This multiobjective optimization problem can be optimized by converting into a ... The online action for generating units is constrained by Ramp rate bounds.
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2017 International Conference on Alternative Energy in Developing Countries and Emerging Economies 2017 AEDCEE, 25‐26 May 2017, Bangkok, Thailand
15th International Symposiumheuristic on District Heating and Cooling A review onTheapplication of various techniques to combined economic and emission dispatch in a modern power system scenario Assessing the feasibility of using the heat demand-outdoor 11 Tapas Kumar Panigrahi , Arun Ku. Sahoo , Aurobindo Behera 11 forecast temperature function for a** 11long-term district heat demand
11 Department
Engineering, Institute Bhubaneswar Department of of Electrical Electrical Engineering, International International Institute of of Information Technology Technology Bhubaneswar (IIIT, (IIIT,c BBSR), BBSR), Bhubaneswar, Bhubaneswar, India a,b,c a a Information b c India
I. Andrić
a
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre
IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
Abstract Abstract
In current current scenario scenario electricity electricity market market is is like like aa commodity commodity market, market, the the electric electric energy energy must must be be economic economic as as well well as as reliable. reliable. For For In the economic economic operation operation in in power power system system economic economic load load dispatch dispatch plays plays an an important important factor. factor. As As most most of of the the generation generation is is based based on on the fossil fuel, it emits the oxides of Nitrogen (NO ), Sulphur (SO ) and Carbon (CO ). To improve the economic operation and x x 2 fossil fuel, it emits the oxides of Nitrogen (NOx), Sulphur (SOx) and Carbon (CO2). To improve the economic operation and Abstract minimize minimize the the adverse adverse environmental environmental impact impact of of the the fossil fossil fuel fuel based based systems, systems, combined combined economic economic and and emission emission dispatch dispatch has has been been suggested by many authors. This paper provides review of various authors around the world suggesting different technique suggested by many authors. This paper provides review of various authors around the world suggesting different technique District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the applied to operation and aa multi-objective optimization problem. applied to the the combined economic operationsector. and environmental environmental impact as multi-objective optimization problem. Various greenhouse gascombined emissionseconomic from the building These systems impact require as high investments which are returned throughVarious the heat optimization techniques have been proposed to apply such as Genetic algorithm (GA), Evolutionary Programming (EP), particle optimization techniques have climate been proposed to apply as Genetic algorithm (GA), Evolutionary (EP),decrease, particle sales. Due to the changed conditions and such building renovation policies, heat demand inProgramming the future could swarm optimization (PSO), Artificial swarm optimization (PSO), Artificial Bee Colony Colony Algorithm Algorithm (ABC), (ABC), Differential Differential evolution evolution (DE), (DE), Backtracking Backtracking Search Search prolonging the investment return period. Bee Optimization (BSO), Flower Pollination Algorithm (FPA) and etc. Optimization (BSO), Flower Pollination Algorithm (FPA) and etc. The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand forecast. The districtPublished of Alvalade, locatedLtd. in Lisbon (Portugal), was used as a case study. The district is consisted of 665 © 2017 Authors. by ©buildings 2017 The Thethat Authors, Published byElsevier Elsevierperiod Ltd. and typology. Three weather scenarios (low, medium, high) and three district © 2017 The Authors, Published by Elsevier Ltd. vary in both construction Peer-review under responsibility of the scientific committee of the 2017 International Conference on Alternative Energy in Peer-review under responsibility responsibility of the the Organizing Organizing Committee of ofdeep). 2017 To AEDCEE. Peer-review under of Committee 2017 AEDCEE. renovation scenarios wereEmerging developed (shallow, intermediate, estimate the error, obtained heat demand values were D eveloping Countries and Economies. compared with results from a dynamic heat demand model, previously developed and validated by the authors. Keywords: Combined Economic and Emission Dispatch (CEED); Multi Objective Optimization (MOO), Economic Load Dispatch Keywords: Combined Dispatch Multi (MOO), Economic Load Dispatch The results showed Economic that when and onlyEmission weather change is (CEED); considered, theObjective margin ofOptimization error could be acceptable for some applications (ELD), Valve Point Loading (VPL), Transmission Losses (TL), Prohinited Operating Zones (POZs), Ramp Rate Limit(RRL). (ELD), Valve Point Loading (VPL), Transmission Losses (TL), Prohinited Operating Zones (POZs), Ramp Rate Limit(RRL). (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the 1. Introduction 1.decrease Introduction in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the Economic operation electricity is in the Optimum operation of is Economic operation ofvalues electricity is highly highly essential in modify the recent recent years. Optimum operation of electricity electricity is almost almost coupled scenarios). Theof suggested couldessential be used to the years. function parameters for the scenarios considered, and depending upon the generation cost of the fossil fuel based system. Considering the employment of controlling the depending upon the generation cost of the fossil fuel based system. Considering the employment of controlling the improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and ** Corresponding Corresponding author. author. Tel.: Tel.: +919438484723; +919438484723; Cooling. E-mail address: arunsahoo89@ gmail.com E-mail address: arunsahoo89@ gmail.com Keywords: Heat demand; Forecast; Climate change 1876-6102 © 1876-6102 © 2017 2017 The The Authors. Authors. Published Published by by Elsevier Elsevier Ltd. Ltd. Peer-review Peer-review under under responsibility responsibility of of the the Organizing Organizing Committee Committee of of 2017 2017 AEDCEE. AEDCEE.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 2017 International Conference on Alternative Energy in Developing Countries and Emerging Economies. 10.1016/j.egypro.2017.10.216
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pollution acts, optimum generation cost is not a main worry but the generation companies have to emphasis to minimize the impact of pollutants for the environment. Fossil fuel based generation affects the environment by producing pollutants gases as oxides of various materials like Nitrogen (NOx), Sulphur (SOx) and Carbon (COx) [1]. Optimum dispatch and emission control are different to each other and are in balance association. The approach to this problem is difficult by conventional methods for single objective function. A possible methodology to get solution of this category of problematic approach, applying orthodox optimization technique by converting into a biobjective problem from a function of unit objective by providing relative values of weight. To control environmental impact and optimum generation a multiple objective optimization is considered as combined economic and emission dispatch (CEED) [2]. ELD concerns with sharing the demanded load between the generators in the economic method by considering the system constraints. Emission dispatch is to control emission of fossil fuel based pollutants from the environment. CEED problem can be complex if nonconvex and nonsmooth cost functions of fuel are applied to the model of considering all the constraint. Over a decade various optimization techniques are being applied to the CEED multi objective problem such as GA, EP, Swarm Intelligence and some Meta-heuristic technique. This paper presents the review of various optimization methods applied to the CEED problem by considering various constraints. 2. Problem Formulation CEED problem provide the solution for the issue to optimize the cost of the fuel and to minimize the emission dispatch. The functions of emission and cost, both are independent, which causes the CEED to be bi-objective problem. This multiobjective optimization problem can be optimized by converting into a unity objective function. 2.1. Objectives 2.1.1.
Cost for Economic Dispatch & Emission Dispatch
Each and every generator has its own characteristics. Fuel is taken as an input and power consumption is taken as output. The cost functions of fuel for the generator j can be signified as a function of generation of real power. [2] Equation 1 & 2 is showing the cost function not considering and considering valve point loading respectively.
min = F
∑ F ( P=) ∑ a P
m= in F
∑
j j =j 1 =j 1
F j= ( Pj )
∑a
=j 1=j 1
j
j
j
2
+ b j Pj + c j
P j 2 + b j P j + c j + e j × sin
(1)
( f (P j
m in j
− Pj )
)
(2)
Here aj, bj, cj, ej & fj = cost co-efficient of the given cost function, F is the overall cost & (FjPj) be the function of cost of the generator for Pj output. The pollutants such as SOx, NOx and CO2 produced by coal based generator can be exhibited distinctly. The total emission of these contaminants can be stated as the sum of a function of exponential and quadratic. [5]
Ed=
N
∑ [α j =1
j
+ β j P j + γ j P j2 + η j e x p (δ j P j )]
(3)
2.2. Constraint 2.2.1.
Real power balance constraints
In every period of schedule, the entire output power should balance the demanded load forecast and the losses in the transmission line. Where Pi is scheduled generation, PD is the demanded load & PL is the transmission line loss and PL can be expressed in terms of B co-efficient [3].
Tapas Kumar Panigrahi et al. / Energy Procedia 138 (2017) 458–463
460 N
∑P − P i
i =1
= PL 2.2.2.
D
N
− PL = 0 N
∑∑PB
=i 1=j 1
i
ij
Pj +
(4) N
∑B
=i 1
0i
Pi + B 0 0
Constraints for capacity of generator.
(5)
The output real power of the generator maintained within the upper and lower limit [4].
Pm in < P < Pm a x 2.2.3.
(6)
Generator ramp rate limit and
The online action for generating units is constrained by Ramp rate bounds. These bounds have an impact on the operational decisions. The current scheduling may disturb the future scheduling as generation increases due to ramp rate bounds. [18] m ax ( Pgmj in , Pg0j − D R j ) ≤ Pg j ≤ m in ( Pgmj ax + U R j )
(7) Here,Pgj0= Active power of previous operation of jth unit in MW. DRj & URj are the respective down and up ramp rate limit. 2.2.4.
Prohibited operating zones (POZs)
Generator committed units having some sections where the process to operate is either not required or unmanageable due to some physical restrictions for the matters concerning uncertainty. All the regions producing discontinuities in the curve of the cost and the unit must process for a definite specified limit. [18]
Pgjmin ≤ Pgj ≤ Pl gj ,1 ; PgjU, J −1 ≤ Pgj ≤ Pl gj , j ; PgjU, ni ≤ Pgj ≤ Pgjmax
(8)
For conversion of multiobjective optimization to the unity objective optimization problem price penalty factor is used. By the given equation
= Pf j
F ( Pjmax ) a j Pj2max + b j Pj max + c j = E ( Pjmax ) α j Pj2max + β j Pj max + γ j
(9)
3. Various Technique applied to the CEED problem Over the recent eras, stochastic based meta-heuristic optimizations methods have been given more emphasize to find global solution. Numerous techniques, based on swarm and evolutionary have been used to get solution for multiobjective CEED problems. Some of the techniques are described here to show the effectiveness for the given problem. Song et al. have proposed [1] a Fuzzy logic controlled genetic algorithm (FCGA) for this problem, here an enhanced GA is applied for an optimum result by considering controllers of two fuzzy values and the crossover possibility and mutation rate adapted and adjusted during the optimization course. T Yalcinoz et al. [2] have proposed modified GA depending on arithmetic crossover operator by considering of arithmetic mean of two new individuals to get an optimum cost with minimum emission. M. A. Abido [3] has approached Niched Pareto genetic algorithm (NPGA) and it has no limitation for the amount
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of optimized objectives and also uses a fuzzy based mechanism to reduce the early convergence and to extract the optimize result over the trade-off curve and also a clustering method is employed to deliver as a demonstrative and adaptable Pareto optimal set. M. A. Abido [4] has proposed Strength Pareto Evolutionary Algorithm (SPEA), it takes a diversity-preserving mechanism to converge faster and explore the problems. A hierarchical clustering algorithm is applied to find a representative for controllable Pareto-optimal set and a fuzzy set theory to excerpt the optimum result. This approach is superior and conserves the variety solutions above the trade-off front. D. Gong et al. [5] have analysed about a hybrid technique combining PSO and differential evolution (DE). PSO is modified for discovering the total search space and a local differential evolution is used to developing the subspace with sparse solutions. Time variant acceleration co-efficient is applied with some extra integration such as crowding distance for getting a better global leader. M. A. Abido [6] has also proposed a multiobjective particle swarm optimization (MOPSO). PSO is extended by configuring new classifications for local and global best and a nondominated local and global set of solution is considered instead of a single set of global and local best. This process provides an optimum result for cost and emission compared to multi-objective stochastic search technique (MOSST). L.Wang et al. [7] have approached Fuzzified Multiobjective PSO (FMOPSO), a fuzzified process is applied to find the global best value. The gbest value found is an area instead of a point. All the point appears in the area can give an optimum solution and a fuzzified formula is used to study about the behaviour of the point and tournament selection is used to get the gbest value. This method converges very fast as comparison to GA. Aydin et al. [8] have approached an Artificial Bee Colony with Dynamic Population Size (ABCDP) algorithm and it is similar to Incremental Artificial Bee Colony algorithm with Local Search (IABC-LS) with less parameter is to be adjusted. Both the method was applied to get the optimum cost and economic emission. Guvenc [9] has proposed genetic algorithm based on similarity crossover. Here, the offspring are produced by using similarity dimension between parent chromosomes. M. Basu [10] has applied multiobjective Differential Evolution algorithm (MODE) individually for both cost of fuel and minimum emission to find the trade-off point on the surface. It was applied for various populations and the result is better as compared with DE. A. Bhattacharya et al. [11] have combined Differential Evolution (DE) & Biogeography Based Optimization (BBO), BBO technique gives a better result depending on the geographical dissemination of various biological species considering global solutions by mutation & migration. BBO adopts a hybrid migration operator and combining with the operator of DE to perform (Mutation, Crossover & Selection) to find the effective search space. An improved multi-objective binary differential evolution (IMBDE) algorithm was applied by Y. Di et al. [12], the constraint is handled by marginal exploration operator. This method gives an optimum solution for both emission and fuel cost than BBO & NSGA-II. It has a better global searching ability. K. Bhattacharjee et al. [13] have used Backtracking search algorithm (BSA), uses a different strategy for mutation by using the targeted individual and variable in and for new type of crossover strategy to create new test individuals in every generation for a better global search. Comparing with the other method, BSA provides an optimum solution for different generator unit system. Y. Lu et al. [14] have applied enhanced multi-objective differential evolution (EMODE) optimization method for the problem. To get better converge rate a local random search (LRS) operator is incorporated with the DE. Here a new technique is integrated to the method for modification from use of price penalty factor. This method provides a faster convergence and optimum solution than MODE. A multi-objective chaotic ant swarm optimization (MOCASO) was applied by J. Cai et. al. [15].This method is used by combining the chaotic performance of distinct ant with the intellectual association of colony of ant to optimize the multi objective problem. This is a cost & emission effective technique as compared to FMOPSO. B. Hadji et al. [16] have proposed Dance bee colony algorithm with dynamic step size, it was inspired by the behaviour of ant at the time of foraging. The method is to search the global best by the evaluation process and it gives a better solution as compared to other method. J.S. Dhillon et al. [17] have analysed the binary successive approximation-based evolutionary search by considering the committed generation unit. After trade-off point observe, fuzzy mechanism assistances for the judge to decide the finest value. The weights are regulated for a better global search. This method gives a better result and a faster convergence as compared to the other technique for 3, 13 and 40 IEEE generator unit system. R. Zhang et al. [18] had applied an enhanced multi-objective cultural algorithm (EMOCA) by relating cultural algorithm (CA) structure with PSO to process with the evolution of total
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search space. Two knowledge structures of recognition space in CA are processed again to conferee to the structures of the problem. This method has a better performance over MODE and NSGA-II with a nearly faster convergence. A.Y. Abdelaziz et al. [19] had processed a new algorithm as Flower Pollination Algorithm (FPA) by the process of reproduction of flower from the pollination process. It has better performance for the CEED & ELD problem with a very faster convergence rate. B. Jeddi et al. [20] have proposed a modified harmony search algorithm (MHSA) considering various constraints. An inventing technique established on wavelet mutation applied collectively with a original memory attention pattern centered on the roulette wheel process are planned to improve the precision and convergence speed as compared to conventional Harmony Search Algorithm (HSA). R T. F. Ah King et al. [21] have analysed Non-Dominated Sorting Genetic Algorithm (NSGA-II) to get an optimum solution for the problem. Here a non-dominated set of solutions was happened and a decision maker will find the better solution from the fuzzy based membership function. The optimum solution varies with different POZs. K.K. Mandal et al. [22] have addressed self-organizing hierarchical particle swarm optimization (SOHPSO) considering various constraints for better convergence. A time varying acceleration co-efficient was applied to maintain a steadiness between the cognitive and social component throughout the entire procedure for search. Table 1. Comparison of result between heuristics techniques applied to CEED for cost & emission for different test unit. Techniques FLGA[1] NGA[2] NPGA[3] MOEA[4] NIMBUS[5] HPSODE[6] MOPSO[7] FMOPSO[8] ABCDP[9] GASC[10] MODE[11] HDEBBO[12] IMBDE[13] BSO[14] EMODE[15] CASOP[16] DBC[17] BSA-EV[18] EMOCA[19] FPA[20]
Brief Results With Constraints Without VPL & Considering Transmission Loss for a 6 unit system and cost is varying for different demand. Only considering the loss the cost for a 3 unit & 10 unit system respectively as 8333.3($/h) & 3.7006(M$/h) and the emission for NOx & SOx was calculated Separately. With VPL & TL for 6 unit System Cost of fuel is 607.77($/h) & Emission of 0.1948(ton/h) Optimum cost & emission for 6 unit system with TL,VPL & Security Constraint is 607.80($/h) & 0.19422 (ton/h) Optimum cost & emission only considering loss for 3 unit system is 8335.069($/h), for is SOx 9.010785(ton/h) & for NOx 0.09854765 (ton/h). Without transmission loss for a 6 unit system loss the optimum cost & emission is 600.115($/h), 0.194203(ton/h) respectively. Considering loss cost is 606.0073($/h) & emission is 0.194179 (ton/h) With loss and security constraint it gives a cost of optimum cost for 6 unit system as 607.78($/h) & Emission of .1942(ton/h) Considering loss & VPL for 14 unit system the optimum cost is 13094.9 ($/h) & Emission of 38.0501(ton/h) The optimum cost & emission for 6 unit system with TL & VPN is 605.425(($/h) & 0.1942 (ton/h) respectively. For 40 generator system cost is 121412.82 ($/h) and emission is 176682.25 (ton/h) Considering loss it gives optimum cost for 6 unit & 11 generator unit system. Applied with loss and VPL the cost & emission for 6 unit system for 1200MW demand is 64843($/h) & 1286(lb.), for 10 unit system with demand 2000MW is 113480 ($/h) 7 4124.90(lb), for 40 unit 10,500 MW demand 125790 ($/h) & 211190(lb) The optimum cost & emission of NOx & SOx with loss & VPL is optimized as 8344.58319 ($/h), 0.095923 & 8.965927 (ton/h) for 3 unit. 49,615.05371($/h) 759.8670138(ton/h) Considering only loss for 6 unit system the optimum cost is 38388.0363($/h) & emission is 523.4624(ton/h) Using VPL & Loss the better cost and emission for all the test system for 6 unit system is 63,976($/h) & 1360.1(Ib/h) for 10 unit system 113480($/h) & 4124.9(Ib/h),for 40 unit system 121415.653($/h) & 356424.497(Ib/h) With Loss & VPL the optimum cost and emission is different for different load demand as 500MW, 700MW, 900MW. Using the transmission loss the minimum cost is 47187.41($/h) and emission is 696.92(kg/h) for 6 generator units. Minimum cost is 605.3456($/h) &emission is 0.19420(t/h) for a 6 unit , 121417($/h) & 176682.9(t/h) for 40 unit system With ramp rate limit & POZs and considering the VPL & TL for 40 unit system for different demand of having optimum cost and minimum emission also for 3 unit and 13 unit system. Considering loss with POZs for 6 unit system & 10 unit system gives an optimum cost of 64,790.37($/h) & 113,444.85($/h) respectively and emission of 1288.97(lb) & 4113.98(lb). Considering the valve point loading for different test unit as 3 unit, 6 Unit, 10 Unit & 40 unit system gives the optimum cost & emission for different demand
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4. Conclusion CEED is one of the significant aspects of modern power system dynamics. A study of various proposed optimization techniques implemented for the minimizing cost and emission are presented through the paper. The CEED problem is optimized by different evolutionary algorithm considering the different type of constraint in the respective methods. The optimization methods are applied to the different type of test system based on IEEE to verify the superiority with others technique. Different convergence rate is achieved by different technique are discussed in this paper. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22]
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