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

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 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.

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).

Table 1

PSO parameters

Parameter .	Value .
	1
	0.9
	0.4
	1.8
	1.8
Swarm size	9
Particle size	3

Parameter .	Value .
	1
	0.9
	0.4
	1.8
	1.8
Swarm size	9
Particle size	3

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The limits of decision variables are shown in Table 2.

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 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.

In these relations, p is the duration of the construction of the thermal plant, is the percentage of cost spent in year p before the start of the operation, is the installed capacity of the alternative thermal plant in megawatt, is the cost of construction per unit of capacity, is firm energy produced by hydropower plant, is secondary energy produced by hydropower plant, is the number of hours per year, PF is the thermal plant factor, is the fixed operation and maintenance cost per unit of capacity, is the variable operation and maintenance cost per unit of energy generation, is thermal plant availability, is fuel costs, is the cost per unit of thermal plant fuel, is the heating value of fuel, and is the thermal plant efficiency.

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 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.

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.

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.

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.

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.

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.

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 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 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.

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%.

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.

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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