ORIGINAL_ARTICLE
Potentiometric of the Renewable Hybrid System for Electrification of Gorgor Station
In this paper, an optimal design of the renewable combustion plant has been investigated with the aim of ensuring the required load on the Gorgor station. The purpose of this study is to minimize the cost of the proposed hybrid unit during the period of operation of the designed system simultaneously. Information on the intensity of solar radiation and the intensity of wind blowing in the area are taken and applied in the simulation of the system. The intended target function includes the cost of investment, replacement cost and maintenance cost. After the design phase, the main objective is to check the economic benefits of the project's utilization from the grid and compare it with the renewable electricity system, as well as to calculate the initial investment return in renewable electricity. First, the initial cost of consuming electricity from this project is calculated using a renewable electricity system, and then the cost of project is determined using the national grid. Further, by calculating the annual current cost of each of these combinations, the investment return in each mode is obtained. Various options for the use of renewable energies are surveyed separately and in combination. The technical-economic analysis is done on each of these options and ultimately the best one is presented.
https://joape.uma.ac.ir/article_775_eff76b97ad8071b790a7f79223d13a2a.pdf
2020-02-01
1
14
10.22098/joape.2019.5476.1410
Gorgor station
Electrical energy audit
optimization
Design
Economic analysis
H.
Shayeghi
hshayeghi@gmail.com
1
Technical Engineering Department, University of Mohaghegh Ardabili, Ardabil, Iran
LEAD_AUTHOR
Y.
Hashemi
yashar_hshm@yahoo.com
2
Technical Engineering Department, University of Mohaghegh Ardabili, Ardabil, Iran
AUTHOR
[1] R. Afshan and J. Salehi, “Optimal scheduling of battery energy storage system in distribution network considering uncertainties using hybrid monte carlo-genetic approach,” J. Oper. Autom. Power Eng., vol. 6, pp. 1-12, 2018.
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[2] M. Majidi and S. Nojavan, “Optimal sizing of energy storage system in a renewable-based microgrid under flexible demand side management considering reliability and uncertainties,” J. Oper. Autom. Power Eng., vol. 5, pp. 205-214, 2017.
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[3] M. Khalid, M. AlMuhaini, R. P. Aguilera, and A. V. Savkin, “Method for planning a wind–solar–battery hybrid power plant with optimal generation-demand matching,” IET Renew. Power Gener., vol. 12, pp. 1800-1806, 2018.
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[4] R. Atia and N. Yamada, “Sizing and analysis of renewable energy and battery systems in residential microgrids,” IEEE Trans. Smart Grid, vol. 7, pp. 1204-1213, 2016.
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[5] J. P. Castillo, C. D. Mafiolis, E. C. Escobar, A. G. Barrientos, and R. V. Segura, “Design, construction and implementation of a low cost solar-wind hybrid energy system,” IEEE Latin America Trans., vol. 13, pp. 3304-3309, 2015.
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[10] P. P. Vergara, J. M. Rey, L. C. P. Da Silva, and G. Ordóñez, “Comparative analysis of design criteria for hybrid photovoltaic/wind/battery systems,” IET Renew. Power Gener., vol. 11, pp. 253-261, 2016.
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16
ORIGINAL_ARTICLE
A Bi-Level Optimization Approach for Optimal Operation of Distribution Networks with Retailers and Micro-grids
Distributed energy resources (DERs) including distributed generators (DGs) and controllable loads (CLs) are managed in the form of several microgrids (MGs) in active distributions networks (ADNs) to meet the demand locally. On the other hand, some loads of distribution networks (DNs) can be supplied by retailers which participate in wholesale energy markets. Therefore, there are several decision makers in DNs which their cooperation should be modeled for optimal operation of the network. For this purpose, a bi-level optimization approach is proposed in this paper to model the cooperation between retailers and MGs in DNs. In the proposed model, the aim of the upper level (leader) and lower level (follower) problems are to maximize the profit of retailers and to minimize the cost of MGs, respectively. To solve the proposed multi-objective bi-level optimization model, multi-objective Particle Swarm Optimization (MOPSO) algorithm is employed. The effectiveness of the proposed bi-level model and its solution methodology is investigated in the numerical results.
https://joape.uma.ac.ir/article_773_5c5e9fc6b3705ef2fd7a00c27f97be21.pdf
2020-02-01
15
21
10.22098/joape.2019.5432.1407
Bi-level Optimization
Micro-grids
Particle Swarm Optimization
Retailer
H.
Fateh
hadi.fateh1990@gmail.com
1
Department of Electrical Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran.
AUTHOR
A.
Safari
asafari1650@yahoo.com
2
Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.
AUTHOR
S.
Bahramara
s_bahramara@yahoo.com
3
Department of Electrical Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran.
LEAD_AUTHOR
[1] A. Rastgou, J. Moshtagh, and S. Bahramara, “Probabilistic Power Distribution Planning Using Multi-Objective Harmony Search Algorithm,” J. Oper. Autom. Power Eng., vol. 6, pp. 111-125, 2018.
1
[2] P. Sheikhahmadi, S. Bahramara, J. Moshtagh, and M. Yazdani Damavandi, “A risk-based approach for modeling the strategic behavior of a distribution company in wholesale energy market,” Appl. Energy, vol. 214, pp. 24-38, 2018.
2
[3] S. Bahramara, M. Yazdani-Damavandi, J. Contreras, M. Shafie-Khah, and J. P. Catalão, “Modeling the strategic behavior of a distribution company in wholesale energy and reserve markets,” IEEE Trans. Smart Grid, vol. 9, pp. 3857-3870, 2018.
3
[4] A. Sadeghi Yazdankhah and R. Kazemzadeh, “Power management in a utility connected micro-grid with multiple renewable energy sources,” J. Oper. Autom. Power Eng., vol. 5, pp. 1-10, 2017.
4
[5] A. A. Algarni and K. Bhattacharya, “A generic operations framework for discos in retail electricity markets,” IEEE Trans. Power Syst., vol. 24, pp. 356-367, 2009.
5
[6] A. Safdarian, M. Fotuhi-Firuzabad, and M. Lehtonen, “A stochastic framework for short-term operation of a distribution company,” IEEE Trans. Power Syst., vol. 28, pp. 4712-4721, 2013.
6
[7] C. Zhang, Q. Wang, J. Wang, M. Korpås, P. Pinson, J. Østergaard, “Trading strategies for distribution company with stochastic distributed energy resources,” Appl. Energy, vol. 177, pp. 625-635, 2016.
7
[8] S. M. Larimi, M. Haghifam, and A. Ghadiri, “Determining the guaranteed energy purchase price for Distributed Generation in electricity distribution networks,” Util. Policy, vol. 41, pp. 118-127, 2016.
8
[9] H. Haghighat and S. W. Kennedy, “A bilevel approach to operational decision making of a distribution company in competitive environments,” IEEE Trans. Power Syst., vol. 27, pp. 1797-1807, 2012.
9
[10] R. Palma-Behnke, L. S. Vargas, and A. Jofré, “A distribution company energy acquisition market model with integration of distributed generation and load curtailment options,” IEEE Trans. Power Syst., vol. 20, pp. 1718-1727, 2005.
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[11] J. Vasiljevska, J. P. Lopes, and M. Matos, “Evaluating the impacts of the multi-microgrid concept using multicriteria decision aid,” Electr. Power Syst. Res., vol. 91, pp. 44-51, 2012.
11
[12] N. Hatziargyriou, A. Anastasiadis, A. Tsikalakis, and J. Vasiljevska, “Quantification of economic, environmental and operational benefits due to significant penetration of Microgrids in a typical LV and MV Greek network,” Eur. Trans. Electr. Power., vol. 21, pp. 1217-1237, 2011.
12
[13] A. K. Marvasti, Y. Fu, S. DorMohammadi, and M. Rais-Rohani, “Optimal operation of active distribution grids: A system of systems framework,” IEEE Trans. Smart Grid, vol. 5, pp. 1228-1237, 2014.
13
[14] S. Bahramara, M. P. Moghaddam, and M. Haghifam, “A bi-level optimization model for operation of distribution networks with micro-grids,” Int. J. Electr. Power Energy Syst., vol. 82, pp. 169-178, 2016.
14
[15] S. Bahramara, M. P. Moghaddam, and M. R. Haghifam, “Modelling hierarchical decision making framework for operation of active distribution grids,”IET Gener. Transm. Distrib., vol. 9, pp. 2555-2564, 2015.
15
[16] H. Algarvio, F. Lopes, J. Sousa, and J. Lagarto, “Multi-agent electricity markets: Retailer portfolio optimization using Markowitz theory,” Electr. Power Syst. Res., vol. 148, pp. 282-294, 2017.
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[17] M. Khojasteh and S. Jadid, “Decision-making framework for supplying electricity from distributed generation-owning retailers to price-sensitive customers,” Util. Policy, vol. 37, pp. 1-12, 2015.
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[18] S. Nojavan, K. Zare, and B. Mohammadi-Ivatloo, “Risk-based framework for supplying electricity from renewable generation-owning retailers to price-sensitive customers using information gap decision theory,” Int. J. Electr. Power Energy Syst., vol. 93, pp. 156-170, 2017.
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[19] N. Mahmoudi, T. K. Saha, and M. Eghbal, “Modelling demand response aggregator behavior in wind power offering strategies,” Appl. Energy, vol. 133, pp. 347-355, 2014.
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[20] M. Zugno, J. M. Morales, P. Pinson, and H. Madsen, “A bilevel model for electricity retailers' participation in a demand response market environment,” Energy Econ., vol. 36, pp. 182-197, 2013.
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[21] M. Marzband, A. Sumper, A. Ruiz-Álvarez, J. L. Domínguez-García, and B. Tomoiagă, “Experimental evaluation of a real time energy management system for stand-alone microgrids in day-ahead markets,” Appl. Energy, vol. 106, pp. 365-376, 2013.
21
T. Lv, Q. Ai, and Y. Zhao, “A bi-level multi-objective optimal operation of grid-connected microgrids,” Electr. Power Syst. Res., vol. 131, pp. 60-70, 2016.
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[22] A. Rastgou, S. Bahramara, and J. Moshtagh, “Flexible and robust distribution network expansion planning in the presence of distributed generators,” Int. Trans. Electr. Energy Syst., p. e2637.
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[23] A. Rastgou, J. Moshtagh, and S. Bahramara, “Improved harmony search algorithm for electrical distribution network expansion planning in the presence of distributed generators,” Energy, vol. 151, pp. 178-202, 2018.
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[24] M. Azaza and F. Wallin, “Multi objective particle swarm optimization of hybrid micro-grid system: A case study in Sweden,” Energy, vol. 123, pp. 108-118, 2017.
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29
ORIGINAL_ARTICLE
Stochastic Assessment of the Renewable–Based Multiple Energy System in the Presence of Thermal Energy Market and Demand Response Program
The impact of different energy storages on power systems has become more important due to the development of energy storage technologies. This paper optimizes the stochastic scheduling of a wind-based multiple energy system (MES) and evaluates the operation of the proposed system in combination with electrical and thermal demand-response programs and the three-mode CAES (TM-CAES) unit. The proposed wind-integrated MES consists of a TM-CAES unit, electrical boiler unit, and thermal storage system which can exchange thermal energy with the local thermal network and exchange electricity with the local grid. The electrical and thermal demands as well as wind farm generation are modeled as a scenario-based stochastic problem using the Monte Carlo simulation method. Afterwards, the computational burden is reduced by applying a proper scenario-reduction algorithm to initial scenarios. Finally, the proposed methodology is implemented to a case study to evaluate the effectiveness and appropriateness of the proposed method.
https://joape.uma.ac.ir/article_778_64981506aeab33a060edece71c450189.pdf
2020-02-01
22
31
10.22098/joape.2019.5072.1382
Three mode compressed air energy storage (TM-CAES)
thermal energy market
Stochastic programming
wind generation
demand response program
H.
Mousavi-Sarabi
hessam.mousavi.7@gmail.com
1
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.
AUTHOR
M.
Jadidbonab
mohammad.jadidbonab@gmail.com
2
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.
AUTHOR
B.
Mohammadi ivatloo
ivatloo@gmail.com
3
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.
LEAD_AUTHOR
[1] M. Budt, D. Wolf, R. Span, and J. Yan, “A review on compressed air energy storage: Basic principles, past milestones and recent developments,” Appl. Energy, vol. 170, pp. 250-268, 2016.
1
[2] E. Heydarian-Forushani and H. Aalami, “Multi objective scheduling of utility-scale energy storages and demand response programs portfolio for grid integration of wind power,” J. Oper. Autom. Power Eng., vol. 4, pp. 104-116, 2016.
2
[3] K. Afshar and A. Shokri Gazafroudi, “Application of stochastic programming to determine operating reserves with considering wind and load uncertainties,” J. Oper. Autom. Power Eng., vol. 1, pp. 96-109, 2007.
3
[4] M. Jadid-Bonab, A. Dolatabadi, B. Mohammadi-Ivatloo, M. Abapour, and S. Asadi, “Risk-constrained Energy Management of PV Integrated Smart Energy Hub in the Presence of Demand Response Program and Compressed Air Energy,” IET Renew. Power Gener., 2019.
4
[5] S. Shafiee, H. Zareipour, A. M. Knight, N. Amjady, and B. Mohammadi-Ivatloo, “Risk-constrained bidding and offering strategy for a merchant compressed air energy storage plant,” IEEE Trans. Power Syst., vol. 32, pp. 946-957, 2017.
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[6] E. Drury, P. Denholm, and R. Sioshansi, “The value of compressed air energy storage in energy and reserve markets,” Energy, vol. 36, pp. 4959-4973, 2011.
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[7] A. Mohammadi, M. H. Ahmadi, M. Bidi, F. Joda, A. Valero, and S. Uson, “Exergy analysis of a Combined Cooling, Heating and Power system integrated with wind turbine and compressed air energy storage system,” Energy Conv. Manag., vol. 131, pp. 69-78, 2017.
7
[8] E. Yao, H. Wang, L. Wang, G. Xi, and F. Maréchal, “Multi-objective optimization and exergoeconomic analysis of a combined cooling, heating and power based compressed air energy storage system,” Energy Conv. Manag., vol. 138, pp. 199-209, 2017.
8
[9] X. Liu, Y. Zhang, J. Shen, S. Yao, and Z. Zhang, “Characteristics of air cooling for cold storage and power recovery of compressed air energy storage (CAES) with inter-cooling,” Appl. Therm. Eng., vol. 107, pp. 1-9, 2016.
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[10] M. Saadat, F. A. Shirazi, and P. Y. Li, “Modeling and control of an open accumulator Compressed Air Energy Storage (CAES) system for wind turbines,” Appl. Energy, vol. 137, pp. 603-616, 2015.
10
[11] M. Y. Damavandi, S. Bahramara, M. P. Moghaddam, M.-R. Haghifam, M. Shafie-khah, and J. P. Catalão, “Bi-level approach for modeling multi-energy players' behavior in a multi-energy system,” Proce. IEEE Power Tech, Eindhoven, 2015, pp. 1-6.
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[12] M. Yazdani-Damavandi, N. Neyestani, M. Shafie-khah, J. Contreras, and J. P. Catalao, “Strategic behavior of multi-energy players in electricity markets as aggregators of demand side resources using a bi-level approach,” IEEE Trans. Power Syst., vol. 33, pp. 397-411, 2018.
12
[13] P. Sheikhahmadi, S. Bahramara, J. Moshtagh, and M. Y. Damavandi, “A risk-based approach for modeling the strategic behavior of a distribution company in wholesale energy market,” Appl. Energy, vol. 214, pp. 24-38, 2018.
13
[14] M. Jadidbonab, M. Vahid-Pakdel, H. Seyedi, and B. Mohammadi-ivatloo, “Stochastic assessment and enhancement of voltage stability in multi carrier energy systems considering wind power,” Int. J. Electr. Power Energy Syst., vol. 106, pp. 572-584, 2019.
14
[15] M. T. Hagh, M. Jadidbonab, and M. Jedari, “Control strategy for reactive power and harmonic compensation of three-phase grid-connected photovoltaic system,” CIRED-Open Access Proce. J., vol. 2017, pp. 559-563, 2017.
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[16] M. G. Molina and P. E. Mercado, “Power flow stabilization and control of microgrid with wind generation by superconducting magnetic energy storage,” IEEE Trans. Power Electron., vol. 26, pp. 910-922, 2011.
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[17] M. Abbaspour, M. Satkin, B. Mohammadi-Ivatloo, F. H. Lotfi, and Y. Noorollahi, “Optimal operation scheduling of wind power integrated with compressed air energy storage (CAES),” Renew. Energy, vol. 51, pp. 53-59, 2013.
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[18] A. Dolatabadi, M. Jadidbonab, and B. Mohammadi-ivatloo, “Short-term scheduling strategy for wind-based energy hub: a hybrid stochastic/IGDT approach,” IEEE Trans. Sustain. Energy, vol. 10, no. 1, pp. 438-448, 2019.
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[19] M. Jadidbonab, H. Mousavi-Sarabi, and B. Mohammadi-Ivatloo, “Risk-constrained scheduling of solar-based three state compressed air energy storage with waste thermal recovery unit in the thermal energy market environment,” IET Renew. Power Gener., 2018.
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[20] H. Liang and W. Zhuang, “Stochastic modeling and optimization in a microgrid: A survey,” Energies, vol. 7, pp. 2027-2050, 2014.
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[21] A. Hooshmand, M. H. Poursaeidi, J. Mohammadpour, H. A. Malki, and K. Grigoriads, “Stochastic model predictive control method for microgrid management,” Proce. IEEE PES in Innovative Smart Grid Tech. (ISGT), 2012, pp. 1-7.
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34
ORIGINAL_ARTICLE
Selective Harmonics Elimination Technique in Cascaded H-Bridge Multi-Level Inverters Using the Salp Swarm Optimization Algorithm
A new optimization method is proposed in this paper for finding the firing angles in multi-level voltage source inverters to eliminate low-order selective harmonics and reduce total harmonic distortion (THD) value of the output voltage. For thid end, Fourier series is used for calculating objective function and selecting specific harmonics. Regarding the nature and complexity of the employed non-algebraic equations in the optimization problem for achieving the optimal angle in the multi-level inverter, a recent developed meta-heuristic method known as Salp Swarm Algorithm (SSA) is presented. In the proposed method, the optimal angles for a given multi-level inverter are obtained based on the objective function such that the magnitudes of the selective harmonics and the THD value of the output voltage are reduced. The method is applied on a cascaded H-bridge type five-level inverter. The simulation results illustrate that the magnitudes of the selective harmonics and the THD percentage of the output voltage have been reduced through selecting the optimal switching angle by the proposed optimization algorithm. The result of this method are compared with those of SPWM method. Moreover, the performance of SSA algorithm with respect to PSO algorithm is compared which shows its rapid convergence speed and less THD value.
https://joape.uma.ac.ir/article_779_44989d8539f38b3764d4dec563106ebd.pdf
2020-02-01
32
42
10.22098/joape.2019.5545.1418
"Elimination of selective harmonics"
'Cascaded multi-level H-bridge inverter"
"Total harmonic distortion (THD)"
"Salp swarm optimization algorithm"
M.
Hosseinpour
hoseinpour.majid@gmail.com
1
Department of Electrical and Computer Engineering, University of Mohaghegh Ardabili, Ardabil, Iran.
LEAD_AUTHOR
S.
Mansoori
saeede.mansoori@gmail.com
2
Department of Electrical and Computer Engineering, University of Mohaghegh Ardabili, Ardabil, Iran.
AUTHOR
H.
Shayeghi
hshayeghi@uma.ac.ir
3
Department of Electrical and Computer Engineering, University of Mohaghegh Ardabili, Ardabil, Iran.
AUTHOR
[1] J.-S. Lai, F.Z. Peng, “Multilevel converters-a new breed of power converters,” Proce. IEEE Ind. Appl. Conf. 1995. Thirtieth IAS Annu. Meet. IAS’95., Conf. Rec. 1995 IEEE, 1995, pp. 2348–2356.
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[3] M.M. Rahimian, M. Hosseinpour, A. Dejamkhooy, “A modified phase-shifted pulse width modulation to extend linear operation of hybrid modular multi-level converter,” J. Oper. Autom. Power Eng., vol. 6, no. 2, pp. 183-192, 2018.
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[4] N. Rasekh, MM. Rahimian, M. Hosseinpour, A. Dejamkhooy, A. Akbarimajd, “A step by step design procedure of PR controller and capacitor current feedback active damping for a LCL-type grid-tied T-type inverter,” Proce. 2019 10th Int. Power Electron. Drive Syst. Tech. Conf. (PEDSTC), 2019, pp. 612-617.
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41
ORIGINAL_ARTICLE
Energy management of virtual power plant to participate in the electricity market using robust optimization
Virtual power plant (VPP) can be studied to investigate how energy is purchased or sold in the presence of electricity market price uncertainty. The VPP uses different intermittent distributed sources such as wind turbine, flexible loads, and locational marginal prices (LMPs) in order to obtain profit. VPP should propose bidding/offering curves to buy/sell from/to day-ahead market. In this paper, robust optimization approach is proposed to achieve the optimal offering and bidding curves which should be submitted to the day-ahead market. This paper uses mixed-integer linear programming (MILP) model under GAMS software based on robust optimization approach to make appropriate decision on uncertainty to get profit which is resistance versus price uncertainty. The offering and bidding curves of VPP are obtained based on derived data from results. The proposed method, due to less computing, is also easy to trace the problem for the VPP operator. Finally, the price curves are obtained in terms of power for each hour, which operator uses the benefits of increasing or decreasing market prices for its plans. Also, results of comparing deterministic and RO cases are presented. Results demonstrate that profit amount in maximum robustness case is reduced 25.91 % and VPP is resisted against day-ahead market price uncertainty.
https://joape.uma.ac.ir/article_782_5505c388c0742d22d0e1127eb502ffca.pdf
2020-02-01
43
56
10.22098/joape.2019.5362.1400
Virtual power plant
Electricity market uncertainty
Robust optimization approach
Bidding and offering curves
Distributed energy resources
M.
Mohebbi-Gharavanlou
mehran.mohebbi96@ms.tabrizu.ac.ir
1
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.
AUTHOR
S.
Nojavan
sayyad.nojavan@bonabu.ac.ir
2
Department of Electrical Engineering, University of Bonab, Bonab, Iran.
LEAD_AUTHOR
K.
Zareh
kazem.zare@tabrizu.ac.ir
3
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.
AUTHOR
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[14] S. Skarvelis-Kazakos, E. Rikos, E. Kolentini, L. M. Cipcigan, and N. Jenkins, “Implementing agent-based emissions trading for controlling Virtual Power Plant emissions,” Electr. Power Syst. Res., vol. 102, pp. 1-7, 2013.
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38
ORIGINAL_ARTICLE
FOA: ‘Following’ Optimization Algorithm for solving Power engineering optimization problems
These days randomized-based population optimization algorithms are in wide use in different branches of science such as bioinformatics, chemical physics andpower engineering. An important group of these algorithms is inspired by physical processes or entities’ behavior. A new approach of applying optimization-based social relationships among the members of a community is investigated in this paper. In the proposed algorithm, search factors are indeed members of the community who try to improve the community by ‘following’ each other. FOA implemented on 23 well-known benchmark test functions. It is compared with eight optimization algorithms. The paper also considers for solving optimal placement of Distributed Generation (DG). The obtained results show that FOA is able to provide better results as compared to the other well-known optimization algorithms.
https://joape.uma.ac.ir/article_784_ac38155da2b4a0a2d65cb69530c79d23.pdf
2020-02-01
57
64
10.22098/joape.2019.5522.1414
optimization
social relationships
heuristic algorithms
following optimization, following
M.
Dehghani
adanbax@gmail.com
1
Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran.
LEAD_AUTHOR
M.
Mardaneh
mardaneh@sutech.ac.ir
2
Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran.
AUTHOR
O. P.
Malik
maliko@ucalgary.ca
3
Department of Electrical Engineering, University of Calgary, Calgary Alberta Canada.
AUTHOR
[1] M. Dehghani, Z. Montazeri, A. Dehghani, N. Nouri, and A. Seifi, "BSSA: Binary spring search algorithm," Proce. IEEE 4th Int. Conf. Knowl. Base. Eng. Innovation (KBEI)., 2017, pp. 0220-0224.
1
[2] M. Dehghani, Z. Montazeri, A. Dehghani, and A. Seifi, "Spring search algorithm: A new meta-heuristic optimization algorithm inspired by Hooke's law," Proce. IEEE 4th Int. Conf. Knowl. Base. Eng. Innovation (KBEI)., 2017, pp. 0210-0214.
2
[3] M. Dehghani, Z. Montazeri, O. P. Malik, A. Ehsanifar, and A. Dehghani, "OSA: Orientation Search Algorithm," Int. J. Ind. Electron., Control. Optim., vol. 2, pp. 99-112, 2019.
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44
ORIGINAL_ARTICLE
Multi-Area State Estimation Based on PMU Measurements in Distribution Networks
State estimation in the energy management center of active distribution networks has attracted many attentions. Considering an increase in complexity and real-time management of active distribution networks and knowing the network information at each time instant are necessary. This article presents a two-step multi-area state estimation method in balanced active distribution networks. The proposed method is based on the location of PMU measurements of the network. The network is divided into several sub-areas about PMUs in the first step. A local sate estimation is implemented in each sub-area. The estimated values of the first step along with real measurements are used as measurements for second step estimation. The measurements are located in each sub-area using these values based on the ellipse area method, and the best location of measurements is extracted. Therefore, a second step state estimation including integrated state estimation of the whole network is performed by using the measurements obtained and located from the first step. The estimation results of the first step are used in the second step which improve the estimation accuracy. Simulations are performed on a standard IEEE 69-bus network to validate the proposed method.
https://joape.uma.ac.ir/article_807_922eccb415dfc79e25950f2f5f2e7aed.pdf
2020-02-01
65
74
10.22098/joape.2019.5798.1434
measurements location
state estimation
synchronous measurements
two-step state estimation
zoning distribution networks
O.
Eghbali
om_eghbali@sut.ac.ir
1
Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
AUTHOR
R.
Kazemzadeh
r.kazemzadeh@sut.ac.ir
2
Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
LEAD_AUTHOR
K.
Amiri
k_amiri@sut.ac.ir
3
Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
AUTHOR
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[14] C. Muscas, M. Pau, P. A. Pegoraro, S. Sulis, F. Ponci and A. Monti, “Two-step procedures for wide-area distribution system state estimation”, in Instrum. Meas. Technol. Conf. (I2MTC) Proceedings, 2014 IEEE Int., pp. 1517-1522, 2014.
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21
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[23] C. Muscas, P. A. Pegoraro, S. Sulis, M. Pau, F. Ponci and A. Monti, “Fast multi-area approach for distribution system state estimation”, Instrum. Meas. Technol. Conf. Proc. (I2MTC), 2016 IEEE Inter., pp. 1-6, 2016.
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35
ORIGINAL_ARTICLE
SCA based Fractional-order PID Controller Considering Delayed EV Aggregators
The EVs battery has the ability to enhance the balance between the load demand and power generation units. The EV aggregators to manage the random behaviour of EV owners and increasing EVs participation in the ancillary services market are employed. The presence of aggregators could lead to time-varying delay in load frequency control (LFC) schemes. The effects of these delays must be considered in the LFC controller design. Due to the dependency of controller effectiveness on its parameters, these parameters should be designed in such a way that the LFC system has desired performance in the presence of time-varying delay. Therefore, a Sine Cosine Algorithm (SCA) is utilized to adjust the fractional-order PID (FOPID) controller coefficients. Also, some evaluations are performed about the proposed LFC performance by integral absolute error (IAE) indicator. Simulations are carried out in both single and two area LFC system containing EV aggregators with time-varying delay. According to results, the proposed controller has fewer frequency variations in contrast to other controllers presented in the case studies. The obtained output could be considered as a solution to evaluate the proposed controller performance for damping the frequency oscillations in the delayed LFC system.
https://joape.uma.ac.ir/article_808_3ceb7f93070b19e90417e0d0a73693d5.pdf
2020-02-01
75
85
10.22098/joape.2019.6088.1460
Electric vehicle aggregator
Time-varying delay
Fractional-order PID
Sine cosine algorithm
Load frequency control
F.
Babaei
farshad291371@gmail.com
1
Department of Electrical Engineering, Shahid Madani Azarbaijan University, Tabriz, Iran
LEAD_AUTHOR
A.
Safari
asafari1650@yahoo.com
2
Department of Electrical Engineering, Shahid Madani Azarbaijan University, Tabriz, Iran
AUTHOR
[1] L. Erickson, “Reducing greenhouse gas emissions and improving air quality: Two global challenges”, Environ. Prog. Sustainable Energy, vol. 36, pp. 982-988, 2017.
1
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2
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26