Sustainable planning of multipurpose hydropower reservoirs with environmental impacts in a simulation–optimization framework

Hydrology Research Vol 54 No 1, 31 doi: 10.2166/nh.2022.084 © 2023 The Authors Sustainable planning of multipurpose hydropower reservoirs with environmental impacts in a simulation–optimization framework Amir Hatamkhania, Ali Moridi a, * and Timothy O. Randhir b a Civil, Water and Environmental Engineering Faculty, Shahid Beheshti University, Tehran, Iran Department of Environmental Conservation, College of Natural Sciences, University of Massachusetts, Amherst, MA, USA *Corresponding author. E-mail: a_moridi@sbu.ac.ir b AM, 0000-0002-3974-2170; TOR, 0000-0002-1084-9716 ABSTRACT Hydropower projects involve enormous investments that require an efficient cost–benefit framework and optimization model for proper development. Dams and hydropower plants have many impacts on the environment. These environmental impacts are often not included in the economic calculations and planning of the projects, which leads to the loss of natural resources. The primary purpose of this research is to incorporate environmental impacts into optimization and decision-making. A comprehensive simulation–optimization model is developed to optimize hydropower decisions. The positive and negative values of environmental impacts are incorporated into an economic objective function under different scenarios, and optimal design was done for each scenario. The results show that considering environmental economics affects the multipurpose hydropower project’s NPV and decision outcomes. Considering environmental impacts compared to not considering them has reduced NPV of the project by 13.9%. The results emphasize the importance of including these impacts to achieve sustainable development and management. Key words: carbon sequestration, economic evaluation, environmental impacts, hydropower reservoir, simulation–optimization, sustainable development HIGHLIGHTS • • • Environmental impacts (EIs) are included in the multipurpose hydropower project optimization problem. Two different methods (hydrological method and habitat condition of indicator species) are employed for environmental flow assessment. Considering EI is effective on decision variables and optimal energy production and agriculture development. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/). Downloaded from http://iwaponline.com/hr/article-pdf/54/1/31/1163901/nh0540031.pdf by guest Hydrology Research Vol 54 No 1, 32 GRAPHICAL ABSTRACT 1. INTRODUCTION Economic evaluation is one of the most important decision criteria for the selectivity and prioritization of projects. Since hydropower projects require huge investment, economic feasibility and optimal planning of dams and power plants are critical. Therefore, developing a comprehensive hydro-economic model is essential for technical and economic analysis and optimal sizing of hydropower projects. Mathematical programming models, including simulation and optimization models, are effective tools for achieving optimal water systems’ design and operation and are extensively used in analyzing hydropower projects. Bozorg Haddad et al. (2011) investigated a strategy for the optimal design, control, and operation of small hydropower plants using the honey bee mating optimization algorithm. The objective function included the annual difference between energy generation benefits, initial investment, and operation costs. The algorithm determined the annual benefit and operating cost of generated energy. Hosnar & Kovac-kraj (2014) developed a model to optimize the construction of a hydropower plant to identify the maximum economic and profit from selling energy produced from the hydro system. Rahi et al. (2012) used particle swarm optimization (PSO) to maximize the benefit to cost ratio of a hydropower plant. The analysis of benefit–cost ratio was based upon direct costs, and revenue generated from indirect benefits have not been taken into consideration. Yazdi & Moridi (2018) proposed a multi-objective optimization model for the estimation of design parameters in cascade hydropower multipurpose reservoir systems. The main objectives considered in the optimization model included minimizing the squared deviation of release from demands and maximizing the total amount of generated energy. Paseka et al. (2018) presented an approach to the optimal design of a multipurpose reservoir that would provide water for downstream environmental demand, drinking and industrial water supply, agricultural water supply, and hydropower Downloaded from http://iwaponline.com/hr/article-pdf/54/1/31/1163901/nh0540031.pdf by guest Hydrology Research Vol 54 No 1, 33 production in the current conditions of climate uncertainty. The objective functions include minimizing the cost of dam construction and maximizing benefits from water utilization for hydropower and water sales. Hatamkhani & Moridi (2019) used a simulation–optimization approach to solve optimal planning at the basin scale. The objective functions of the problem were (1) maximizing the area under cultivation (AUC) of agricultural sectors and (2) maximizing the energy generated by the hydropower plant. Azizipour et al. (2022) employed the capabilities of the cellular automata-based method to maximize the firm energy generation of multi-reservoir hydropower systems. Installed capacities were selected as decision variables in the design phase and were determined in an iterative procedure. Ren et al. (2021) investigated the optimal management of water resources and its effect on optimal hydropower generation. They used a new version of Developed Wildebeest Herd Optimization (DWHO) to forecast hydropower generation. The optimal planning of hydropower projects in past studies is based on technical aspects like energy production or supply deficits. Even economic objective functions are in a simplified form like the price of sale or marginal cost of energy, while environmental impacts of hydropower are neglected. Hydropower is one of the most convenient renewable energy sources and is growing quickly all over the world. Its fast deployment also has different environmental impacts that cannot be neglected. Therefore, it is essential to consider the sustainability of hydropower projects so that this energy resource grows effectively. These environmental impacts are also known as externalities. An externality is an inefficiency that appears when part of the costs or benefits of an activity is external to the decision-maker’s calculations. In other words, part of the benefits or the costs affects those who play no role in the decision (Zhang et al. 2015). Most of the previous studies focus on the estimation of GHG emissions, so it is necessary to emphasize other environmental impacts (Nautiyal & Goel 2020). Hondo (2005), Zhang et al. (2007), Goel et al. (2010), and Ferreira et al. (2016) are among the studies that investigated GHG emissions. Gunawardena (2010) investigated the environmental externalities of a run of river project in Sri Lanka and estimated inequity in the distribution of the impacts among various social groups. The impacts were assessed using different valuation approaches. Finer & Jenkins (2012) examined the ecological impacts, in terms of forest loss and river connectivity, of the planned proliferation of hydropower dams among Andean tributaries of the Amazon River. The ecological impact analysis showed that 47% of the potential new dams have a high impact, and just 19% have a low impact on the environment. Xia et al. (2020) presented a framework for hydropower project externalities based on the Life Cycle Assessment and the economic valuation of hydropower externalities. They applied this method in the assessment of the Three Gorges Project and the Xiluodu Project. Briones-Hidrovo et al. (2019) argued that the assessment of hydropower impact on local ecosystems should be considered among conventional hydropower costs. They employed, a cost–benefit analysis, based on an ecosystem services valuation approach. The economic valuation of the environment makes it possible to compare environment protection and socio-economic development to achieve sustainable use of scarce resources. This paper emphasizes the importance of assessing the impact of project development on the environment in the planning phase. Most design processes include impacts as non-technical indicators through environmental impact assessment studies (Stevovic et al. 2015). These studies are primarily descriptive texts that have been done after completing all the technical design optimizations and have not always been included in the optimization calculations. The main purpose of this article is to include environmental impacts in the decision-making process and to see how considering the economic value of these impacts can change the optimal planning of water resources projects. Using an integrated approach and considering the technical, economic, and environmental aspects, a hydro-economic model is presented to solve the problem of optimal development of hydropower projects and integrate optimization problem and environmental impacts assessment of these projects. It should be noted that the environmental flow requirement which is determined according to the habitat conditions of the indicator species and the Tennant method is considered as a constraint that must be satisfied in the optimization model without an economic perspective. The alternative thermal plant method is used to estimate the benefits of the hydropower project based on the concept of opportunity cost. Positive and negative environmental impacts are considered, along with the traditional costs and benefits of the project to form the economic objective function of the problem. The decision variables include the normal water level (NWL) of the reservoir (reservoir capacity), the installed capacity (IC) of the hydropower plant, and the AUC of the agricultural development plan downstream of the reservoir. Water evaluation and planning (WEAP) model is used to simulate water resources allocation, and the particle swarm optimization (PSO) is employed for optimization. The developed model is used for optimal planning and design the Abriz reservoir in Iran, and the results are presented in different scenarios. Downloaded from http://iwaponline.com/hr/article-pdf/54/1/31/1163901/nh0540031.pdf by guest Hydrology Research Vol 54 No 1, 34 2. MATERIALS AND METHODS 2.1. Study area The Maroon River Basin is part of the Persian Gulf Basin with an area of 25,349 square kilometers. It is limited to the Karun River Basin from the west and north and the Zohreh River Basin from the east. Maroon Basin is located in Kohgiluyeh, Boyer-Ahmad, and Khuzestan provinces. Kohgiluyeh and Boyer-Ahmad provinces have a smaller basin area than Khuzestan province with a share of about 20%. Figure 1 shows the map of the Maroon Basin in Iran and the location of dams and rivers. The agriculture development plan covered by this study is located in Dehdasht and Charam regions in Kohgiluyeh and Boyer-Ahmad provinces. Dehdasht and Charam are the only integrated plains of Kohgiluyeh and Boyer-Ahmad provinces and comprise more than 30%. With its climate, soil, potential lands, and very favorable factors of agriculture, as well as young people ready to work, this region is a suitable platform for the development and prosperity of agriculture if the required water is provided. So, there is a need for a project to cover the significant demands of these two large cities of Kohgiluyeh and Boyer-Ahmad provinces. Figure 2 shows a schematic of resources and demands in the Maroon River Basin. The Abriz Dam site is located on the Maroon River. The most important goals of this project are to irrigate agricultural lands in the region, supply the drinking and industry demands, and generate energy during peak times. The long-term average annual inflow to the Abriz Dam is 659.2 million cubic meters (equivalent to 21 cubic meters per second). The average monthly distribution of the long-term inflow of the Maroon River at the Abriz Dam site and monthly evaporation is shown in Figure 3. Figure 4 shows the geometric relations of the reservoir. Figure 1 | Map of the Maroon Basin in Iran. Downloaded from http://iwaponline.com/hr/article-pdf/54/1/31/1163901/nh0540031.pdf by guest Hydrology Research Vol 54 No 1, 35 Figure 2 | Schematic of resources and demands in the Maroon River Basin. Figure 3 | Monthly inflow and evaporation at the Abriz reservoir. 2.2. Simulation–optimization approach (WEAP–PSO model) In the optimal hydropower reservoir design and planning problem, the reservoir’s NWL, the AUC, and the IC are the main decision variables that can take any values in their predefined continuous interval (Hatamkhani & Moridi 2019). Hence, many combinations of these variables will result in a specific value of performance and economic efficiency. To choose the best combination, a systematic search or optimization algorithm can be used. So, in this research, a simulation–optimization approach is employed. The flow diagram of the simulation–optimization model is shown in Figure 5. For the simulation of water resources allocation, WEAP model is used. WEAP is distinguished by its integrated approach to simulating water Downloaded from http://iwaponline.com/hr/article-pdf/54/1/31/1163901/nh0540031.pdf by guest Hydrology Research Vol 54 No 1, 36 Figure 4 | Elevation–volume–area curves for the Abriz reservoir. Figure 5 | Flow diagram of the proposed simulation–optimization approach. Downloaded from http://iwaponline.com/hr/article-pdf/54/1/31/1163901/nh0540031.pdf by guest Hydrology Research Vol 54 No 1, 37 resources systems and policy orientation (Sieber & Purkey 2011). It provides a flexible, comprehensive, and user-friendly framework for policy analysis and is employed in many studies in recent years (Hatamkhani & Moridi 2021; Fanta et al. 2022). Simulation of water resources allocation in the Maroon River Basin was performed using the WEAP model for 59 years with monthly time steps. Despite many capabilities, hydropower energy generation module in WEAP has some defects. To improve these limitations, using the scripting environment of WEAP, a hydropower simulation module based on the traditional sequential streamflow routing (SSR) method is employed to enable hydropower simulation (Hatamkhani & Alizadeh 2018). Then, the well-known optimization algorithm, PSO, is linked to the simulation model to solve the problem. In the simulation–optimization process, first, the decision variables are generated by the PSO algorithm (coded in MATLAB) and written in Excel. According to the amount of decision variables, project costs including traditional and environmental costs are calculated. Then these variables are read by WEAP and the water resources simulation model is executed. Water is allocated from the reservoir for downstream needs and the amount of allocation to each sector is calculated. According to the water allocated to the hydropower and agricultural sectors, the economic benefits of the project are calculated. The environmental flow requirement is considered a hard constraint that must be satisfied in the optimization model and is not included in the economic calculations. But supplying this requirement will affect the water available to other sectors and, consequently, the benefits derived from them. Finally, according to the costs and benefits of the project, the objective function (NPV of the project) is calculated. If the PSO stop criteria are met, the algorithm stops. Otherwise, the particle velocity and location are updated and new decision variables are generated. This process is repeated until the optimal solution is obtained. The values of the PSO parameters in this study are shown in Table 1. The appropriate parameter values have been obtained based on previous studies by the authors (Hatamkhani et al. 2022a, 2022b). The limits of decision variables are shown in Table 2. 2.3. Environmental flows assessment Increasing water withdrawals from rivers are leading to serious degradation in river ecosystems. Water is allocated for environmental demands so the river can achieve its natural functions. Environmental flows try to reach a balance between the use of river water for economic development, human activities, and delivering proper ecosystem services ( Jain 2015). In this research, environmental flow is estimated using hydrological methods and habitat conditions of indicator species. The Tennant method developed by Tennant (1976) is one of the most used hydrological methods for determining environmental flow worldwide and has been applied by at least 25 countries (Shaeri Karimi et al. 2012). This method is based on Table 1 | PSO parameters Parameter Value x 1 Wmax 0.9 Wmin 0.4 C1 1.8 C2 1.8 Swarm size 9 Particle size 3 Table 2 | Upper and lower bounds of decision variables Decision variable Min Max NWL (masl) 980 1,026 IC (MW) 10 80 AUC (Hectare) 15,000 30,000 Downloaded from http://iwaponline.com/hr/article-pdf/54/1/31/1163901/nh0540031.pdf by guest Hydrology Research Vol 54 No 1, 38 empirical relationships among the defined percentage of the mean annual runoff and the prescribed ecological condition of the river and aquatic habitat suitability. The Tennant method uses a percentage of the mean annual runoff for two different 6-month periods to determine conditions of flow related to wildlife, fishery, environmental resources, and recreational. According to this method, 20% of the river flow regime in October to March and 40% of April to September is a good amount for the minimum environmental flow. One of the methods for estimating the environmental flow is the use of limnological studies. In this method, based on limnological sampling and ecological studies of the region, the required depth for aquatic life and water-dependent species will be determined, and then based on the required depths, the environmental flow will be estimated. Therefore, this method is divided into two parts: the first part includes the selection of indicator species and their required depths, and the second part is the determination of discharge corresponding to the depth. Indicator species are species that testify to the well-being (health) of the entire ecosystem (Legendre 2013). Therefore, by determining and supplying their major ecological requirements, we can expect that the ecological requirements of other species that coexist with them, which usually have more tolerance for human activities and change, can also be met (Wang et al. 2016; Fu et al. 2021). According to environmental and field studies of the Abriz Dam (Iran Water Resources Management Company 2020), aquatic ecosystem animals including Lutra lutra and Arabibarbus grypus fish are indicator species and the minimum water depth for the habitats of this species is estimated 50 cm for March to June and 30 cm for other months. Table 3 shows the environmental flow calculated by the Tennant and habitat condition of indicator species. The maximum amount of these two methods in different months is considered the required environmental flow downstream of the Abriz Dam. 3. COST–BENEFIT ANALYSIS The various methods of economic evaluation are based on the correct calculation of costs and benefits. Trying to estimate the costs and benefits of the projects accurately can have a tremendous impact on the quality of the economic evaluation and the accuracy of the obtained economic indicators. In the cost–benefit analytical framework, all the costs and benefits of a project are considered first. However, summarizing and comparing the costs and benefits of different years requires a series of computational operations to synchronize. So, to calculate the net present value (NPV) of a project, all costs and benefits must be based on a common year using the principles and techniques of economic engineering. The base year in this study is the beginning of the project operation. The study period is considered 50 years. Figure 6 shows the components of the economic analysis of hydropower projects which will be discussed further. Equation (1) is the objective function of the problem which calculates the NPV of the project. PVC and PVB are the project’s present value of costs and benefits. NPV ¼ PVB  PVC (1) 3.1. Project costs The costs of multi-purpose hydropower projects has different components that fall into two main categories: initial investment and operation & maintenance costs. The project’s investment costs include the costs of dams and related facilities, transmission systems, irrigation and drainage networks, and hydropower plant. All these costs are a function of the problem decision variables, i.e. NWL (construction cost of dam and transmission system), AUC (irrigation and drainage networks), and IC (hydropower plant). The following are the cost functions of each: CCH ¼ f(NWL, AUC, IC) ¼ DCC þ TSC þ IDC þ HPC (2) Table 3 | Environmental flow downstream of the Abriz Dam (CMS) Month Oct Nov Dec Jan Feb Mar Apr May Jun July Aug Sep Annual Tennant method (CMS) 2.21 2.43 3.44 3.78 4.01 5.27 4.66 3.46 3.59 3.53 2.68 2.24 3.43 Habitat condition method (CMS) 2.1 2.1 2.1 2.1 2.1 5.7 5.7 5.7 5.7 2.1 2.1 2.1 3.3 Environmental flow (CMS) 2.21 2.43 3.44 3.78 4.01 5.7 5.7 5.7 5.7 3.53 2.68 2.24 3.93 Downloaded from http://iwaponline.com/hr/article-pdf/54/1/31/1163901/nh0540031.pdf by guest Hydrology Research Vol 54 No 1, 39 Figure 6 | Economic evaluation of the multipurpose hydropower reservoir. where DCC is the construction cost of dam, TSC is the transmission system, IDC is the irrigation and drainage networks cost, HPC is the hydropower plant cost, and CCH is the total initial investment cost of the hydropower dam and power plant. Operation and maintenance costs (COMH ) are calculated on an annual basis from the beginning of the operation period to the end of the operation period. COMH is calculated using the following equation: COMH ¼ 0:06 DCC þ 0:01(TSC þ IDC þ HPC) (3) So, the project’s present value of costs (PVC) can be calculated as below:      F P , i, p þ COMH  , i, n PVC ¼ S CCH,p  P A (4) where P is the present value, F is the future value, A is the annual value, i is the interest rate, COMH is operation and maintenance costs, PVC is the present value of project cost, n is the number of time periods, and CCH,p is the capital cost in p years before operation. 3.2. Project benefits 3.2.1. Hydropower benefits using the alternative thermal plant method Alternative thermal plant method is developed based on the concept of opportunity cost. This method is developed based on the concept of opportunity cost. Opportunity cost is the value of what you lose when choosing between two or more options. If the hydropower plant was not built, a thermal power plant would be constructed instead to supply equivalent energy production. So, the benefits of a hydropower plant are equal to the costs of its alternative thermal power plant. In this method, we have to identify all of the costs associated with the thermal plant and divide them into the fixed cost (capacity cost) and variable cost (energy cost) categories (US Army Corps 1985; Raeisi et al. 2016; Hatamkhani & Alizadeh 2018). The present value of costs can be calculated as follows:      F P PVCTPP ¼ S CCT,p  , i, p þ {COMF þ CFu þ COMV }  , i, m P A Downloaded from http://iwaponline.com/hr/article-pdf/54/1/31/1163901/nh0540031.pdf by guest (5) Hydrology Research Vol 54 No 1, 40 PVCTPP is the present value of thermal plant costs, COMF is the fixed operation and maintenance costs, CFu is the fuel cost, COMV is the variable operation and maintenance costs, p is the years of construction of thermal plant, and m is the useful life of thermal plant. Some of the above variables can be defined as the following equations: CCT,p ¼ S(ap  ICTPP  PC ) p ¼ P, (P  1), . . . , 1, 0 (6) FE n hours  PF  TMA (7) ICTPP ¼ COMF ¼ POMF  ICTPP (8) CFu ¼ CFu, FE þ CFu, SE (9) CFu, FE ¼ PFu,FE  FE HVFE  RFE (10) CFu, SE ¼ PFu,SE  SE HVSE  RSE (11) COMV ¼ COMV,FE þ COMV,SE (12) COMV ¼ POMV,FE  FE þ POMV,SE  SE (13) In these relations, p is the duration of the construction of the thermal plant, ap is the percentage of cost spent in year p before the start of the operation, ICTPP is the installed capacity of the alternative thermal plant in megawatt, PC is the cost of construction per unit of capacity, FE is firm energy produced by hydropower plant, SE is secondary energy produced by hydropower plant, n hours is the number of hours per year, PF is the thermal plant factor, POMF is the fixed operation and maintenance cost per unit of capacity, POMV is the variable operation and maintenance cost per unit of energy generation, TMA is thermal plant availability, CFu is fuel costs, PFu is the cost per unit of thermal plant fuel, HV is the heating value of fuel, and R is the thermal plant efficiency. Hence, the benefits of a hydropower plant are equal to the costs of its alternative thermal power plant, PVB of a hydropower plant is: PVBHPP ¼ PVCTPP (14) In Iran, the cost of hydropower plants is provided by the specialized company for the management of production, transmission and distribution of electric power (TAVANIR 2005). That is illustrated in Table 4. Given that Abriz hydropower plant is a peak generation plant, gas turbine is selected as an alternative. Also, it is assumed that the combined cycle plant with greater efficiency and lower energy costs is used for the replacement of secondary energy. The heating value of natural gas 8,600 kcal per cubic meter and diesel 9,232 kcal per liter are considered. For 3 months of the year due to the limited supply of natural gas for thermal power plants, diesel as an alternative fuel is used. The price of natural gas is 31,247 Table 4 | Technical and economic information of thermal power plants Energy value (without fuel cost) Capacity value Variable o&m Construction cost CC Thermal plant Fix o&m OMF OMV Construction time Life availability (percent) Efficiency Euros/ Euros/ Rials/ Type of power plant (year) P (year) TMA (percent) R kW Rials/kW Rials/kW kWh kWh Combustion (gas) turbine 2 12 83 33.4 290.00 4,222,926 49,345 0.06 2.60 Steam plant 5 30 72 41.2 677.00 7,905,080 180,594 0.02 5.56 Combined cycle 5 30 81 50 519.00 7,208,129 83,578 0.03 2.99 Downloaded from http://iwaponline.com/hr/article-pdf/54/1/31/1163901/nh0540031.pdf by guest Hydrology Research Vol 54 No 1, 41 Rials per cubic meter and the price of diesel is 62,494 Rials per liter. The exchange rate used to estimate the benefits and costs of the project is 156,234 Rials per dollar and 171,562 Rials per euro. 3.2.2. Agriculture benefits In studies of water resources development projects aimed at agriculture, the benefit of the project is estimated by the difference between the benefit in the condition with and without the project. The total gross agricultural land area in the future without the project is 20,000 hectares. Based on agricultural economics studies (Iran Water Resources Management Company 2020), the net value of production per hectare of agriculture without the project is estimated at 44 million rials. The net production value per hectare of agricultural lands in the future conditions with the project is estimated at 237 million rials. Therefore, the net benefit from agriculture is calculated from the difference in income in the conditions with and without the project. Agricultural benefits are calculated according to Equation 15.   P PVBAgr ¼ ((AUCFPV)  880) , i, n (15) A where PVBAgr is the present value of agriculture benefit, AUC is the area under cultivation, and FPV is the value per hectare of agricultural lands. It should be noted that the monthly allowable deficit in supplying the agriculture demand (WDAgr ) is equal to 15%. Therefore, the area under agriculture should be estimated so that the amount of deficit is not more than 15%. For this purpose, in the situation where agricultural demands are not well supplied, a penalty function was considered to determine the AUC according to this constraint: if WDAgr . 15 then AUC ¼ AUC  (WDAgr  15)PE AUC (16) where PE is the value of the penalty coefficient. Finally, the project’s total benefits are estimated based on the sum of agriculture and hydropower benefit. PVB ¼ PVBHPP þ PVBAgr (17) 3.3. Economic evaluation of environmental impacts 3.3.1. Positive impacts Electricity generation using thermal plants causes substantial human health and environmental damages, which requires a cost to eliminate this pollution or compensate for the damages caused by it. Due to the fact that the hydropower plants do not consume fossil fuel to produce energy, they do not cause GHGs and pollution emission and it can be considered an advantage for hydropower plants. Asian Development Bank (1996) introduced the ‘adjusted conversion’ method to adjust environmental costs commensurate with each of the pollutants from electricity generation. This method is proposed to calculate the appropriate adjustment index using GDPppp per capita (based on purchasing power parity). After multiplying this index by environmental costs per ton of pollutants of the country of origin, environmental costs are calculated per ton of pollutants in the country under the study. In this research, the method proposed by the Asian Development Bank is used to calculate the environmental costs of thermal power plants. Thus, the environmental costs of each kilowatt-hour of electrical energy of thermal power plants (Rials/kilowatt-hour) were obtained (World Bank Group 2002), and the results of which are summarized in Table 5. This cost must be included in economic analysis and added to other thermal plant costs. Damage costs of emitting pollutants and greenhouse gases (EPG) can be calculated as below: EPG ¼ PFE  FE þ PSE  SE (18) where PFE is the average external costs per unit of energy produced in a thermal power plant for replacing firm energy and PSE is the average external costs per unit of energy produced in a thermal power plant for replacing secondary energy. Downloaded from http://iwaponline.com/hr/article-pdf/54/1/31/1163901/nh0540031.pdf by guest Hydrology Research Vol 54 No 1, 42 Table 5 | Environmental costs of thermal power plants energy generation (Rials/kilowatt-hour) Plant owner Type of power plant Environmental costs (Rials/kWh) Ministry of Energy Combustion turbine Steam plant Combined cycle 1,735.8 11.62.8 2,322.8 Private Sector Combustion turbine Steam plant Combined cycle 1,553.4 1,218.5 4,816.8 3.3.2. Negative impacts The environmental damage costs caused by the negative impacts of the project must be added to the rest of the costs to calculate the total cost. Figure 5 summarizes the costs of the project. The environmental costs calculation method is presented in the following paragraphs. 3.3.2.1. Damage to the forest habitat. One of the activities affecting the natural environment in the project’s construction phase is the cleaning of the vegetation in the reservoir area. The habitat value of lost trees and shrubs such as oak and astragalus is considered as one of the project costs. A typographic vegetation map was prepared in the dam and power plant area based on the field visits carried out in the project area. According to the rate of uprooting and eradication penalty for each tree type (Table 1), the total damage caused by cutting tree and shrub cover in the reservoir Abriz Dam at different normal water levels is presented in Table 6. Therefore, the habitat damage cost caused by cutting down trees in the reservoir area (HVD) is calculated using the following equation: HVD ¼ 9:5NWL–9,193 (19) 3.3.2.2. Carbon sequestration. Carbon sequestration is another important vegetation service. Absorbing carbon dioxide from the air and storing it in plants or soil leads to air purification and reducing greenhouse gases in the atmosphere. The amount of carbon sequestration in each species varies according to the type of species. Vegetative shape, stem diameter, canopy size, leaf area, root system, environmental edaphic factors, etc., are among the factors affecting the amount of carbon sequestration by a species (Motamedi et al. 2020). Vegetation clearance in the reservoir area leads to the extinction of significant species such as oak, pistachio, almond, and astragalus. For this reason, the amount of carbon stored at different levels in the dam area was calculated and is presented in Table 7. Therefore, the damage function caused by the loss of carbon sequestration (CSD) is as follows. CSD ¼ 1:4375NWL  1403:9 (20) Table 6 | Damage to the forest habitat at different NWLs Habitat value of trees and shrubs within the reservoir area (million rials) NWL Oak Pistachio Almond Astragalus Myrtus Total damage (billion rials) 1,026 271,058 165,525 64,050 57,000 6,000 563 1,018 231,053 141,000 53,925 37,350 6,000 469 1,010 196,980 120,300 45,300 24,675 6,000 393 1,002 168,525 103,050 38,175 19,125 6,000 335 Downloaded from http://iwaponline.com/hr/article-pdf/54/1/31/1163901/nh0540031.pdf by guest Hydrology Research Vol 54 No 1, 43 Table 7 | Damage due to lack of carbon sequestration at different NWLs Carbon sequestration (ton) by species in the reservoir area Total carbon sequestration Total annual damage due to lack of carbon NWL Oak Pistacia Almond Astragalus for all species (ton) sequestration (billion Rials) 1,026 5,287 6,180 3,780 29 1,527.6 67 1,018 5,423 5,388 3,295 25 1,413.1 62 1,010 4,957 4,690 2,869 22 1,253.8 55 1,002 456 4,120 2,520 19 711.5 31 4. RESULTS AND DISCUSSION To solve the optimal design of the hydropower project, the developed simulation–optimization (PSO–WEAP) model is employed. In the simulation–optimization model, four scenarios were examined based on how the environmental costs and benefits of the project are considered in the objective function. These four scenarios are presented in Table 8. Figure 7 shows the convergence process of particles to the optimal solution in the base scenario (scenario 1: without considering any environmental impacts) and the most complete scenario (scenario 4: considering all environmental impacts). As can be seen, the objective function starts from very large negative values at the beginning of the optimization process, meaning that the project costs are greater than the benefits. Eventually, the value of the objective function is converged to the best value. Figure 8 shows the convergence process of the decision variables in scenarios 1 and 4. As it is clear from Figures 7 and 8, both the objective function and the decision variables in scenario 4 have converged to the optimal value in higher iterations. This is because the objective function is more complex in scenario 4, and the environmental terms are also added to the objective function. Table 8 | Description of scenarios Scenarios Description Scenario 1 Costs and benefits of the project without considering the environmental impacts Scenario 2 Costs and benefits of the project with considering the positive environmental impacts Scenario 3 Costs and benefits of the project with considering the negative environmental impacts Scenario 4 Costs and benefits of the project with considering all environmental impacts Figure 7 | Convergence of objective function. Downloaded from http://iwaponline.com/hr/article-pdf/54/1/31/1163901/nh0540031.pdf by guest Hydrology Research Vol 54 No 1, 44 Figure 8 | Convergence process of the decision variables: (a) storage capacity, (b) installed capacity, (c) area under cultivation. Table 9 summarizes the results of the WEAP-PSO model in four scenarios. The first thing that is clear from the results is the maximum value of the NWL (reservoir capacity) in scenarios 1 and 2. In these two scenarios, the negative environmental impacts of the project are not considered. By increasing the NWL, the grow rate of benefits seems to be more than the costs. Due to technical limitations, the NWL value is set to maximum, and in the absence of these restrictions, the value may be higher. Considering the positive effects of the hydropower reservoir (scenario 2), the amount of NPV of the project has increased to 2,035 billion rials (about 9%) compared with scenario 1. Considering the positive impacts Downloaded from http://iwaponline.com/hr/article-pdf/54/1/31/1163901/nh0540031.pdf by guest Hydrology Research Vol 54 No 1, 45 Table 9 | Simulation–optimization (WEAP–PSO) results Scenarios S1 S2 S3 S4 NWL (masl) 1,026 1,026 1,024.17 1,024.96 IC (MW) 30.84 33.45 27.44 29.83 AUC (Hectare) 19,399.38 19,259.36 19,139.60 19,190.06 NPV (Billion Rials) 22,703.55 24,737.95 17,864.34 19,542.12 Energy generation (GWh) 149.55 154.8 142.6 146.45 Firm energy (GWh) 56.95 65.2 52.8 55.07 and the effect it has on the objective function also affected the amount of decision variables. Since the project’s positive impacts are applied to the generated energy, it has increased the IC and, as a result, the energy produced by the power plant. In scenario 2, the total generated energy of the power plant has increased by 5.25 GWh (3.5%) compared with scenario 1. However, the increase in IC has had a significant impact on firm energy generation, increasing by 14.5% (from 56.95 to 65.2 GWh). On the other hand, because more water is allocated for energy production, the amount of cultivated area has decreased. In reviewing scenarios 3 and 4 (negative impacts are considered), the first thing that is clear is the significant decrease in NPV compared with scenarios 1 and 2. Therefore, the type of environmental impact consideration can make a 34% difference in the NPV of the project (scenario 2 compared with scenario 3). In scenario 4, where all environmental impacts are considered in economic valuation, compared with scenario 1, the NPV of the project is reduced by 3,161.43 billion rials. This indicates that the negative impacts have a more significant effect on the NPV of the project than the positive. With the addition of negative impacts in scenarios 3 and 4, the NWL decreases and is no longer at its maximum value. This can be justified given that the changes in negative impacts were a function of the NWL of the reservoir. With the decrease of NWL, the amount of area under agricultural cultivation has decreased. The reason for this is the lack of adequate supply of agricultural needs due to the reduction of reservoir storage capacity. Given that the maximum deficit of agricultural demand is 15%, the AUC is estimated to meet this constraint. On the other hand, negative impacts influenced the decision variable of IC and the amount of energy production. For comparison, the energy produced in scenario 2 was 154.8, which in scenario 3 reached 142.6. This means that the energy generated by the power plant is reduced by 9.2%. This is due to both the reduction of the NWL (reduction of the net head and flow through the power plant) and the reduction of the IC of Figure 9 | Agriculture demand requirement and supply delivered in different months. Downloaded from http://iwaponline.com/hr/article-pdf/54/1/31/1163901/nh0540031.pdf by guest Hydrology Research Vol 54 No 1, 46 Table 10 | Agriculture demand and environmental flow coverage Demand site coverage Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Agriculture (Percent) 85.88 89.38 91.00 100.00 96.61 96.04 98.18 96.98 93.99 94.14 90.60 88.25 Environmental flow (Percent) 100.00 99.78 98.27 99.21 99.54 99.98 100.00 100.00 99.52 100.00 100.00 100.00 the power plant, which is calculated 6 MW smaller. In scenario 4, with the addition of the positive environmental impacts of the project, the values of the decision variables have improved compared with scenario 3. Increasing the NWL and IC has led to an improvement in the AUC and energy generation. In the optimal solution, the agricultural and environmental demands downstream of the Abriz reservoir is also well supplied. Figure 9 shows the agricultural requirement (which is a function of the AUC) and the average supply delivered in different months of the simulation period. According to Figure 9, the highest deficit in meeting the agricultural demand happened in September and October. However, it should be noted that the amount of AUC in the optimization model is determined in such a way that the unmet requirement is less than 15% (according to Equation (16)). To clarify, the coverage of the demand (percent of requirement met) is shown in Table 3. As can be seen, the lowest coverage is for October with 85.8%. Also, the optimization model performs well in supplying the environmental flow, which is considered a hard constraint, and according to Table 10, the lowest amount of coverage occurred in December which is only 1.7%. 5. CONCLUSION One of the most critical environmental problems today is the lack of environmental considerations in macro-policies and programs. The main reason for this is the lack of an economic valuation system for environmental resources and its non-inclusion in the economic evaluation of hydropower projects. Investment at the macroeconomic planning level usually leads to sectors with a major GDP share. The result is a weakening of environmental resources. This study aimed to present a constructive approach to solving the problem discussed and ensuring sustainability. Therefore, we consider some of the project’s environmental impacts in the cost–benefit framework and solve the problem of optimal design and planning of a hydropower reservoir. For this purpose, an optimization–simulation approach was presented using the link between a water allocation simulation model (WEAP) and an optimization algorithm (PSO). The developed model was applied to the Abriz hydropower reservoir in the Maroon Basin in Iran. The decision variables of the problem include the NWL of the reservoir, IC of the hydropower plant, and the cultivated area of agricultural development downstream of the reservoir. According to the way of considering costs and benefits of the project in calculating the economic objective function, four scenarios were defined and the optimization results for these four scenarios were presented. According to the results, it is clear that considering the environmental economics of the project has a significant effect on the results of optimization, including the objective function and decision variables. According to the results, the economic effect of the negative environmental impacts of the project was more significant than its positive impacts. Considering environmental impacts compared with not considering them has reduced the NPV of the project by 13.9%. Also, the AUC and energy production has decreased by 209.32 hectares and 3.1 GWh, respectively. The results show that the inclusion of environmental considerations in the economic analysis of the hydropower reservoir affects the development and planning of the project, and these impacts must be considered along with other economic and social planning. Considering the environmental impacts prevents the overestimation of design variables and gives us a more realistic picture of the project’s benefits and costs. Using the integrated simulation–optimization approach, optimal planning can be determined so that negative impacts are minimized and positive impacts are maximized, which lead to more movement toward sustainable planning at basin scale. In this article, the optimal planning of a multipurpose hydropower dam is evaluated, it should be noted that in future research other services of the reservoir such as flood management or recreational activities can be considered in the cost–benefit analysis. Also, in addition to environmental impacts considered in this research, other impacts such as preventing soil erosion in the forest area can be included in the economic optimization of the projects. Downloaded from http://iwaponline.com/hr/article-pdf/54/1/31/1163901/nh0540031.pdf by guest Hydrology Research Vol 54 No 1, 47 FUNDING The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. CONSENT TO PARTICIPATE All authors consent to participate. CONSENT TO PUBLISH All authors consent to publish. DATA AVAILABILITY STATEMENT Data cannot be made publicly available; readers should contact the corresponding author for details. CONFLICT OF INTEREST The authors declare there is no conflict. REFERENCES Asian Development Bank 1996 Economic Evaluation of Environmental Impacts: A Workbook. Manila, Philippines. Azizipour, M., Sattari, A., Afshar, M. H. & Goharian, E. 2022 Incorporating reliability into the optimal design of multi-hydropower systems: A cellular automata-based approach. Journal of Hydrology 604, 127227. Bozorg Haddad, O., Moradi-Jalal, M. & Marino, M. A. 2011 Design–operation optimisation of run-of-river power plants. In Proceedings of the Institution of Civil Engineers-Water Management, Vol. 164, No. 9. Thomas Telford Ltd, pp. 463–475. Briones-Hidrovo, A., Uche, J. & Martínez-Gracia, A. 2019 Estimating the hidden ecological costs of hydropower through an ecosystem services balance: a case study from Ecuador. Journal of Cleaner Production 233, 33–42. Fanta, S. S., Namara, W. G. & Yesuf, M. B. 2022 Assessment of water supply and demand in Gilgel Gibe watershed, southwest Ethiopia. Sustainable Water Resources Management 8 (4), 1–28. Ferreira, J. H. I., Camacho, J. R., Malagoli, J. A. & Júnior, S. C. G. 2016 Assessment of the potential of small hydropower development in Brazil. Renewable and Sustainable Energy Reviews 56, 380–387. Finer, M. & Jenkins, C. N. 2012 Proliferation of hydroelectric dams in the Andean Amazon and implications for Andes-Amazon connectivity. PLoS ONE 7 (4), e35126. Fu, Y., Leng, J., Zhao, J., Na, Y., Zou, Y., Yu, B., Fu, G. & Wu, W. 2021 Quantitative calculation and optimized applications of ecological flowbased on nature-based solutions. Journal of Hydrology 598, 126216. Goel, V., Prakash, R. & Bhat, I. K. 2010 Life cycle energy and GHG analysis of hydroelectric power development in India. International Journal of Green Energy 7, 119. Gunawardena, U. P. 2010 Inequalities and externalities of power sector: a case of Broadlands hydropower project in Sri Lanka. Energy Policy 38 (2), 726–734. Hatamkhani, A. & Alizadeh, H. 2018 Clean-development-mechanism-based optimal hydropower capacity design. Journal of Hydroinformatics 20 (6), 1401–1418. Hatamkhani, A. & Moridi, A. 2019 Multi-objective optimization of hydropower and agricultural development at river basin scale. Water Resources Management 33 (13), 4431–4450. Hatamkhani, A. & Moridi, A. 2021 Optimal development of agricultural sectors in the basin based on economic efficiency and social equality. Water Resources Management 35 (3), 917–932. Hatamkhani, A., Moridi, A. & Asadzadeh, M. 2022a Water allocation using ecological and agricultural value of water. Sustainable Production and Consumption 33, 49–62. Hatamkhani, A., KhazaiePoul, A. & Moridi, A. 2022b Sustainable water resource planning at the basin scale with simultaneous goals of agricultural development and wetland conservation. Journal of Water Supply: Research and Technology-Aqua 71 (6), 768–781. Hondo, H. 2005 Life cycle GHG emission analysis of power generation system: Japanese case. Energy 30, 2042–2056. Hosnar, J. & Kovac-kraj, A. 2014 Mathematical modelling and MINLP programming of a hydro system for power generation. Journal of Cleaner Production 65, 194–201. Iran Water Resources Management Company 2020 Integrated Water Resources Management Studies of Maroon Watershed. Tehran, Iran. Jain S. K. 2015 Assessment of environmental flow requirements for hydropower projects in India. Current Science 108, 1815–1825. Legendre, P. 2013 Indicator species: computation. In: Encyclopedia of Biodiversity, Vol. 4. Elsevier Inc., pp. 264–268. http://dx.doi.org/ 10.1016/B978-0-12-384719-5.00430-5. Motamedi, J., Afradi, J., Sheidai Karkaj, E., Alijanpour, A., Emadodin, I., Shafiei, B. & Zandi Esfahan, E. 2020 Environmental factors affecting the structural trials and biomass of Onobrychis aurea Bioss. ECOPERSIA 8 (4), 247–259. Downloaded from http://iwaponline.com/hr/article-pdf/54/1/31/1163901/nh0540031.pdf by guest Hydrology Research Vol 54 No 1, 48 Nautiyal, H. & Goel, V. 2020 Sustainability assessment of hydropower projects. Journal of Cleaner Production 265, 121661. Paseka, S., Kapelan, Z. & Marton, D. 2018 Multi-objective optimization of resilient design of the multipurpose reservoir in conditions of uncertain climate change. Water 10 (9), 1110. Raeisi, S., Mousavi, S. J., Beidokhti, M. T., Rousta, B. A. & Kim, J. H. 2016 Economic optimization of hydropower storage projects using alternative thermal powerplant approach. In: Harmony Search Algorithm (Kim, J. H. & Geem, Z. W., eds). Springer, Berlin, Heidelberg, pp. 353–363. Rahi, O. P., Chandel, A. K. & Sharma, M. G. 2012 Optimization of hydro power plant design by particle swarm optimization (PSO). Procedia Engineering 30, 418–425. Ren, X., Zhao, Y., Hao, D., Sun, Y., Chen, S. & Gholinia, F. 2021 Predicting optimal hydropower generation with help optimal management of water resources by Developed Wildebeest Herd Optimization (DWHO). Energy Reports 7, 968–980. Shaeri Karimi, S., Yasi, M. & Eslamian, S. 2012 Use of hydrological methods for assessment of environmental flow in a river reach. International Journal of Environmental Science and Technology 9 (3), 549–558. Sieber, J. & Purkey, D. 2011 WEAP: Water Evaluation And Planning System. Stockholm Environment Institute, US Center, Somerville. Stevovic, S., Milovanovic, Z. & Stamatovic, M. 2015 Sustainable model of hydro power development – Drina river case study. Renewable and Sustainable Energy Reviews 50, 363–371. TAVANIR 2005 Technical and Economic Information of Power Plants. MOE of Iran, Tehran (in Farsi). Tennant, D. L. 1976 Instream flow regimens for fish, wildlife, recreation and related environmental resources. Fisheries 1, 6–10. USACE 1985 Engineering and Design Hydropower. Military Bookshop, USA. Wang, C., Yu, Y., Wang, P. F., Sun, Q. Y., Hou, J. & Qian, J. 2016 Assessment of the ecological reservoir operation in the Yangtze Estuary based on the salinity requirements of the indicator species. River Research and Applications 32 (5), 946–957. World Bank Group 2002 Environmental Energy Review (EER): Iran, Environment Strategy for the Energy Sector: Fuel for Thought, MOE, 511191/ZR/EER-Iran. Final Report. Xia, B., Qiang, M., Jiang, H., Wen, Q., An, N. & Zhang, D. 2020 Phase-based externality analysis for large hydropower projects. Environmental Impact Assessment Review 80, 106332. Yazdi, J. & Moridi, A. 2018 Multi-objective differential evolution for design of cascade hydropower reservoir systems. Water Resources Management 32 (14), 4779–4791. Zhang, Q., Karney, B., MacLean, H. L. & Feng, J. 2007 Life-cycle inventory of energy use and greenhouse gas emissions for two hydropower projects in China. Journal of Infrastructure Systems 13 (4), 271–279. Zhang, J., Xu, L. & Li, X. 2015 Review on the externalities of hydropower: a comparison between large and small hydropower projects in Tibet based on the CO2 equivalent. Renewable and Sustainable Energy Reviews 50, 176–185. First received 29 July 2022; accepted in revised form 9 December 2022. Available online 30 December 2022 Downloaded from http://iwaponline.com/hr/article-pdf/54/1/31/1163901/nh0540031.pdf by guest