Recently, the Kessem–Tendaho project is completed to bring about socioeconomic development and growth in the Awash River Basin, Ethiopia. To support reservoir Koka, two new reservoirs where built together with extensive infrastructure for new irrigation projects. For best possible socioeconomic benefits under conflicting management goals, like energy production at three hydropower stations and basin wide water supply at various sites, an integrated reservoir system management is required. To satisfy the multi-purpose nature of the reservoir system, multi-objective parameterization-simulation-optimization model is applied. Different Pareto-optimal trade-off solutions between water supply and hydro-power generation are provided for two scenarios (i) recent conditions and (ii) future planned increases for Tendaho and Upper Awash Irrigation projects. Reservoir performance is further assessed under (i) rule curves with a high degree of freedom – this allows for best performance, but may result in rules curves to variable for real word operation and (ii) smooth rule curves, obtained by artificial neuronal networks. The results show no performance penalty for smooth rule curves under future conditions but a notable penalty under recent conditions.
Recently, the Kessem–Tendaho Project is completed to bring
about socioeconomic development and growth in the Awash River Basin,
Ethiopia. To support the existing Koka reservoir two new reservoirs where
built together with extensive infrastructure for new irrigation projects.
Besides the basin wide supply of water of municipal water, irrigation water
for various agricultural sites and sugar cane plantations, the reservoirs are
also responsible for flood protection. Hydropower production is a critical
factor for the local economy. Koka reservoir provides hydropower through
hydro-power station Awash I and supports the hydro-power stations Awash II
and III. Development plans project an increase of 40 000
For maximum socioeconomic gains an integrated reservoir system management is crucial. To achieve this, optimal operational policies for all reservoirs are needed. Mathematical optimization models are used widely in water resources management to provide operational policies for optimal integrated reservoir management (Loucks et al., 1981). To account for multi-purpose nature of the Awash River Basin reservoir system a multi-objective parameterization-simulation-optimization (PSO) model is developed in this study. In PSO a reservoir management simulation model is coupled to an optimization algorithm to iteratively search for better operational policies. The advantages of PSO over other common optimization techniques are discussed in Koutsoyiannis and Economou (2003).
Yibetal et al. (2013) analysed the water audit of Awash Basin using WEAP model on the basis of three different scenarios (Expansion of irrigation area, improvement of irrigation and Climate change). Berhe et al. (2013) assessed the water allocation for future development scenarios in a modelling study using MODSIM model (Labadie, 2007). However hydropower production is modelled on purely opportunistic basis, because releases from the reservoir respond only to irrigation demands.
This study is a first step to provide optimal rule curves for an integrated
management of the reservoir system for possible compromises between energy
production and basin wide water supply for (i) recent conditions and (ii) the
planned increase of 40 000
The Awash River originates from a high plateau, which is the central
Ethiopian Highland and an elevation up to 3000
Awash River basin is one of the twelve basins in Ethiopia. The basin has a
total catchment area of 110 000
Map of Awash River Basin, highlighting the reservoir system and the local irrigation projects (adopted from Gebretsadik, 2015).
Awash River has 15 important tributaries which significantly contribute for the flow of the main course.
The land use is dominated by exposed rock with about 34.9 % followed by
cultivated land of about 27 % and open shrub land (20.9 %). The
seasonal distribution of rainfall with two distinct rainy periods is caused
by a shifting of the Inter Tropical Convergence Zone. The March–May season
is the main rainfall season yielding 100–200
For all reservoirs upper rule curves define the top of conservation storage zones and lower rule curves define the top of buffer storage zones. Important reservoir characteristics are summarized in Table 1. These storages mark upper and lower constraints in the optimization as the task is, to find optimal monthly storage values for each zone. Storage above the conservation zone is designated for flood protection.
Awash I hydropower station, located at Koka reservoir houses three units with
14.4 MW capacity. Average and firm production of hydropower are 110 and
80
Schematic representation of Awash Basin multi-reservoir system in the modelling software OASIS.
The regulated turbine flow is restricted to 40
Management zones and total storage in
Therefore, Gedabbesa swamp is modelled as follows: average monthly patterns for losses to the swamp and returns from the swamp are calculated from gauging stations upstream and downstream of the swamp. In the model all flow exists at node 702 and the difference between the monthly pattern of losses and the flow to node 702 is returned at node 703. Additional mean monthly returns enter at node 701. Similarly, little data is available for Lake Abe, the terminal lake of Awash River.
Awash I hydropower station, located at Koka reservoir houses three units with
14.4 MW capacity. Average and firm production of hydropower are
110
The competing management goals of water supply for irrigation projects,
municipal, ecology and the production of hydropower in the Awash river basin
requires the formulation of two objective functions. Objective function
Pareto-Fronts and rule curves (RC) for all solutions from a specific
model for
Three models are considered, which vary in the formulation of the rule
curves. In model MOD1 the lower rule curve of reservoir Koka is kept
constant. For model MOD2 this lower rule is seasonally variable. Decision
variables for MOD1 and MOD2 are the storage control volumes. A constraint
free formulation and bounded formulation from Müller (2014) is used for
MOD1 and MOD2. Smooth variable rule curves for MOD3 are obtained by
formulating the rule curves as an artificial neuronal network
Optimization runs for the three models where conducted with 60 000 model evaluations and a population size of 48 each. The resulting Pareto-Fronts are depicted for recent conditions in Fig. 3a and for the future development plan in Fig. 3b.
With nearly no deficits under recent conditions and 440 GWh per annum energy
production, MOD1 performs best when preference is set to minimizing deficits
(Objective
For reservoir Tendaho MOD3 proposes a draw down period from March to April and refill in July for low deficits. Surprisingly, RCs for high energy production require an empty reservoir Tendaho.
MOD1 and MOD2 result in lower storages throughout the year and only a major fill in July; this reduces evaporation losses and support from upstream reservoirs. The RCs from all models show the same general course; yet, MOD2 produces the most variable RCs with several draw downs and refills.
Under future development plans no model dominates the others in overall
performance, but the models cover different spaces of the Pareto-space. This
might be due to the formulation of the RCs or an optimization related
problem. In general, deficits under increases demands can be as low as
An integrated reservoir management for the multi-reservoir system in the Awash River Basin is needed to maximize socioeconomic benefits. Multi-objective optimization is carried out for conflicting management goals of energy production and basin wide water supply. Future development plans for expansion in irrigation sites are considered. Reservoir performance is assessed by rule curves with a high degree of freedom and smooth rule curves, obtained by artificial neuronal networks. For recent conditions the smoothness constraint rule curves cause a performance penalty for the reservoir management. The available water resources in the system are sufficient and a high degree of freedom (non-smooth rule curves) allows for a timely precise allocation. Under the development plans with a higher stress on the system, this cannot be observed. The higher degree of freedom cannot provide any additional performance gains. However, the trade-offs under both scenarios and for all considered models are only huge in terms of deficit, while relative gains in energy performance are negligible. A balanced solution will focus on low deficits and the decision maker may choose his preferred management by special consideration of the underlying rule curves.
It is advised to conduct studies to enhance understanding of Gabeddesa Swamp. Additionally shortcomings in the model, like missing translation times for water routing and irrigation efficiencies need to be addressed.
The second author would like to thank the German Academic Exchange Service (DAAD, grant no. A/13/90526) for awarding a scholarship.