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Article

Application of Dynamic Non-Linear Programming Technique to Non-Convex Short-Term Hydrothermal Scheduling Problem

1
Smart Energy Systems Laboratory, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666-15813, Iran
2
Department of Architectural Engineering, Pennsylvania State University, 104 Engineering Unit A, University Park, PA 16802, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Ånund Killingtveit
Energies 2017, 10(9), 1440; https://doi.org/10.3390/en10091440
Received: 22 August 2017 / Revised: 7 September 2017 / Accepted: 16 September 2017 / Published: 19 September 2017
(This article belongs to the Special Issue Hydropower 2017)

Abstract

Short-term hydro-thermal scheduling aims to obtain optimal generation scheduling of hydro and thermal units for a one-day or a one-week scheduling time horizon. The main goal of the problem is to minimize total operational cost considering a series of equality and inequality constraints. The problem is considered as a non-linear and complex problem involving the valve-point loading effect of conventional thermal units, the water transport delay between connected reservoirs, and transmission loss with a set of equality and inequality constraints such as power balance, water dynamic balance, water discharge, initial and end reservoir storage volume, reservoir volume limits and the operation limits of hydro and thermal plants. A solution methodology to the short-term hydro-thermal scheduling problem with continuous and non-smooth/non-convex cost function is introduced in this research applying dynamic non-linear programming. In this study, the proposed approach is applied to two test systems with different characteristics. The simulation results obtained in this paper are compared with those reported in recent research studies, which show the effectiveness of the presented technique in terms of total operational cost. In addition, the obtained results ensure the capability of the proposed optimization procedure for solving short-term hydro-thermal scheduling problem with transmission losses and valve-point effects.
Keywords: dynamic non-linear programming; non-smooth/non-convex optimization problem; short term hydro-thermal scheduling; transmission losses; valve point loading effect dynamic non-linear programming; non-smooth/non-convex optimization problem; short term hydro-thermal scheduling; transmission losses; valve point loading effect

1. Introduction

Power systems are faced with a series of challenging issues taking into account the advances and improvements within them. Remarkable research is being carried out in various areas such as the application and analysis of micro-grids (MGs) and distributed generations (DGs) in the optimal operation of power systems [1], transient stability analysis in power systems [2], dynamic operation and control of the systems [3], connection decisions of distribution transformers [4], and fault current analysis of power systems [5,6]. The authors implemented a direct search method (DSM) in [1] for solving economic dispatch (ED) of a medium-voltage MG considering several kinds of DGs. A modified artificial bee colony (MABC) optimization technique is applied in [7] for obtaining the optimal solution of the ED problem, where a novel mutation strategy based on the differential evolution (DE) method is used for improving the capability of the method in providing the optimal solution. The valve-point loading effect of conventional thermal plants is considered in this study. The authors proposed a three-stage technique in [8] to solve the ED problem of distribution-substation-level MGs, where the main power grid and MGs are studied as two key parts of the system. In this reference, the ED of the main grid and local MGs are solved using sensitive factors and an improved direct search method in stages I and II, respectively, and the optimal reschedules from the original dispatch solutions are provided in stage III. The authors have addressed the ED problem considering voltage magnitudes and reactive power flows in [9], where linear programming method is utilized for solving the problem. In this study, thermal capacities of transmission lines and line power transmission, and exponential loads are studied using piecewise linear models. Power system expansion planning is studied in [10], where costs associated with the fuel and buying emission allowances, and benefits from selling emission allowances are considered. A piecewise linear objective function is proposed for calculating the sensitivity of operation cost with respect to limitations of emission.
Short-term hydro-thermal scheduling (STHTS) is defined as one of the most important and challenging issues in power systems operation. Thermal power plants operational costs are high; however, the initial costs of such generation units are lower. On the other hand, the operational costs of hydro power plants are insignificant; however, the construction costs of such plants are high [11,12]. Accordingly, the combination of these two types of power plants can be considered as an appropriate choice considering economic viewpoints. The main goal of short-term scheduling of hydro-thermal system is determining the optimal power generation of the hydro and thermal plants. The optimal solution provides the minimum total operational cost of the thermal units, while satisfying load demand and a series of equality and inequality constraints of the hydraulic and thermal power system network. The STHTS problem is proposed as a complex non-linear, non-convex and non-smooth optimization problem considering the water transport delay between connected reservoirs, the valve-point loading effect related to the thermal units, transmission loss and many equality and inequality constraints [13,14].
Different optimization methods are employed to obtain optimal solution of generation planning of hydrothermal systems, including heuristic and classical methods. A modified dynamic neighborhood learning based particle swarm optimization (MDNLPSO) method is introduced in [15] to solve the STHTS problem. In this reference, the proposed approach is applied on two test systems with different characteristics. STHTS problem is solved in [16] by employing quadratic approximation based on differential evolution with valuable trade-off (QADEVT) that minimizes fuel cost and pollutant emission simultaneously. The predator prey optimization (PPO) procedure is used in [17] to obtain the optimal power production planning of hydro and thermal units. In [18], a hybrid method differential evolution with adaptive Cauchy mutation is utilized to obtain the optimal generation scheduling of hydro and thermal units, in which water transport delay between connected reservoirs and the effect of valve-point loading of thermal power plants is taken into account. Particle swarm optimization (PSO) is introduced in [19] to deal with STHTS problem with non-convex and non-smooth cost function. The real coded genetic algorithm (RCGA) is used for the solution of STHTS problem with a series of equality and inequality restrictions and non-smooth/non-convex cost function. The suggested algorithm in this reference is armed with a restriction-management approach which eliminates the requirement of penalty parameters. In [20], by using the Lagrangian Relaxation (LR) method, not only are the electrical and hydraulic constrains handled, but also the existing network constraints are considered by employing DC power flow. The lexicographic optimization and hybrid augmented-weighted ε-constraint method are applied in [21] to produce Pareto optimal solutions for STHTS problem. In this reference, mixed integer programming (MIP) is introduced to obtain the optimal power generation planning of hydrothermal system in a day-ahead joint energy and reserve market. In [22], an improved merit order (IMO) and augmented Lagrangian Hopfield network (ALHN) is proposed to solve short-term hydrothermal scheduling with pumped-storage hydro units. The proposed method in this reference considers thermal, hydro and pumped-storage unit commitment (UC). The STHTH problem is solved in [23] with the consideration of AC network constraints, which is implemented a combination of the Benders decomposition method and Bacterial Foraging oriented by Particle Swarm Optimization (BFPSO) method. The application of chaotic maps in a particular game problem called the Parrondo Paradox is studied in [24]. The proposed approach was used in a three-game problem and a more general N-game problem in which non-linear optimization problem is considered to define the parameters for the studied game.
In this study, the STHTS problem is solved using dynamic non-linear programming (DNLP) using general algebraic modeling system (GAMS) software. The valve-point effect of conventional thermal plants, which increases the complexity of solving STHTS problem, is considered in the solution of the problem. In addition, the power transmission loss of the hydro-thermal system is studied in the proposed study. Different case studies are solved to evaluate the performance and ensure the effectiveness of the introduced method. The optimal solutions are compared with those reported in previous studies in terms of total operational cost, which demonstrates the capability of the proposed method to identify solutions having less operational cost. In addition, optimal solutions obtained in this paper ensures the capability of the proposed method to deal with valve-point loading effect of thermal units and system power transmission loss.
The rest of the paper is organized as follows: The mathematical formulation of the STHTS problem is provided in Section 2. Section 3 introduces the proposed solution method for STHTS problem. In Section 4, the proposed approach is implemented on two test systems and the obtained optimal solutions are compared with those reported in previous studies. Finally, the paper is concluded in Section 5.

2. Problem Formulation

The optimal scheduling of hydro-thermal plant includes a non-linear optimization problem involving objective function and a set of linear, non-linear and dynamic constraints. The objective function and equality and inequality constraints of the STHTS problem are explained in the following [25].

2.1. Objective Function

The main goal of short-term planning of hydro-thermal system is determining the optimal power generation of the hydro and thermal plants so as to minimize the total operation cost of the thermal units since the cost of hydro production is insignificant. It should be mentioned that various constraints on the hydraulic and thermal power system network should be considered in the solution of the problem. The objective function to be minimized can be represented as follows [26]:
C ( P ) = t = 1 24 i = 1 N s a i + b i P i t + c i ( P i t ) 2 + | e i s i n ( f i ( P i m i n P i t ) ) |
where C(P) is the total fuel cost. NS is indicator used for the number of thermal plants. Moreover, P i t is power generated by the ith thermal plant at time t. a i , b i , and c i are the cost coefficients of ith thermal plant. Considering multiple steams valves in conventional thermal power plants, it is essential to model the effect of valve-points on fuel cost. Valve-points effect can be modeled by a sinusoidal term, which will be added to the quadratic cost function [27]. P i m i n is minimum power generation of thermal unit i. Moreover, e i and f i are valve-point coefficients of cost function of thermal unit i.

2.2. Power Balance Constraint

The total power generated by hydro and thermal plants should be equal to the sum of total load demand and transmission line losses.
i = 1 N S P i t + j = 1 N h P j t = P D t + P L t
where Nh is the number of hydro units. P j t is the generation of hydro units in megawatts (MW). Moreover, P D t and P L t are load demand and total transmission loss in MW, respectively. P L t can be calculated using the Kron’s loss formula known as B-matrix coefficients [28]. Equation (3) calculates power transmission loss utilizing Kron’s loss formula, which is defined as B-matrix coefficients method in this paper as follows:
P L t = i = 1 N h + N s j = 1 N h + N s P i t B i j P j t + i = 1 N h + N s B i o P i t + B 00 t = 1 , 2 , , T
The coefficients are Kron’s loss formulation used to calculate power transmission of the hydrothermal system. The power loss of the system taking into account N s hydro plants and N h thermal units can be calculated by using such formulation. B-matrix coefficients for calculating the power loss are shown by Bij, Bio, and B00. In such formulation, Bmn is element of matrix B with dimension of ( N S + N h ) × ( N S + N h ) . In addition, B0n is vector of the same length as P, and B00 is considered as a constant.
The hydro power generation, P j t , is a function of water discharge and storage volume, which can be calculated as follows:
P j t = C 1 , j ( V j t ) 2 + C 2 , j ( Q j t ) 2 + C 3 , j V j t Q j t + C 4 , j V j t + C 5 , j Q j t + C 6 , j
where V j t is the storage volume of reservoir in m3, and C 1 , j , C 2 , j , C 3 , j , C 4 , j , C 5 , j , and C 6 , j represent hydro power generation coefficients. Moreover, Q j t is the water discharge amount in m3.

2.3. Limitations of Power Production

The generator capacity constraints are expressed as:
P i min P i t P i max P j min P j t P j max
where P i min and P i max are the respective lower and upper bounds of power generation of thermal units. In addition, the minimum and maximum amounts of power production of hydro units are indicated by P j min and P j max , respectively.

2.4. Hydraulic Network Constraints

2.4.1. Water Dynamic Balance

The reservoir storage of hydro unit is related to previous inflow and spillage, and storage of reservoir discharge from upstream reservoirs, which can be formulated as:
V j t = V j t 1 + I j t Q j t S j t + m = 1 ϕ j [ Q m ( t τ m j ) t + S m ( t τ m j ) t ] , m ϕ j
where I j t is the inflow rate of the reservoir, ϕ j is set of instant upstream hydro plants of the jth reservoir. Additionally, τ is time delay of immediate downstream plants.

2.4.2. Reservoir Storage Volume Limits

The operating volume of reservoir should be limited in interval between minimum and maximum values, which can be stated as:
V j min V j t V j max
where V j min and V j max are the respective lower and upper bounds of operating volume of the reservoir of ith hydro unit.

2.4.3. Water Release Limits

The water release of hydro units should be limited to minimum and maximum values, which can be considered as:
Q j min Q j t Q j max
where Q j min and Q j max are the minimum and maximum release of the water reservoir of the ith hydro plant.

2.4.4. Initial and Final Reservoir Storage Volume

Initial and final volumes of reservoir storage should be taken into account in the formulation of STHTS problem as:
V j t | t = 0 = V j b e g i n V j t | t = τ = V j e n d
where V j b e g i n is the elementary volume of the reservoir and V j e n d is the final volume of the reservoir.

3. Solution Methodology

GAMS is defined as a practical tool to handle general optimization problems, which consists of a proprietary language compiler and a variety of integrated high-performance solvers. GAMS is specifically designed for large and complex problems, which allows creating and maintaining models for a variety of applications. GAMS is able to formulate models in many different types of problem classes, such as linear programming (LP), nonlinear programming (NLP), mixed-integer linear programming (MILP), mixed-integer nonlinear programming (MINLP) and dynamic nonlinear programming (DNLP). Nonlinear models created in GAMS area should be solved by using an NLP algorithm. This paper offers a novel approach based on the NLP method to obtain optimal planning of hydrothermal systems. Accordingly, the STHTS is modeled as a NLP in this study, and is solved by implementing OptQuest/NLP (OQNLP) solver. The STHTS problem is formulated as a nonlinear problem, which can be solved by GAMS software [29] using OQNLP solver [30]. OQNLP is a multi-start heuristic technique, which calls an NLP solver from different starting points. All feasible solutions obtained by such solvers are kept, and the best solution is reported as the final optimal solution. Such a method is capable of finding global optimal solutions of smooth constrained NLPs. A scatter search implementation called OptQuest is employed by OQNLP to compute starting points [31]. OQNLP is able to obtain global optimal solutions of smooth NLPs and MINLPs. A simplified pseudo-code is provided in Figure 1 for introducing the application of OQNLP to find the optimal solution of the optimization problems, which is divided into two levels. The first level generates candidate starting points and selects the best starting point among all of the points. Then, in the second level, new points are generated and evaluated in order to obtain the best solution in terms of generation cost.

4. Case Studies and Simulation Results

In this paper, the performance of the proposed solution is evaluated in several test systems. A Pentium IV PC with 2.8 GHz CPU and 4 GB RAM PC is used to solve the problem in GAMS. The scheduling horizon is chosen as 24 h of a day.

4.1. Test System 1

First test system consists of four hydro plants and an equivalent thermal plant. The hydraulic communication among hydro units of this system is demonstrated in Figure 2. Transmission losses are not considered in this test system. Cost coefficients of thermal plants are ai = 0.002, bi = 19.2, and ci = 5000. The lower and upper operation limits of this thermal plant are 500 and 2500 MW, respectively. Data of thermal unit and hydro plants are adopted from [25]. Two different cases including convex and non-convex cost function are studied for this test system.

4.1.1. Test System 1 Case 1: Quadratic Cost without Valve-Point Loading Effect

In this case, optimal generation scheduling of test system 1 is solved without consideration of valve-point loading impact. The hourly water discharge of the hydro plants and hydro power production, which is calculated by employing Equation (7), are shown in Table 1. In addition, thermal power production for case 1 is provided in Table 1. According to Table 1, the sum of power generation by four hydro units and one thermal plant meets total demand of the system. Hourly hydro discharges of the optimal solution are demonstrated in Figure 3. Considering Figure 3, hydro plant 4 has the maximum discharge among four hydro units, which shows that the power generation of hydro plant 4 is more than the others. In addition, hourly hydro and thermal plant generations are illustrated in Figure 4. The thermal units participates in power demand supply more than the hydro plants according to Figure 4. Moreover, total load demand is satisfied by the power generation of four hydro units and the thermal plants, which is obvious in Figure 4.
The obtained results are compared with those obtained by employing quantum-inspired evolutionary algorithm (QEA) [25], quantum-inspired evolutionary algorithm (WDA) [32], small population-based particle swarm optimization (SPSO) [33], real coded genetic algorithm (RCGA) [34], real-coded quantum-inspired evolutionary algorithm (RQEA) [25], DE [25], modified differential evolution (MDE) [33], differential real-coded quantum-inspired evolutionary algorithm (DRQEA) [25], hybrid chemical reaction optimization (HCRO)-DE [35], modified adaptive particle swarm optimization (MAPSO) [36], real-coded genetic algorithm and artificial fish swarm algorithm (RCGA-AFSA) [34], teaching learning-based optimization (TLBO) [37], smallpopulation-based particle swarm optimization (SPPSO) [33], self-organizing hierarchical particle swarm optimization technique with time-varying acceleration coefficients (SOHPSO_TVAC) [38], PSO [39], improved differential evolution (IDE) [40], fuzzy adaptive particle swarm optimization (FAPSO) [39], dynamic neighborhood learning based particle swarm optimization (DNLPSO) [15], and modified dynamic neighborhood learning based particle swarm optimization (MDNLPSO) [15], and is shown in Table 2. As it can be observed from this table, the best reported cost for this case is equal to $914,660, which is related to FAPSO [39], while total operational cost of the solution obtained by the proposed method is $884,733.965. Accordingly, the proposed method is capable to find better solution in comparison with previous methods in terms of total operational cost.

4.1.2. Test System 1 Case 2: Quadratic Cost Function with Valve-Point Loading

In this case, optimal power scheduling of test system 1 is obtained with consideration of valve-point loading effect. The parameters of valve-point loading impact of thermal unit are ei = 700 and fi = 0.085. The simulations are provided for case 2 with non-convex fuel cost. The optimal planning of discharge of four hydro units are reported in Table 3. In addition, power generation of hydro units, which is obtained by applying Equation (7), are provided in this table. In addition, power production of thermal power plants are presented in Table 3. It can be observed from Table 3 that the power demand during 24-h scheduling time is satisfied by total power generation of four hydro units and one thermal unit.
The optimal solution obtained in this research study is compared with those reported in recent paper, which include QEA [25], DE [25], RCGA-AFSA [34], RQEA [25], DRQEA [25], CRQEA [25], RCCRO [41], ACDE [42], MAPSO [36], TLBO [37], RCGA [34], RQEA [25], DE [25], MDE [33], DRQEA [25], HCRO-DE [35], MAPSO [36], MDNLPSO [15], IDE [41], TLBO [37], RCGA-AFSA [34], SPPSO [33], SOHPSO_TVAC [38], PSO [39], Improved DE [40], IDE [40], and FAPSO [39], and is shown in Table 4. As it can be seen in this table, the minimum total operational cost reported for this case is $914,660.00, which is obtained by applying FAPSO [39]; however, the proposed method in this paper obtained the minimum cost equal to $901,191.9735, which shows the capability of the proposed method in obtaining optimal solution of the STHS problem for test system 1, case 2 with respect to other optimization methods.

4.2. Test System 2

This test system consists of four cascaded hydro power plants and three thermal plants. Valve-point loading effect of thermal plants and transmission losses are considered in this test system. Data of hydro and thermal generation units are adopted from [42]. Coefficients of transmission loss for this system are given as the following:
B = ( 0.34 0.13 0.09 0.10 0.08 0.01 0.02 0.13 0.14 0.10 0.01 0.05 0.02 0.01 0.09 0.10 0.31 0.00 0.11 0.07 0.05 0.01 0.01 0.00 0.24 0.08 0.04 0.07 0.08 0.05 0.11 0.08 1.92 0.27 0.02 0.01 0.02 0.07 0.04 0.27 0.32 0.00 0.02 0.01 0.05 0.07 0.02 0.00 1.35 ) × 10 4 MW 1 B 0 = [ 0.75 0.06   0.70 0.03   0.27 0.77 0.01 ] × 10 6 B 00 = 0.55 MW

4.2.1. Test System 2, Case 1: Quadratic Cost without Valve-Point Loading Effect

This test system consists of four cascades hydro plants and three thermal plants considering valve-point loading effect for all thermal units. In this case, transmission loss is not considered. The optimal hydro discharges and hydro power generation of four hydro units are provided in Table 5. Moreover, power generations of three thermal plants are reported in this table. According to Table 5, the sum of power generation of four hydro units and three thermal plants meets the load demand during the scheduling time of the STHS problem.
Proposed method provided the minimum fuel cost of $41,101.738, which is compared with simulated annealing (SA) [25], DE [11], chaotic artificial bee colony (CABC) [26], adaptive differential evolution (ADE) [23], RCGA [13], DE [10], SPPSO [12], RQEA [10], PSO [27], chaotic differential evolution (CDE) [23], clonal selection algorithm (CSA) [28], TLBO [29], TLBO [18], improved quantum-behaved particle swarm optimization (IQPSO) [30], quasi-oppositional teaching learning based optimization (QTLBO) [29], Improved differential evolution (IDE) [21], adaptive chaotic differential evolution (ACDE) [23], real coded chemical reaction based optimization (RCCRO) [22], differential real-coded quantum-inspired evolutionary algorithm (DRQEA) [10], and adaptive chaotic artificial bee colony algorithm (ACABC) [26], quasi-oppositional group search optimization (QOGSO), as shown in Table 6. Results show that proposed method is better than previous methods used in the test system 2, case 1. As it can be seen, the minimum obtained cost is $41,274.42 which is related to ACABC [43] compare to $41,101.738 obtained by proposed method.

4.2.2. Test System 2 Case 2: Quadratic Cost Function with Valve-Point Loading

The valve-point effects and transmission losses are considered in this case, which make the problem more complex. The optimal result obtained by OQNLP is reported in Table 7. The hourly discharge of four hydro plants and the power generation of the hydro units are prepared in this table. In addition, power generation of three thermal plants are reported in Table 7. The power transmission loss of the hydrothermal system by applying Equation (13) during 24-h scheduling time interval is also reported in this table. In this case, considering Table 7, total generation of four hydro units and three thermal plants meets total load demand and power transmission loss of the system.
Comparisons of simulation results for this case study are accomplished in Table 8. It can be observed that the obtained result using the proposed method outperform the results of QEA [25], ABC [43], DE [32], SPPSO [50], RQEA [25], DNLPSO [15], PSO [51], CSA [44], TLBO [33], SA-MOCDE [37], GSA [52], QOTLBO [33], MOCA-PSO [53], SHPSO-TAC [51], IDE [40], RCGA-AFSA [34], QABDEVT [16], ACDE [54]. Taking into account transmission losses and valve-point effects, the best reported solution is related to ACDE [54], which obtained total cost of $41,593.48. However, the proposed method provided the optimal solution with the total operational cost of $41,350.5574 which is better than other methods.

5. Conclusions

In this study, dynamic non-linear programming is introduced to obtain optimal scheduling of a hydrothermal system. The valve-point loading impact of conventional thermal units and system power transmission loss are considered in finding the optimal solution of the short-term hydro-thermal scheduling problem by studying two test systems. Optimal solutions are reported and analyzed, and are compared with those provided in recent papers. Results showed the capability of the proposed method to obtain better solutions in terms of total operational cost in comparison with other heuristic algorithms. Test system 1 includes four cascaded hydro units and one equivalent thermal plant, in which daily savings are $29,926.035 and $13,468.026 in comparison with previously reported solutions for both cases 1 and 2, respectively. In addition, for test system 2, which contains four cascaded hydro units and three thermal plants, daily savings are $172.682 and $242.9226 in comparison with reported solutions in previous studies for both cases 1 and 2, respectively. The optimal solutions show that the proposed method is an effective and high-performance technique to solve short-term hydro-thermal scheduling problem considering transmission losses and valve-point loading effects. The future research trends in the area of short-term hydro-thermal scheduling can be concentrated on consideration of limitations of AC network constraints. In addition, the unit commitment problem of hydrothermal systems, considering the start-up cost, minimum uptime, and minimum downtime of the generation units can be considered as another research topic in this area. Moreover, the unavailability of the generation units and consideration of renewable energy sources such as wind power are other exciting subjects to be investigated. Also, middle and long-term scheduling of hydro-thermal system, considering the installation and maintenance cost of hydro and thermal plants, may be introduced as interesting subject in the area of hydro-thermal systems.

Author Contributions

All authors have contributed equally work.

Conflicts of Interest

The authors declare no conflict of interest.

Nomenclature

Indexes
tTime interval of planning
NsThe number of thermal plants
NhThe number of hydro units
Constants
a i , b i and c i Cost coefficients of ith thermal plant
e i and f i Valve-point coefficients of cost function of thermal unit i
P i min Minimum power generation of thermal unit i
P i max Maximum power generation of thermal unit i
V j min Lower of operating volume of reservoir of ith hydro unit
V j max Upper bounds of operating volume of reservoir of ith hydro unit
Q j min Minimum release of water reservoir of the ith hydro plant
Q j max Maximum release of water reservoir of the ith hydro plant
V j b e g i n Elementary volume of reservoir
V j e n d Final volume of reservoir
P D t Load demand at time t
C 1 , j , C 2 , j , C 3 , j , C 4 , j , C 5 , j , and C 6 , j Hydro power generation coefficients
ϕ j Set of instant upstream hydro plants of jth
Variables
P i t Power generated by the ith thermal plant at time t
P j t Generation of hydro units
P L t Total transmission loss at time t
V j t The storage volume of reservoir
Q j t The water discharge amount
I j t The inflow rate of the reservoir
Acronyms
STHTSShort-term hydro-thermal scheduling
DNLPDynamic non-linear programming
MGSMicro-grids
DGSDistributed generations
DSMDirect search method
EDEconomic dispatch
MABCModified artificial bee colony
DEDifferential evolution
MDNLPSOModified dynamic neighborhood learning based particle swarm optimization
QADEVTQuadratic approximation based on differential evolution with valuable trade-off
PPOPredator prey optimization
PSOParticle swarm optimization
RCGAReal coded genetic algorithm
LRLagrangian relaxation
MIPMixed integer programming
IMOImproved merit order
ALHNAugmented Lagrangian hopfield network
UCUnit commitment
BFPSOBacterial foraging oriented by particle swarm optimization
DNLPDynamic non-linear programming
GAMSGeneral algebraic modeling system
LPLinear programming
NLPNonlinear programming
MILPMixed-integer linear programming
MINLPMixed-integer nonlinear programming
DNLPDynamic nonlinear programming
QEAQuantum-inspired evolutionary algorithm
WDAWhole distribution algorithm
SPSOSmall population-based particle swarm optimization
RQEAReal-coded quantum-inspired evolutionary algorithm
MDEModified differential evolution
DRQEADifferential real-coded quantum-inspired evolutionary algorithm
DNLPSODynamic neighborhood learning based particle swarm optimization
HCROHybrid chemical reaction optimization
MAPSOModified adaptive particle swarm optimization
RCGA-AFSAReal coded genetic algorithm and artificial fish swarm algorithm
TLBOTeaching learning-based optimization
SOHPSO_TVACSelf-organizing hierarchical particle swarm optimization technique with time-varying acceleration coefficients
IDEImproved differential evolution
FAPSOFuzzy adaptive particle swarm optimization
ACDEAdaptive chaotic differential evolution
CABCAdaptive chaotic artificial bee colony
CSAClonal selection algorithm
IQPSOImproved quantum-behaved particle swarm optimization
GSOGroup search optimization
ACDEAdaptive chaotic differential evolution algorithm

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Figure 1. Pseudo-code of the applied OptQuest/NLP (OQNLP) method.
Figure 1. Pseudo-code of the applied OptQuest/NLP (OQNLP) method.
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Figure 2. Hydro subsystem used in the all test systems.
Figure 2. Hydro subsystem used in the all test systems.
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Figure 3. Hourly hydro discharges volumes of the optimal solution for test system 1, case 1.
Figure 3. Hourly hydro discharges volumes of the optimal solution for test system 1, case 1.
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Figure 4. Hourly hydro and thermal plant generations for test system 1, case 1.
Figure 4. Hourly hydro and thermal plant generations for test system 1, case 1.
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Table 1. Hourly plant discharges, power outputs and total thermal generation (test system 1, case 1).
Table 1. Hourly plant discharges, power outputs and total thermal generation (test system 1, case 1).
HourHydro Plant Discharges (104 m3)Hydro Power Output (megawatts (MW))Thermal Generation (MW)Total Generation (MW)
Plant 1Plant 2Plant 3Plant 4Plant 1Plant 2Plant3Plant 4
16.2546.00011.63215.34461.52845.31656.480224.231982.4451370
26.4886.00011.91416.91964.33946.57655.928219.6941003.4641390
36.5946.00012.30318.53765.77747.80456.376209.020981.0241360
46.5926.00012.74120.00066.10249.58657.320189.900927.0921290
56.4316.00013.12920.00064.97251.29657.694306.000810.0391290
66.6176.00013.53120.00066.28452.39658.133306.000927.1871410
77.0056.00013.92320.00069.23852.93458.611306.0001163.2171650
87.5456.00014.25420.00073.25952.93458.728306.0001509.0792000
97.9276.00014.56820.00076.16653.46458.565306.0001745.8052240
108.1096.00014.89420.00077.85154.50058.136306.0001823.5122320
118.0876.00015.27720.00078.36755.99457.612306.0001732.0272230
128.2726.00015.62120.00080.35357.41657.056306.0001809.1752310
138.1656.25416.04720.00079.96360.18056.475306.0001727.3822230
148.1246.61316.49420.00080.13163.51256.112306.0001694.2452200
158.0436.92716.93920.00080.07466.72755.351306.0001621.8482130
167.9307.27217.13720.00079.56569.94754.948306.0001559.5402070
177.9507.67015.69420.00079.85872.86857.561306.0001613.7122130
187.7687.95014.28120.00078.59774.37259.277306.0001621.7542140
197.6628.37412.88820.00077.83076.13160.031306.0001720.0082240
207.4528.75118.73320.00076.21677.72851.940306.0001768.1162280
217.06315.00019.14520.00073.145101.60749.988303.0551712.2052240
2211.99115.00019.67620.000101.75098.08247.637298.5341573.9982120
2311.93115.00020.36820.000100.69194.26944.320292.3561318.3641850
2415.00015.00013.13320.000107.02080.95059.005284.4001058.6251590
Table 2. Comparisons of simulation results for test system 1, case 1. Employing quantum-inspired evolutionary algorithm (QEA); quantum-inspired evolutionary algorithm (WDA); small population-based particle swarm optimization (SPSO); real-coded genetic algorithm (RCGA); real-coded quantum-inspired evolutionary algorithm (RQEA); modified differential evolution (MDE); differential real-coded quantum-inspired evolutionary algorithm (DRQEA); hybrid chemical reaction optimization-differential evolution (HCRO-DE); modified adaptive particle swarm optimization (MAPSO); real coded genetic algorithm and artificial fish swarm algorithm (RCGA-AFSA); teaching learning-based optimization (TLBO); SPPSO; self-organizing hierarchical particle swarm optimization technique with time-varying acceleration coefficients (SOHPSO_TVAC); particle swarm optimization (PSO); improved differential evolution (IDE); fuzzy adaptive particle swarm optimization (FAPSO); dynamic neighborhood learning based particle swarm optimization (DNLPSO) and; modified dynamic neighborhood learning based particle swarm optimization (MDNLPSO).
Table 2. Comparisons of simulation results for test system 1, case 1. Employing quantum-inspired evolutionary algorithm (QEA); quantum-inspired evolutionary algorithm (WDA); small population-based particle swarm optimization (SPSO); real-coded genetic algorithm (RCGA); real-coded quantum-inspired evolutionary algorithm (RQEA); modified differential evolution (MDE); differential real-coded quantum-inspired evolutionary algorithm (DRQEA); hybrid chemical reaction optimization-differential evolution (HCRO-DE); modified adaptive particle swarm optimization (MAPSO); real coded genetic algorithm and artificial fish swarm algorithm (RCGA-AFSA); teaching learning-based optimization (TLBO); SPPSO; self-organizing hierarchical particle swarm optimization technique with time-varying acceleration coefficients (SOHPSO_TVAC); particle swarm optimization (PSO); improved differential evolution (IDE); fuzzy adaptive particle swarm optimization (FAPSO); dynamic neighborhood learning based particle swarm optimization (DNLPSO) and; modified dynamic neighborhood learning based particle swarm optimization (MDNLPSO).
Optimization MethodMin. Cost ($)Max. Cost ($)Ave. Cost ($)
QEA [25]926,538.29930,484.13928,426.95
WDA [32]925,618.5-928,219.8
SPSO [33]925,308.86923,083.48926,185.32
RCGA [34]923,966.285924,108.731924,232.072
RQEA [25]923,634.53926,957.39924,992.46
DE [25]923,234.56928,395.84925,157.28
MDE [33]922,556.38923,201.13923,813.99
DRQEA [25]922,526.73925,871.51923,419.37
DNLPSO [15]922,498923,580922,837
HCRO [35]922,444.79922,513.62922,936.17
MAPSO [36]922,421.66923,508922,544
RCGA-AFSA [34]922,339.625922,346.323922,362.532
TLBO [37]922,373.39922,873.81922,462.24
SPPSO [33]922,336.31922,362.532923,083.48
SOHPSO_TVAC [38]922,018.24--
PSO [39]921,920--
IDE [40]917,237.7917,277.8917,250.1
FAPSO [39]914,660--
Proposed method884,733.965--
Table 3. Hourly plant discharges, power outputs and total thermal generation (test system 1, case 2).
Table 3. Hourly plant discharges, power outputs and total thermal generation (test system 1, case 2).
HourHydro Plant Discharges (104 m3)Hydro Power Output (MW)Thermal
Generation (MW)
Total
Generation (MW)
Plant 1Plant 2Plant 3Plant 4Plant 1Plant 2Plant 3Plant 4
15.2126.00010.73013.08053.15645.31655.550198.5391017.4391370
25.5556.48710.97616.21157.19949.93555.337210.0901017.4391390
35.1486.00010.25215.32754.32947.50854.789185.936943.5191360
45.0236.00010.00019.57153.61349.30255.220188.347832.6391290
55.0526.00017.57120.00054.12651.02452.771299.440943.5191290
65.1506.00010.00019.61555.15052.13355.526303.6721168.5611410
75.6056.57412.83520.00059.40756.70459.328306.0001586.1971650
85.2576.19612.06512.50056.68953.75759.195244.1621756.6372000
95.6136.66112.69420.00060.24257.42759.693306.0001762.6992240
1013.47613.30613.13719.485104.42390.25159.762302.8661719.6772320
1111.5896.02110.10419.73998.01551.32056.792304.1961793.5972230
1211.7526.12211.91619.73998.73253.67859.620304.3731740.2602310
139.2546.17021.62620.00086.63355.01842.089306.0001756.6372230
148.2746.13727.94319.92180.93255.7251.827304.8801675.4802200
155.0006.00021.95419.47655.06856.15240.489302.8101608.7972130
165.3206.54421.60820.00058.13561.47241.271300.3251645.7572070
175.2348.59514.15719.97857.18475.32260.241291.4961645.7572130
187.8216.64014.81020.00079.15162.07759.593293.4221719.6772140
198.4249.19910.29819.98783.52077.59257.383301.8281793.5972240
2010.47411.90427.51619.60796.00088.8076.038145301.5951674.8582280
2114.45914.87110.00019.888109.20594.84855.758305.3311645.7572240
228.38114.99827.29619.99382.34391.1350.747300.0171387.0382120
238.42514.99626.77819.39982.20386.8132.930291.0161096.5801850
2415.0006.72113.13319.190107.02047.55759.005279.8371058.6251590
Table 4. Comparisons of simulation results for test system1, case 2.
Table 4. Comparisons of simulation results for test system1, case 2.
Optimization MethodMin. Cost ($)
QEA [25]930,647.96
DE [25]928,662.84
RCGA-AFSA [34]927,899.872
RQEA [25]926,068.33
DRQEA [25]925,485.21
CRQEA [25]925,403.1
RCCRO [41]925,214.20
ACDE [42]924,661.53
MAPSO [36]924,636
TLBO [37]924,550.78
RCGA [34]923,966.285
RQEA [25]923,634.53
DE [25]923,234.56
MDE [33]922,556.38
DRQEA [25]922,526.73
HCRO-DE [35]922,444.79
MAPSO [36]922,421.66
MDNLPSO [15]923,961
IDE [40]923,016.29
TLBO [37]922,373.39
RCGA-AFSA [34]922,339.625
SPPSO [33]922,336.31
SOHPSO_TVAC [38]922,018.24
PSO [39]921,920
Improved DE [40]917,250.1
IDE [40]917,237.7
FAPSO [39]914,660.00
Proposed method901,191.9735
Table 5. Optimal discharges and power output for test system 2 case 1.
Table 5. Optimal discharges and power output for test system 2 case 1.
HourHydro Plant Discharges (104 m3)Hydro Power Output (MW)Thermal Power Output (MW)
Plant 1Plant 2Plant 3Plant 4Plant 1Plant 2Plant 3Plant 4Plant 1Plant 2Plant 3
15.9406.00011.91915.33559.10345.31754.084224.158102.673124.908139.758
26.6997.76614.02420.00065.98958.07252.592236.005102.673124.908139.760
36.8646.00216.35813.09967.79646.73348.618169.512102.673124.908139.760
45.0006.00020.25112.10253.00048.54534.952146.162102.673124.908139.760
55.0006.00019.3547.48653.36150.29836.816162.185102.673124.908139.760
65.5626.00018.08120.00058.43551.42641.026281.773102.673124.908139.760
78.7177.23510.0509.52181.43460.38148.532189.953175.000165.181229.519
86.7936.00010.00012.00268.40251.29549.484226.483175.000209.816229.520
98.2786.98410.00017.85379.13158.61750.361287.556175.000209.816229.520
109.7216.90110.00015.16687.81958.64051.683267.523175.000209.816229.520
119.9807.65110.00016.17489.46864.62652.759278.812175.000209.816229.519
1211.5269.68212.52119.99196.73976.82256.159305.945175.000209.816229.519
138.8308.82718.36319.43183.31871.35248.797292.197175.000209.816229.519
148.3376.89417.75012.23580.72359.11051.359224.472175.000209.816229.519
157.7916.29719.37413.63977.68256.13747.225236.017175.000209.812208.126
165.4747.83610.00017.34559.43167.73156.316262.186175.000209.816229.519
178.5557.62510.00012.24383.82166.50357.468227.928174.999209.816229.465
189.6289.14410.00017.59390.45275.04458.138282.031175.000209.816229.519
198.8018.76410.00012.80085.27371.21358.453240.726175.000209.816229.520
205.0008.95121.30514.37955.09971.22946.410262.927175.000209.816229.520
217.16712.82525.4038.68073.74187.12722.093197.616175.000124.903229.520
2210.38113.61126.99119.65294.07486.7838.525303.277102.673124.908139.760
239.95615.00026.18719.73191.46986.38911.133293.668102.673124.908139.760
247.9797.65613.14314.00179.29653.38859.005240.969102.673124.908139.760
Table 6. Comparison of obtained optimal costs for test system 2 case 1.
Table 6. Comparison of obtained optimal costs for test system 2 case 1.
Optimization MethodMin. CostMean. CostMax. Cost
SA [44]45,466--
DE [32]44,526.11--
CABC [43]43,362.68--
ADE [43]43,222.41--
RCGA [34]42,886.35243,261.91243,032.334
DE [25]42 801.04--
SPPSO [33]42,740.2343,622.1444,346.97
RQEA [25]42 715.69--
PSO [45]42,474.00--
CDE [42]42,452.99--
CSA [46]42,440.574--
TLBO [47]42,386.1342,407.2342,441.36
TLBO [33]42,385.8842,407.2342,441.36
IQPSO [48]42,359.00--
GSO [49]42,316.3942,339.3542,379.18
QTLBO [47]42,187.4942,193.4642,202.75
QOGSO [49]42,120.0242,130.1542,145.37
IDE [40]41,856.5--
ACDE [42]41,593.48--
RCCRO [41]41,497.8541,498.2141,502.36
DRQEA [25]41,435.76--
ACABC [43]41,274.42--
Proposed method41,101.738--
Table 7. Hourly plant discharges, power outputs and total thermal generation for test system 2 case 2.
Table 7. Hourly plant discharges, power outputs and total thermal generation for test system 2 case 2.
HourHydro Plant Discharge, 104 m3Hydro Plant Generation (MW)Thermal Plant Generation (MW)Loss MWTotal Generation, MW
Plant 1Plant 2Plant 3Plant 4Plant 1Plant 2Plant 3Plant 4Plant 1Plant 2Plant 3
16.2247.06412.9916.79761.23252.27756.939130.234102.511124.322229.5187.033757.033
27.2808.23713.10719.95669.96159.94255.806237.968102.673124.908139.75911.017791.017
35.0926.00012.8185.32753.35445.53355.918188.300102.674122.365139.7607.903707.903
45.0006.00014.57218.11153.11947.34955.882184.83995.53980.854139.7597.341657.341
55.0006.00011.5456.00053.47049.14956.573149.761102.657124.908139.7606.278676.278
65.0006.00015.31416.64853.63250.30956.596280.372102.674124.908145.34513.836813.836
75.3936.00016.44517.90757.42050.87754.608291.657157.940124.907229.51916.928966.928
85.9816.00010.00018.06462.81450.87754.961292.422175.000162.192229.52017.7861027.786
98.6356.21316.13319.70782.53352.94955.354304.242175.000209.815229.52019.4131109.413
108.2776.15617.25818.83380.53353.53551.892298.668175.000209.816229.51918.9631098.963
119.9907.66810.11118.23391.04165.15454.262294.077175.000209.816229.51918.8701118.870
1214.07211.84913.39920.000105.69086.77257.662306.000175.000209.815229.52020.4581170.458
139.0236.01320.35613.23485.28352.69042.348246.964175.000294.724229.52016.5291126.529
147.2247.20818.02818.42173.58261.93850.867295.082175.000162.205229.51918.1931048.193
158.0317.65116.11018.68279.96365.73257.051295.698175.000124.908229.51917.8721027.872
168.3148.53919.34720.00082.21471.78650.242294.907175.000209.816194.41418.3791078.379
179.2389.66125.08016.98688.31477.55818.529268.360175.000209.815229.52017.0971067.097
1810.24511.05410.68920.00093.95382.46557.043291.376175.000209.816229.51919.1721139.172
198.8499.96210.00015.11185.62974.48256.903254.959175.000209.815229.51916.3081086.308
208.5469.66821.65115.28183.46171.18541.508257.413175.000208.174229.51916.2601066.260
217.0299.97823.58820.00072.39971.58430.674294.843102.674124.908229.52016.601926.601
226.07711.22726.32415.16264.68176.31011.250265.100102.673124.907229.52014.442874.442
2311.22413.40624.42220.00097.74581.82123.040295.500102.673124.908139.76015.448865.448
2415.00015.00013.1339.594107.02080.95059.005194.843102.674124.907139.7579.157809.157
Table 8. Comparisons of simulation results for test system 2 case 2.
Table 8. Comparisons of simulation results for test system 2 case 2.
Optimization MethodMin. Cost
QEA [25]44,686.31
ABC [43]43,362.00
QOGSO [49]43,560.35
DE [32]42,801.04
SPPSO [50]42,740.23
RQEA [25]42,715.69
DNLPSO [15]42,645
PSO [51]42,474.00
CSA [44]42,440.574
TLBO [33]42,386.13
SA-MOCDE [37]42,038.00
GSA [52]42,032.35
QOTLBO [33]42,187.49
MOCA-PSO [53]42,001.00
SHPSO-TAC [51]41,983.00
IDE [40]41,856.5
RCGA-AFSA [34]41,818.42
QABDEVT [16]41,762.00
ACDE [54]41,593.48
Proposed method41,350.5574
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