThe role of sirtuin through the Forkhead box (FOXO) is to regulate apoptosis, lipid metabolism, cell proliferation, inflammation, autophagy and stress resistance. Where FOXO3 acetylation expresses apotosis-related genes, such as: Bim, TRAIL and FasL [33]. Nuclear factor-κB (NF-κB) is a widespread transcription factor affecting inflammation, apoptosis, adhesion and the cell cycle through regulation of target genes. Sirtuin can inhibit the activity of NF-κB so that it can play a role in glucose control, AGEs, cytokines, growth factors, dan toll-like receptors [42].
\nSirtuin 1 regulates lipid homeostasis by regulating the sterol regulatory element binding protein (SREBP), liver X receptor (LXR) and farnesoid x receptor (FXR). Sirtuin 1 directly deacetylates SREBP, inhibits SREBP target gene expression and reduces lipid and cholesterol levels [33, 34]. Tumor suppressor p53 is a transcription factor associated with oxidative stress responses. With the ability of SIRT 1 to deacetylate P53 it will reduce transcription activity.
\nIncreased oxidative stress in the elderly will decrease SIRT1 levels, resulting in increased inflammation, increased apoptosis, decreased autophagy, decreased levels of endothelial nitric oxide synthase (eNOS), increased AT1R expression, reduced ability in redox reactions and lipid metabolism. The reduced expression of SIRT1 due to aging will make the kidneys prone to progressive structural and functional disorders (\nFigure 3\n) [12, 33, 43].
\nThe role of SIRT1 in metabolic Homeostais [
Kume et al. (2010) found the association between mitochondrial damage of proximal tubular cells in aging kidneys with reduced expression of sirtuin. In proximal tubular cells of old mice, autophagy responses to kidney hypoxia were decreased and caused dysfunction and fibrosis. Increasing sirtuin expression by caloric restriction helps improvement of aging kidneys [44]. The similar result was stated by He et al. (2010), who found sirtuin expression protected kidneys in oxidized state and provided anti apoptotic and anti fibrotic effects to kidneys [45].
\nKidney function is determined by glomerular filtration rate. This filtration barrier consists of endothelial cells, glomerular basal membrane and podocytes. Podocyte is critical in maintaining glomerular filtration. Podocyte damage caused by oxidative stress leads to reduction of glomerular filtration rate. Sirtuin preserves homeostasis and protects podocytes from oxidation.
\nTransforming growth factor (TGF)-β is a key cytokine that regulates apoptosis, cell cycle, differentiation and accumulation of extracellular matrix. Association between TGF-β/Smad and kidney fibrosis occurrence has been proven in many studies. Sirtuin, which suppress expression of TGF-β, is decreased in aging kidneys. This results in increased expression of TGF-β thus also increases kidney fibrosis.
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