Economic Dispatch Research Papers - Academia.edu (original) (raw)

Grid-connected microgrids with storage systems are reliable configurations for critical loads which can not tolerate interruptions of energy supply. In such cases, some of the energy resources should be scheduled in order to coordinate... more

Grid-connected microgrids with storage systems are reliable configurations for critical loads which can not tolerate interruptions of energy supply. In such cases, some of the energy resources should be scheduled in order to coordinate optimally the power generation according to a defined objective function. This paper defines a generationside power scheduling and economic dispatch of a gridconnected microgrid that supplies a fixed load and then, the scheduling is enhanced by including penalties in order to increase the use of the renewable energy sources and guarantee a high state of charge in the storage system for the next day. Linear models are proposed for the scheduling which are implemented in GAMS. The microgrid model is obtained deploying MATLAB/Simulink toolbox and then downloaded into dSPACE 1006 platform based on real-time simulation to test the economic dispatch. A compromise between cost and use of renewable energy is achieved.

The concept of the energy management system, developed in this work, is to determine the optimal combination of energy from several generation sources and to schedule their commitment, while optimizing the cost of purchased energy, power... more

The concept of the energy management system, developed in this work, is to determine the optimal combination of energy from several generation sources and to schedule their commitment, while optimizing the cost of purchased energy, power losses and voltage drops. In order to achieve these objectives, the non-dominated sorting genetic algorithm II (NSGA-II) was modified and applied to an IEEE 33-bus test network containing 10 photovoltaic power plants and 4 battery energy storage systems placed at optimal points in the network. To evaluate the system performance, the resolution was performed under several test conditions. Optimal Pareto solutions were classified using three decision-making methods, namely analytic hierarchy process (AHP), technique for order preference by similarity to ideal solution (TOPSIS) and entropy-TOPSIS. The simulation results obtained by NSGA-II and classified using entropy-TOPSIS showed a significant and considerable reduction in terms of purchased energy cost, power losses and voltage drops while successfully meeting all constraints. In addition, the diversity of the results proved once again the robustness and effectiveness of the algorithm. A graphical interface was also developed to display all the decisions made by the algorithm, and all other information such as the states of power systems, voltage profiles, alarms, and history.

Reliable and inexpensive electricity provision is one of the significant research objectives since decades. Various Economic Dispatch (ED) methods have been developed in order to address the challenge of continuous and sustainable... more

Reliable and inexpensive electricity provision is one of the significant research objectives since decades. Various Economic Dispatch (ED) methods have been developed in order to address the challenge of continuous and sustainable electricity provision at optimized cost. Rapid escalation of fuel prices, depletion of fossil fuel reserves and environmental concerns have compelled us to incorporate the Renewable Energy (RE) resources in the energy mix. This paper presents Combined Emission Economic Dispatch (CEED) models developed for a system consisting of multiple Photo Voltaic (PV) plants and thermal units. Based on the nature of decision variables, our proposed model is essentially a Mixed Integer Optimization Problem (MIOP). Particle Swarm Optimization (PSO) is used to solve the optimization problem for a scenario involving six conventional and thirteen PV plants. Two test cases, Combined Static Emission Economic Dispatch (SCEED) and Combined Dynamic Emission Economic Dispatch (DCEED), have been considered. SCEED is performed for full solar radiation level as well as for reduced radiation level due to clouds effect. Simulation results have proved the effectiveness of the proposed model.

This paper examines peat power production in Ireland under the three pillars of energy policy -security, competitiveness and environment. Peat contributes to energy securityas an indigenous fuel, it reduces dependency on imports. During a... more

This paper examines peat power production in Ireland under the three pillars of energy policy -security, competitiveness and environment. Peat contributes to energy securityas an indigenous fuel, it reduces dependency on imports. During a period of low capacity margins, the operation of the peat plants is useful from a system security perspective. Peat generation is being financially supported by consumers through an electricity levy. The fuel also has high carbon intensity. It is not politically viable to consider peat on equal economic criteria to other plant types because of history and location. This paper reviews electricity generation through combustion of peat in Ireland, and quantifies the costs of supporting peat utilising economic dispatch tools, finding the subsidy is not insignificant from a cost or carbon perspective. It shows that while peat is beneficial for one pillar of energy policy (security), the current usage of peat is not optimal from a competitiveness or environmental perspective. By switching from the current 'must-run' mode of operation for peat to the 'dispatched' mode used for the other generation, significant societal savings (in the range of €21m per annum) can be achieved, as well as reducing system emissions by approximately 5% per year.

In this paper, a computer package called CPFLOW which is a comprehensive tool for tracing power system steady-state stationary behavior due to parameter variations is presented. The variations include general bus real and/or reactive... more

In this paper, a computer package called CPFLOW which is a comprehensive tool for tracing power system steady-state stationary behavior due to parameter variations is presented. The variations include general bus real and/or reactive loads, area real and/or reactive loads, or system-wide real and/or reactive loads, and real generation at P-V buses (e.g. determined by economic dispatch or participation factor). The main advantages of CPFLOW over repetitive power flow calculations are its computational speed and reliability as well as its wide applicability. A detailed description of the implementation regarding the predictor, corrector, step-size control and parameterizations employed in CPFLOW is presented. CPFLOW has comprehensive modeling capability and can handle power systems u p to 12000 buses. For an illustrative purpose, CPFLOW is applied to a 3500-bus power system with a comprehensive set of operational limits and controls.

This paper presents an evolutionary based technique for solving the multi-objective based economic environmental dispatch by considering the stochastic behavior of renewable energy resources (RERs). The power system considered in this... more

This paper presents an evolutionary based technique for solving the multi-objective based economic environmental dispatch by considering the stochastic behavior of renewable energy resources (RERs). The power system considered in this paper consists of wind and solar photovoltaic (PV) generators along with conventional thermal energy generators. The RERs are environmentally friendlier, but their intermittent nature affects the system operation. Therefore, the system operator should be aware of these operating conditions and schedule the power output from these resources accordingly. In this paper, the proposed EED problem is solved by considering the nonlinear characteristics of thermal generators, such as ramp rate, valve point loading (VPL), and prohibited operating zones (POZs) effects. The stochastic nature of RERs is handled by the probability distribution analysis. The aim of proposed optimization problem is to minimize operating cost and emission levels by satisfying various operational constraints. In this paper, the single objective optimization problems are solved by using particle swarm optimization (PSO) algorithm, and the multi-objective optimization problem is solved by using the multi-objective PSO algorithm. The feasibility of proposed approach is demonstrated on six generator power system.

Previous work (Elec. Power Sys. Res. 56 (2000) 225) has demonstrated the capability of decision trees (DT) in solving optimisation problems for the economic dispatch (ED) problem including environmental constraints. This paper explains a... more

Previous work (Elec. Power Sys. Res. 56 (2000) 225) has demonstrated the capability of decision trees (DT) in solving optimisation problems for the economic dispatch (ED) problem including environmental constraints. This paper explains a new improvement in the DT technique by adding fuzzy logic (FL) to the unit limits and load (FLDT). By doing so, the numerical convergence of the overall technique improves. Furthermore, the generating cost obtained via FLDT is lower than that obtained with the classical formulation due to the fuzzyfying of the generating unit limits. Also, including the uncertainty of the load as a fuzzy load, new results are obtained that account not only for minimum cost but also for uncertainty. A 10 unit Chilean test system is used to validate and highlight the performance of this proposition. #

The aim of this work is to solve the unit commitment (UC) problem in power systems by calculating minimum production cost for the power generation and finding the best distribution of the generation among the units (units scheduling)... more

The aim of this work is to solve the unit commitment (UC) problem in power systems by calculating minimum production cost for the power generation and finding the best distribution of the generation among the units (units scheduling) using binary grey wolf optimizer based on particle swarm optimization (BGWOPSO) algorithm. The minimum production cost calculating is based on using the quadratic programming method and represents the global solution that must be arriving by the BGWOPSO algorithm then appearing units status (on or off). The suggested method was applied on "39 bus IEEE test systems", the simulation results show the effectiveness of the suggested method over other algorithms in terms of minimizing of production cost and suggesting excellent scheduling of units.

This paper presents a selective survey of papers, books, and reports that articulate recent trends of Security Constrained Economic Dispatch (SCED) of integrated renewable energy systems (IRES). The time-period under consideration is 2008... more

This paper presents a selective survey of papers, books, and reports that articulate recent trends of Security Constrained Economic Dispatch (SCED) of integrated renewable energy systems (IRES). The time-period under consideration is 2008 through 2020. This is done to provide an up-to-date review of the recent, major advancements in the SCED, and state-of-theart since 2008. This helps identify further challenges needed in adopting smarter grids, and indicate ways to address these challenges. The study was conducted in three areas of interest that are relevant for articulating the recent trends of SCED. These areas are (i) SCED of power systems with IRES, (ii) SCED mathematical formulation and solution methods, and (iii) SCED challenges. The review results and research directions deduce that the state of the art research is not enough and needs special attention on following the path of artificial intelligence-based optimization.

This paper presents a new and accurate method for estimating the parameters of thermal power plants fuel cost function. Proper and precise estimation of these parameters is very important for optimal economical operations of power systems... more

This paper presents a new and accurate method for estimating the parameters of thermal power plants fuel cost function. Proper and precise estimation of these parameters is very important for optimal economical operations of power systems as they directly impact the economic dispatch calculations. The objective function to be minimized in the economic dispatch is usually the summation of fuel cost functions corresponding to generating units. The input–output characteristics of thermal power plants are affected by many factors such as the ambient operating temperature and aging of generating units. Thus, periodical estimation of power plant characteristics is very crucial to improve the overall operational and economical practices. The higher the accuracy of the estimated coefficients, the more accurate the results obtained from the economic dispatch calculations. Different models that describe the input–output curve of thermal generating units are considered. The traditional estimation problem is viewed and formulated as an optimization one. The goal is to minimize the total estimation error. A particle swarm optimization algorithm is employed to minimize the error associated with the estimated parameters. Three different study cases are considered in this work to test the performance of the method. Results obtained are compared to those computed by least error square method. Comparison results are in favor of particle swarm optimization algorithm in all study cases considered.

Güç sistemlerinde enerji üretim maliyeti minimum olması gereken en önemli faktördür. Rekabetçi piyasa koşulları, enerji verimliliği, fosil yakıtların tükeniyor ve giderek pahalılaşıyor olması ve çevresel kaygılar bu faktörü olabildiğince... more

Güç sistemlerinde enerji üretim maliyeti minimum olması gereken
en önemli faktördür. Rekabetçi piyasa koşulları, enerji verimliliği,
fosil yakıtların tükeniyor ve giderek pahalılaşıyor olması ve çevresel
kaygılar bu faktörü olabildiğince minimum yapma gereksiniminin
en önemli nedenleridir. Enerji maliyetini azaltabilmenin en iyi
yollarından birisi ise, yenilenebilir enerji kaynaklarına enerji
portföyünde hatırı sayılır bir yer ayırmaktan geçmektedir. Rüzgâr
enerjisi de düzensiz bir kaynak olmasına karşın teknolojisindeki
gelişmeler onu yenilenebilir enerji kaynakları arasında gözde
yapmıştır. Bu çalışmada Parçacık Sürü Optimizasyonu algoritması
ile ekonomik yük dağıtımı probleminin çözülmesi 6 jeneratörlü ve
40 jeneratörlü test sistemlerinde ayrı ayrı uygulanmıştır. İletim hattı
kayıplarının ve rüzgâr enerji santrallerinin de dâhil edildiği durumlar
da incelenmiş ve rüzgâr enerji santrallerinin güç sistemlerine olan
etkisi gösterilmiştir.

ABSTRACT This paper applies the sensitivity factor method to the reactive power/voltage dispatch problem and combines it with the fast Newton-Raphson economic dispatch to solve the optimal power flow problem. The advantage of this method... more

ABSTRACT This paper applies the sensitivity factor method to the reactive power/voltage dispatch problem and combines it with the fast Newton-Raphson economic dispatch to solve the optimal power flow problem. The advantage of this method is that it is fast and reliable. Firstly, the real power generalized generation shift distribution (GGSD) factors are used in economic dispatch to find the real power generation for every unit so that the fuel cost is minimum. During the process of solving the economic dispatch problem, the voltage magnitudes of load buses in a power system may be changed and may exceed the ranges of secure values. Therefore, reactive power sensitivity factors of bus voltage magnitudes are used to regulate the voltage magnitudes of load buses to the secure ranges. The process is repeated to find the optimal generation with all voltages staying within the secure ranges. Results show that the algorithm converges very fast

This paper presents a survey of papers and reports which address various aspects of economic dispatch. The time period considered is 1977-88. This is done to avoid any repetition of previous studies which were published prior to 1977.... more

This paper presents a survey of papers and reports which address various aspects of economic dispatch. The time period considered is 1977-88. This is done to avoid any repetition of previous studies which were published prior to 1977. Four very important and related areas of economic dispatch are identified and papers published in the general area of economic dispatch are classified into these. These areas are:-(i) Optimal power flow, (ii) economic dispatch in relation to AGC, (iii) dynamic dispatch and (iv) economic dispatch with non-conventional generation sources.

Micro-grids (MGs) are introduced as a solution for distributed energy resource (DER) units and energy storage systems (ESSs) to participate in providing the required electricity demand of controllable and non-controllable loads. In this... more

Micro-grids (MGs) are introduced as a solution for distributed energy resource (DER) units and energy storage systems (ESSs) to participate in providing the required electricity demand of controllable and non-controllable loads. In this paper, the authors study the short-term scheduling of grid-connected industrial heat and power MG which contains a fuel cell (FC) unit, combined heat and power (CHP) generation units, power-only unit, boiler, battery storage system, and heat buffer tank. The paper is aimed to solve the multi-objective MG dispatch problem containing cost and emission minimization with the considerations of demand response program and uncertainties. A probabilistic framework based on a scenario method, which is considered for load demand and price signals, is employed to overcome the uncertainties in the optimal energy management of the MG. In order to reduce operational cost, time-of-use rates of demand response programs have been modeled, and the effects of such programs on the load profile have been discussed. To solve the multi-objective optimization problem, the e-constraint method is used and a fuzzy satisfying approach has been employed to select the best compromise solution. Three cases are studied in this research to confirm the performance of the proposed method: islanded mode, grid-connected mode, and the impact of time of the use-demand response program on MG scheduling.

In order to coordinate the scheduling problem between an isolated microgrid (IMG) and electric vehicle battery swapping stations (BSSs) in multi-stakeholder scenarios, a new bi-level optimal scheduling model is proposed for promoting the... more

In order to coordinate the scheduling problem between an isolated microgrid (IMG) and electric vehicle battery swapping stations (BSSs) in multi-stakeholder scenarios, a new bi-level optimal scheduling model is proposed for promoting the participation of BSSs in regulating the IMG economic operation. In this model, the upper-level sub-problem is formulated to minimize the IMG net costs, while the lower-level aims to maximize the profits of the BSS under real-time pricing environments determined by demand responses in the upper-level decision. To solve the model, a hybrid algorithm, called JAYA-BBA, is put forward by combining a real/integer-coded JAYA algorithm and the branch and bound algorithm (BBA), in which the JAYA and BBA are respectively employed to address the upper- and lower- level sub-problems, and the bi-level model is eventually solved through alternate iterations between the two levels. The simulation results on a microgrid test system verify the effectiveness and superiority of the presented approach.

The economic viability of producing baseload wind energy was explored using a cost-optimization model to simulate two competing systems: wind energy supplemented by simple-and combined cycle natural gas turbines (''wind+gas''), and wind... more

The economic viability of producing baseload wind energy was explored using a cost-optimization model to simulate two competing systems: wind energy supplemented by simple-and combined cycle natural gas turbines (''wind+gas''), and wind energy supplemented by compressed air energy storage (''wind+CAES''). Pure combined cycle natural gas turbines (''gas'') were used as a proxy for conventional baseload generation. Long-distance electric transmission was integral to the analysis. Given the future uncertainty in both natural gas price and greenhouse gas (GHG) emissions price, we introduced an effective fuel price, p NGeff , being the sum of the real natural gas price and the GHG price. Under the assumption of p NGeff ¼ 5/GJ(lowerheatingvalue),650W/m2windresource,750kmtransmissionline,andafixed905/GJ (lower heating value), 650 W/m 2 wind resource, 750 km transmission line, and a fixed 90% capacity factor, wind+CAES was the most expensive system at b6.0/kWh, and did not break even with the next most expensive wind+gas system until p NGeff ¼ 5/GJ(lowerheatingvalue),650W/m2windresource,750kmtransmissionline,andafixed909.0/GJ. However, under real market conditions, the system with the least dispatch cost (short-run marginal cost) is dispatched first, attaining the highest capacity factor and diminishing the capacity factors of competitors, raising their total cost. We estimate that the wind+CAES system, with a greenhouse gas (GHG) emission rate that is onefourth of that for natural gas combined cycle plants and about one-tenth of that for pulverized coal plants, has the lowest dispatch cost of the alternatives considered (lower even than for coal power plants) above a GHG emissions price of $35/tC equiv. , with good prospects for realizing a higher capacity factor and a lower total cost of energy than all the competing technologies over a wide range of effective fuel costs. This ability to compete in economic dispatch greatly boosts the market penetration potential of wind energy and suggests a substantial growth opportunity for natural gas in providing baseload power via wind+CAES, even at high natural gas prices. r

The distribution of natural gas is carried out by means of long ducts and intermediate compression stations to compensate the pressure drops due to friction. The natural gas compressors are usually driven by an electric motor or a gas... more

The distribution of natural gas is carried out by means of long ducts and intermediate compression stations to compensate the pressure drops due to friction. The natural gas compressors are usually driven by an electric motor or a gas turbine system, offering possibilities for energy management, one of these consisting in generating energy for use in-plant or to commercialize as independent power producer. It can be done by matching the natural gas demand, at the minimum pressure allowed in the reception point, and the storage capacity of the feed duct with the maximum compressor capacity, for storing the natural gas at the maximum permitted pressure. This allows the gas turbine to drive an electric generator during the time in which the decreasing pressure in duct is above the minimum acceptable by the sink unit. In this paper, a line-pack management analysis is done for an existing compression station considering its actual demand curve for determining the economic feasibility of maintaining the gas turbine system driver generating electricity in a peak and off-peak tariff structure. The potential of cost reduction from the point of view of energy resources (natural gas and electric costs) is also analyzed.

Economic emission dispatch (EED) problems are one of the most crucial problems in power systems. Growing energy demand, limitation of natural resources and global warming make this topic into the center of discussion and research. This... more

Economic emission dispatch (EED) problems are one of the most crucial problems in power systems. Growing energy demand, limitation of natural resources and global warming make this topic into the center of discussion and research. This paper reviews the use of quantum computational intelligence (QCI) in solving economic emission dispatch problems. QCI techniques like quantum genetic algorithm (QGA) and quantum particle swarm optimization (QPSO) algorithm are discussed here. This paper will encourage the researcher to use more QCI based algorithm to get better optimal result for solving EED problems.

– Flower Pollination Algorithm (FPA) is a new biologically inspired meta-heuristic optimization technique based the pollination process of flowers. FPA mimics the flower pollination characteristics in order to survival by the fittest.... more

– Flower Pollination Algorithm (FPA) is a new biologically inspired meta-heuristic optimization technique based the pollination process of flowers. FPA mimics the flower pollination characteristics in order to survival by the fittest. This paper presents implementation of FPA optimization in solving Combined Economic Emission Dispatch (CEED) problems in power system. CEED actually is a bi-objective problem where the objective of economic dispatch (ED) and emission dispatch (EMD) are combined into a single function by using price penalty factor. Hence, CEED is used to minimize the total generation cost by minimizing fuel cost and emission concurrently and at the same time determines the optimum power generation. In this paper, the valve point loading effect problem in power system also will be considered. The proposed algorithm are tested on four different test systems which are: 6-generating unit and 11-generating unit without valve point effect with no transmission loss, 10-generating unit with having valve point effect and transmission loss, and lastly 40-generating unit with having valve point effect without transmission loss. The results of these four different test cases were compared with the optimization techniques reported in recent literature in order to observe the effectiveness of FPA. Result shows FPA able to perform better than other algorithms by having minimum fuel cost and emission.

The combined heat and power economic dispatch (CHPED) problem enhances the conversion efficiency from fossil fuels into electricity, eventually reducing the green house gas. However, the optimization formulation for a popular bench-mark... more

The combined heat and power economic dispatch (CHPED) problem enhances the conversion efficiency from fossil fuels into electricity, eventually reducing the green house gas. However, the optimization formulation for a popular bench-mark example has not properly considered the constraint of heatpower feasible operating region so far. Thus, this study proposes a novel technique to consider the non-convex heat-power feasible region in the CHPED problem more accurately. This study divides the non-convex operating region into two convex operating sub-regions by introducing two binary variables indicating the searching region. In addition, more accurate results and better demand constraints are proposed to more fairly compare the results for this bench-mark problem in the future.

A new decomposition method is presented that includes the network through ac modeling within the hydrothermal scheduling optimization process including the losses. In short-term hydrothermal scheduling, the transmission network is... more

A new decomposition method is presented that includes the network through ac modeling within the hydrothermal scheduling optimization process including the losses. In short-term hydrothermal scheduling, the transmission network is typically modeled with dc power flow techniques. Such modeling, however, can lead to impractical solutions when it is verified with ac power flow. Another proposal considers in thermal systems the ac network modeling but not the optimization of losses. The approach presented here addresses issues such as congestion management and control of service quality that often arise in large and weakly meshed networks-the typical pattern of power systems in Latin America. Generalized Benders decomposition and traditional, well-known optimization techniques are used to solve this problem. The master problem stage defines the generation levels by regarding the inter-temporal constraints, whereas the subproblem stage determines both the active and the reactive economical dispatches for each time interval of the load curve. It meets the electrical constraints through a modified ac optimal power flow (OPF). Another important contribution is the inclusion of accelerating techniques aimed at reducing the number of iterations and CPU time. The methodology was proven in a real system and test systems. Results are discussed in this paper.

This paper presents Reinforcement Learning (RL) approaches to Economic Dispatch problem. In this paper, formulation of Economic Dispatch as a multi stage decision making problem is carried out, then two variants of RL algorithms are... more

This paper presents Reinforcement Learning (RL) approaches to Economic Dispatch problem. In this paper, formulation of Economic Dispatch as a multi stage decision making problem is carried out, then two variants of RL algorithms are presented. A third algorithm which takes into consideration the transmission losses is also explained. Efficiency and flexibility of the proposed algorithms are demonstrated through different representative systems: a three generator system with given generation cost table, IEEE 30 bus system ...

Güç sistemlerinde enerji üretim maliyeti minimum olması gereken en önemli faktördür. Rekabetçi piyasa koşulları, enerji verimliliği, fosil yakıtların tükeniyor ve giderek pahalılaşıyor olması ile çevresel kaygılar bu faktörü olabildiğince... more

Güç sistemlerinde enerji üretim maliyeti minimum olması gereken en önemli faktördür. Rekabetçi piyasa koşulları, enerji verimliliği, fosil yakıtların tükeniyor ve giderek pahalılaşıyor olması ile çevresel kaygılar bu faktörü olabildiğince minimum yapma gereksiniminin en önemli nedenleridir. Bu nedenle eldeki santrallerin minimum maliyet ve maksimum fayda anlayışı içerisinde talep edilen yük miktarını karşılaması gerekmektedir. Bu çalışmada da bu anlayışı sağlayabilmek için verilen 40 jeneratörlü bir sistemde diferansiyel gelişim optimizasyonu ile ekonomik yük dağıtımı yapılmıştır.

This paper work present one of the latest meta heuristic optimization approaches named whale optimization algorithm as a new algorithm developed to solve the economic dispatch problem. The execution of the utilized algorithm is analyzed... more

This paper work present one of the latest meta heuristic optimization approaches named whale optimization algorithm as a new algorithm developed to solve the economic dispatch problem. The execution of the utilized algorithm is analyzed using standard test system of IEEE 30 bus system. The proposed algorithm delivered optimum or near optimum solutions. Fuel cost and emission costs are considered together to get better result for economic dispatch. The analysis shows good convergence property for WOA and provides better results in comparison with PSO. The achieved results in this study using the above-mentioned algorithm have been compared with obtained results using other intelligent methods such as particle swarm Optimization. The overall performance of this algorithm collates with early proven optimization methodology, Particle Swarm Optimization (PSO). The minimum cost for the generation of units is obtained for the standard bus system.

This paper develops an efficient and clear insight view about the application of various PSO algorithms to the economic load dispatch problem with emission restrictions as the constraint. Solution acceleration techniques in the algorithm... more

This paper develops an efficient and clear insight view about the application of various PSO algorithms to the economic load dispatch problem with emission restrictions as the constraint. Solution acceleration techniques in the algorithm which enhance the speed and robustness of the algorithm are developed. The power and usefulness of the algorithm is demonstrated through its application to a test system.

This paper addresses the effect of the wind power units into the classical Environment/Economic Dispatch (EED) model which called hereafter as Wind/Environment/Economic Dispatch (WEED) problem. The optimal dispatch between thermal and... more

This paper addresses the effect of the wind power units into the classical Environment/Economic Dispatch (EED) model which called hereafter as Wind/Environment/Economic Dispatch (WEED) problem. The optimal dispatch between thermal and wind units so that minimized the total generating costs are considered as multi objective model. Normally, the nature of the wind energy as a renewable energy sources has uncertainty in generation. Therefore, in this paper, use a practical model known as 2 m-point to estimate the uncertainty of wind power. To solve the WEED problem, this paper proposed a new meta-heuristic optimization algorithm that uses online learning mechanism. Honey Bee Mating Optimization (HBMO), a moderately new population-based intelligence algorithm, shows fine performance on optimization problems. Unfortunately, it is usually convergence to local optima. Therefore, in the proposed Online Learning HBMO (OLHBMO), two neural networks are trained when reached to the predefined threshold by current and previous position of solutions and their fitness values. Moreover, Chaotic Local Search (CLS) operator is use to develop the local search ability and a new data sharing model determine the set of non-dominated optimal solutions and the set of non-dominated solutions to kept in the external memory. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) as a decision-making technique is employed to find the best solution from the set of Pareto solutions. The proposed model has been individually examined and applied on the IEEE 30-bus 6-unit, the IEEE 118-bus 14-unit, and 40-unit with valve point effect test systems. The robustness and effectiveness of this algorithm is shows by these test systems compared to other available algorithms.

This paper presents an improved heuristic algorithm for solving combined heat and power economic dispatch (CHPED) problem in large scale power systems, by employing improved group search optimization (IGSO) method. The basic deficiency of... more

This paper presents an improved heuristic algorithm for solving combined heat and power economic dispatch
(CHPED) problem in large scale power systems, by employing improved group search optimization
(IGSO) method. The basic deficiency of the original GSO algorithm is the fact that, it gives a near optimal
solution rather than an optimal one in a limited runtime period. In this paper, some modifications have
been applied to the original GSO in order to improve its searching ability. In this work, a sinusoidal term
is employed to consider the valve-point effects and Kron’s formula is used in order to consider the transmission
losses. Moreover, prohibited operating zones of power-only units are considered. The effectiveness
and accuracy of the proposed method for solving the non-linear and non-convex problems is
validated by carrying out simulation studies on sample benchmark test cases and three large scale power
systems. Numerical results indicate that proposed method has the better performance in comparison
with original GSO and some recently published papers.

The following criteria and assumptions are applied: i) all HV nodal voltages to be within the range 0.90-1 .IO p.u. under intact and contingent situations; ii) for simplicity, assume that independence between random variables and no more... more

The following criteria and assumptions are applied: i) all HV nodal voltages to be within the range 0.90-1 .IO p.u. under intact and contingent situations; ii) for simplicity, assume that independence between random variables and no more than one generator or circuit outage happens simultaneously in the studied period.

The objective of the Economic Dispatch Problems (EDPs) of electric power generation is to schedule the committed generating units outputs so as to meet the required load demand at minimum operating cost while satisfying all units and... more

The objective of the Economic Dispatch Problems (EDPs) of electric power generation is to schedule the committed generating units outputs so as to meet the required load demand at minimum operating cost while satisfying all units and system equality and inequality constraints. Recently, global optimization approaches inspired by swarm intelligence and evolutionary computation approaches have proven to be a potential alternative for the optimization of difficult EDPs. Particle swarm optimization (PSO) is a population-based stochastic algorithm driven by the simulation of a social psychological metaphor instead of the survival of the fittest individual. Inspired by the swarm intelligence and probabilities theories, this work presents the use of combining of PSO, Gaussian probability distribution functions and/or chaotic sequences. In this context, this paper proposes improved PSO approaches for solving EDPs that takes into account nonlinear generator features such as ramp-rate limits and prohibited operating zones in the power system operation. The PSO and its variants are validated for two test systems consisting of 15 and 20 thermal generation units. The proposed combined method outperforms other modern metaheuristic optimization techniques reported in the recent literature in solving for the two constrained EDPs case studies.

This paper proposes a control scheme which minimizes the operating cost of a grid connected micro-grid supplemented by battery energy storage system (BESS). What distinguishes approach presented here from conventional strategies is that... more

This paper proposes a control scheme which minimizes the operating cost of a grid connected micro-grid supplemented by battery energy storage system (BESS). What distinguishes approach presented here from conventional strategies is that not only the price of electricity is considered in the formulation of the total operating cost but an additional item that takes into account inevitable battery degradation. The speed of degradation depends on battery technology and its mission profile and this effect demands for eventual replacement of the stack. Therefore it can be mapped in additional operating cost. By modeling battery degradation as a function of depth of discharge (DoD) and discharge rate and translating incremental loss of capacity in each cycle into associated cost, objective function has been defined and solved using GAMS. Simulation results are presented to verify the proposed approach.

In recent decays, soft computing techniques such as genetic algorithm (GA) and artificial neural networks (ANN) are increasingly employed in a diverse area of applications. As optimization tools, genetic algorithm and Hopfield net are... more

In recent decays, soft computing techniques such as genetic algorithm (GA) and artificial neural networks (ANN) are increasingly employed in a diverse area of applications. As optimization tools, genetic algorithm and Hopfield net are successfully applied in constrained optimization problems. In this study suitability of Multi-Layered Perceptron (MLP) for a constrained optimization problem; namely economic dispatch problem, is investigated and a comparison is carried out between GA, Hopfield and MLP. As a case study, the performance of the MLP techniques in economic dispatch problem is compared to the results given in literature. For economic dispatch problem, the MLP approach is compared with an improved Hopfield NN approach (IHN) [1], a fuzzy logic controlled genetic algorithm (FLCGA) [2], an advance engineered-conditioning genetic approach (AECGA) [3] and an advance Hopfield NN approach (AHNN) . Results show that although MLP neural network is inherently not suitable for optimization task when compared to GA and Hopfield, the performance of MLP neural network may be highly improved in order to obtain comparable results to that of GA and Hopfield neural networks.

The power system operation needs to ensure that the electrical power is generated with minimum costs. Thus, the economic operation of power system requires that the total demand equals the produced power. The objective is to minimize the... more

The power system operation needs to ensure that the electrical power is generated with minimum costs. Thus, the economic operation of power system requires that the total demand equals the produced power. The objective is to minimize the total generation cost for the system. The economic dispatch (ED) is one of the important problems of power system operation and control. In this paper, the economic dispatch is realized with artificial intelligence algorithms for the determination of optimum dispatch solution, including satisfied all the constraints. The developed algorithms for economic dispatch problem are tested on a Romanian regional network and on IEEE 30-bus system.

A multi-objective particle swarm optimization (MOPSO) approach is presented for generating Pareto-optimal solutions for reservoir operation problems. This method is developed by integrating Pareto dominance principles into particle swarm... more

A multi-objective particle swarm optimization (MOPSO) approach is presented for generating Pareto-optimal solutions for reservoir operation problems. This method is developed by integrating Pareto dominance principles into particle swarm optimization (PSO) algorithm. In addition, a variable size external repository and an efficient elitist-mutation (EM) operator are introduced. The proposed EM-MOPSO approach is first tested for few test problems taken from the literature and evaluated with standard performance measures. It is found that the EM-MOPSO yields efficient solutions in terms of giving a wide spread of solutions with good convergence to true Pareto optimal solutions. On achieving good results for test cases, the approach was applied to a case study of multi-objective reservoir operation problem, namely the Bhadra reservoir system in India. The solutions of EM-MOPSOs yield a trade-off curve/surface, identifying a set of alternatives that define optimal solutions to the problem. Finally, to facilitate easy implementation for the reservoir operator, a simple but effective decision-making approach was presented. The results obtained show that the proposed approach is a viable alternative to solve multi-objective water resources and hydrology problems. MOPSO FOR GENERATING OPTIMAL TRADE-OFFS IN RESERVOIR OPERATION 2909 to understand how many solutions of A are covered by B and vice versa.

An enhanced particle swarm optimization algorithm (PSO) is presented in this work to solve the non-convex OPF problem that has both discrete and continuous optimization variables. The objective functions considered are the conventional... more

An enhanced particle swarm optimization algorithm (PSO) is presented in this work to solve the non-convex OPF problem that has both discrete and continuous optimization variables. The objective functions considered are the conventional quadratic function and the augmented quadratic function. The latter model presents non-differentiable and non-convex regions that challenge most gradient-based optimization algorithms. The optimization variables to be optimized are the generator real power outputs and voltage magnitudes, discrete transformer tap settings, and discrete reactive power injections due to capacitor banks. The set of equality constraints taken into account are the power flow equations while the inequality ones are the limits of the real and reactive power of the generators, voltage magnitude at each bus, transformer tap settings, and capacitor banks reactive power injections. The proposed algorithm combines PSO with Newton-Raphson algorithm to minimize the fuel cost function. The IEEE 30-bus system with six generating units is used to test the proposed algorithm. Several cases were investigated to test and validate the consistency of detecting optimal or near optimal solution for each objective. Results are compared to solutions obtained using sequential quadratic programming and Genetic Algorithms.

Most of electrical power utilities in the world are required to ensure that electrical energy requirement from the customer is served smoothly in accordance to the respective policy of the country. Despite serving the power demands of the... more

Most of electrical power utilities in the world are required to ensure that electrical energy requirement from the customer is served smoothly in accordance to the respective policy of the country. Despite serving the power demands of the country, the power utility has also to ensure that the electrical power is generated within minimal cost. Thus, the total demand must be appropriately shared among the generating units with an objective to minimize the total generation cost for the system in order to satisfy the economic operation of the system. Economic dispatch is a procedure to determine the electrical power to be generated by the committed generating units in a power system so that the total generation cost of the system is minimized, while satisfying the load demand simultaneously. This paper presents the economic power dispatch problems solved using Ant Colony Optimization (ACO) technique. ACO is a meta-heuristic approach for solving hard combinatorial optimization problems. In this study, the proposed technique was tested using the standard IEEE 26-Bus RTS and the results revealed that the proposed technique has the merit in achieving optimal solution for addressing the problems. Comparative studies with other optimization technique namely the artificial immune system (AIS) were also conducted in order to highlight the strength of the proposed technique.

Grid-connected microgrids with storage systems are reliable configurations for critical loads which can not tolerate interruptions of energy supply. In such cases, some of the energy resources should be scheduled in order to coordinate... more

Grid-connected microgrids with storage systems are reliable configurations for critical loads which can not tolerate interruptions of energy supply. In such cases, some of the energy resources should be scheduled in order to coordinate optimally the power generation according to a defined objective function. This paper defines a generationside power scheduling and economic dispatch of a gridconnected microgrid that supplies a fixed load and then, the scheduling is enhanced by including penalties in order to increase the use of the renewable energy sources and guarantee a high state of charge in the storage system for the next day. Linear models are proposed for the scheduling which are implemented in GAMS. The microgrid model is obtained deploying MATLAB/Simulink toolbox and then downloaded into dSPACE 1006 platform based on real-time simulation to test the economic dispatch. A compromise between cost and use of renewable energy is achieved.

This paper addresses the effect of the wind power units into the classical Environment/Economic Dispatch (EED) model which called hereafter as Wind/Environment/Economic Dispatch (WEED) problem. The optimal dispatch between thermal and... more

This paper addresses the effect of the wind power units into the classical Environment/Economic Dispatch (EED) model which called hereafter as Wind/Environment/Economic Dispatch (WEED) problem. The optimal dispatch between thermal and wind units so that minimized the total generating costs are considered as multi objective model. Normally, the nature of the wind energy as a renewable energy sources has uncertainty in generation. Therefore, in this paper, use a practical model known as 2 m-point to estimate the uncertainty of wind power. To solve the WEED problem, this paper proposed a new meta-heuristic optimization algorithm that uses online learning mechanism. Honey Bee Mating Optimization (HBMO), a moderately new population-based intelligence algorithm, shows fine performance on optimization problems. Unfortunately, it is usually convergence to local optima. Therefore, in the proposed Online Learning HBMO (OLHBMO), two neural networks are trained when reached to the predefined threshold by current and previous position of solutions and their fitness values. Moreover, Chaotic Local Search (CLS) operator is use to develop the local search ability and a new data sharing model determine the set of non-dominated optimal solutions and the set of non-dominated solutions to kept in the external memory. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) as a decision-making technique is employed to find the best solution from the set of Pareto solutions. The proposed model has been individually examined and applied on the IEEE 30-bus 6-unit, the IEEE 118-bus 14-unit, and 40-unit with valve point effect test systems. The robustness and effectiveness of this algorithm is shows by these test systems compared to other available algorithms.

Abstrack In this paper, a new multiobjective evohrtionary algorithm for EnvironmentaUEconomic power Dispatch (EED) optimimtion problem is presented. The EED problem is formulated as a nonlinear constrained multiobjective optimization... more

Abstrack In this paper, a new multiobjective evohrtionary algorithm for EnvironmentaUEconomic power Dispatch (EED) optimimtion problem is presented. The EED problem is formulated as a nonlinear constrained multiobjective optimization problem with both equatity and inequality constraints. A new Nondominated Sorting Genetic Atgorithm (NSGA) based approach is proposed to handle the problem as a true multiobjective optimization problem with competing and non-commensurable objectives. The proposed approach employs a diversity-preservingtechnique to overcome the premature convergence and search bias problems and produce a wetl-distributed Pareto-optimal set of nondominated solutions. A hierarchical clustering technique is also imposed to provide the decision maker with a representative and manageable Paretooptirnal set. Several optimization runs of the proposed approach are carried out on a standard IEEE test system. The results demonstrate the capabilities of the proposed NSGA based approach to generate the true Pareto-optimal set of nondominated solutions of the multiobjective EED problem in one single run. Simulation results with the proposed approach have been compared to those reported in the literature.The comparison shows the superiority of the proposed NSGA based approach and confirms its potential to solve the multiobjectiveEED problem.

Genetic algorithm is a search and optimisation method simulating natural selection and genetics. It is the most popular and widely used of all evolutionary algorithms. Genetic algorithms, in one form or another, have been applied to... more

Genetic algorithm is a search and optimisation method simulating natural selection and genetics. It is the most popular and widely used of all evolutionary algorithms. Genetic algorithms, in one form or another, have been applied to several power system problems. This paper gives a brief introduction to genetic algorithms and reviews some of their most important applications in the field

The Economic Dispatch Problem (EDP) is one of the important optimization problem in a power system. Traditionally, in EDP, the cost function for each generator has been approximately represented by a single quadratic function. The main... more

The Economic Dispatch Problem (EDP) is one of the important optimization problem in a power system. Traditionally, in EDP, the cost function for each generator has been approximately represented by a single quadratic function. The main aim of EDP is to minimize the total cost of generating real power while satisfying the equality constraints of power balance and the inequality generator capacity constraints. In this paper a New Economic Dispatch Problem Formulation (NEDPF) has been proposed to solve EDP. This new formulation is based on the reduction of the number of variables (number of generators) and elimination of the equality and inequality constraints, thus the transformation of the constrained non linear programming problem to an unconstrained one. The new unconstrained objective function, is minimized by Hooke-Jeeves' method. The NEDPF was tested for different cases (2, 3 and 6 generator units) and the results are judged satisfactory.

Optimal power flow (OPF) is one of the main functions of power generation operation and control. It determines the optimal setting of generating units. It is therefore of great importance to solve this problem as quickly and accurately as... more

Optimal power flow (OPF) is one of the main functions of power generation operation and control. It determines the optimal setting of generating units. It is therefore of great importance to solve this problem as quickly and accurately as possible. This paper presents the solution of the OPF using genetic algorithm technique. This paper proposes a new methodology for solving OPF. This methodology is divided into two parts. The first part employs the genetic algorithm (GA) to obtain a feasible solution subject to desired load convergence, while the other part employs GA to obtain the optimal solution. The main goal of this paper is to verify the viability of using genetic algorithm to solve the OPF problem simultaneously composed by the load flow and the economic dispatch problem. Six buses system are used to highlight the goodness of this solution technique.

A meta-heuristic algorithm called harmony search (HS), mimicking the improvisation process of music players, has been recently developed. The HS algorithm has been successful in several optimization problems. The HS algorithm does not... more

A meta-heuristic algorithm called harmony search (HS), mimicking the improvisation process of music players, has been recently developed. The HS algorithm has been successful in several optimization problems. The HS algorithm does not require derivative information and uses stochastic random search instead of a gradient search. In addition, the HS algorithm is simple in concept, few in parameters, and easy in implementation. This paper presents an improved harmony search (IHS) algorithm based on exponential distribution for solving economic dispatch problems. A 13-unit test system with incremental fuel cost function taking into account the valve-point loading effects is used to illustrate the effectiveness of the proposed IHS method. Numerical results show that the IHS method has good convergence property. Furthermore, the generation costs of the IHS method are lower than those of the classical HS and other optimization algorithms reported in recent literature.

In this paper we describe an efficient approach for solving the economic dispatch problem using Genetic Algorithms (GAs). We recommend the use of adaptive probabilities crossover and mutation to realize the twin goals of maintaining... more

In this paper we describe an efficient approach for solving the economic dispatch problem using Genetic Algorithms (GAs). We recommend the use of adaptive probabilities crossover and mutation to realize the twin goals of maintaining diversity in the population and sustaining the convergence capacity of the GA. In the Adaptive Genetic Algorithm (AGA), the probabilities of crossover and mutation, pc

This paper presents the basis for a new approach to solving the problem of inter-utility power transactions in deregulated electricity markets. The problem is formulated as an optimization approach with a nonlinear objective function and... more

This paper presents the basis for a new approach to solving the problem of inter-utility power transactions in deregulated electricity markets. The problem is formulated as an optimization approach with a nonlinear objective function and linear constraints. An example system is discussed, presenting the operation state for inter-utility transactions based on the equal-lambda criterion. The same example is presented for the proposed method and the results are discussed.