Ghadi, MJ, Ghavidel, S, Rajabi, A, Azizivahed, A, Li, L & Zhang, J 2019, 'A review on economic and technical operation of active distribution systems', Renewable and Sustainable Energy Reviews, vol. 104, pp. 38-53.View/Download from: UTS OPUS or Publisher's site
© 2019 Elsevier Ltd Along with the advent of restructuring in power systems, considerable integration of renewable energy resources has motivated the transition of traditional distribution networks (DNs) toward new active ones. In the meanwhile, rapid technology advances have provided great potentials for future bulk utilization of generation units as well as the energy storage (ES) systems in the distribution section. This paper aims to present a comprehensive review of recent advancements in the operation of active distribution systems (ADSs) from the viewpoint of operational time-hierarchy. To be more specific, this time-hierarchy consists of two stages, and at the first stage of this time-hierarchy, four major economic factors, by which the operation of traditional passive DNs is evolved to new active DNs, are described. Then the second stage of the time-hierarchy refers to technical management and power quality correction of ADSs in terms of static, dynamic and transient periods. In the end, some required modeling and control developments for the optimal operation of ADSs are discussed. As opposed to previous review papers, potential applications of devices in the ADS are investigated considering their operational time-intervals. Since some of the compensating devices, storage units and generating sources may have different applications regarding the time scale of their utilization, this paper considers real scenario system operations in which components of the network are firstly scheduled for the specified period ahead; then their deviations of operating status from reference points are modified during three time-intervals covering static, dynamic and transient periods.
Ghavidel Jirsaraie, S, Jabbari Ghadi, M, Vahed, AA, Aghaei, J, Li, L & Zhang, J 2019, 'Risk-Constrained Bidding Strategy for a Joint Operation of Wind Power and Compressed Air Energy Storage Aggregators', IEEE Transactions on Sustainable Energy, pp. 1-1.View/Download from: UTS OPUS or Publisher's site
Ghavidel, S, Rajabi, A, Ghadi, MJ, Azizivahed, A, Li, L & Zhang, J 2019, 'Risk-constrained demand response and wind energy systems integration to handle stochastic nature and wind power outage', IET Energy Systems Integration, vol. 1, no. 2, pp. 114-120.View/Download from: Publisher's site
Ghadi, MJ, Rajabi, A, Ghavidel, S, Azizivahed, A, Li, L & Zhang, J 2019, 'From active distribution systems to decentralized microgrids: A review on regulations and planning approaches based on operational factors', Applied Energy, vol. 253.View/Download from: UTS OPUS or Publisher's site
© 2019 Elsevier Ltd Restructuring of power systems along with the integration of renewable energy resources in electricity networks have transformed traditional distribution networks (DNs) into new active distribution systems (ADSs). In addition, rapid advancement of technology has enabled the bulk utilization of power generation units and energy storage (ES) systems in distribution networks. The next step in this trend is to decentralize ADSs to microgrids (MGs). This paper aims to present a review on the recent advancements in the development of ADSs and MGs. In this respect, the regulatory requirements and economic concepts, by which the traditional passive DNs are evolved into ADSs, are categorized and illustrated first. Then, the state-of-the-art of ADS formation is detailed based on the novel standpoint of grid operation factors which are involved in deregulated electricity markets at the distribution level. After presenting highlighted projects of MGs across the world, a similar review approach has been adopted to explain the formation of MGs which play a vital role in the decentralization of ADSs. This survey can provide both policy makers and distribution system planners with new perspectives to establish or participate in day-ahead wholesale markets.
Sharifian, A, Ghadi, MJ, Ghavidel, S, Li, L & Zhang, J 2018, 'A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data', Renewable Energy, vol. 120, pp. 220-230.View/Download from: UTS OPUS or Publisher's site
© 2017 Elsevier Ltd Nowadays, due to some environmental restrictions and decrease of fossil fuel sources, renewable energy sources and specifically wind power plants have a major part of energy generation in the industrial countries. To this end, the accurate forecasting of wind power is considered as an important and influential factor for the management and planning of power systems. In this paper, a novel intelligent method is proposed to provide an accurate forecast of the medium-term and long-term wind power by using the uncertain data from an online supervisory control and data acquisition (SCADA) system and the numerical weather prediction (NWP). This new method is based on the particle swarm optimization (PSO) algorithm and applied to train the Type-2 fuzzy neural network (T2FNN) which is called T2FNN-PSO. The presented method combines both of fuzzy system's expert knowledge and the neural network's learning capability for accurate forecasting of the wind power. In addition, the T2FNN-PSO can appropriately handle the uncertainties associated with the measured parameters from SCADA system, the numerical weather prediction and measuring tools. The proposed method is applied on a case study of a real wind farm. The obtained simulation results validate effectiveness and applicability of the proposed method for a practical solution to an accurate wind power forecasting in a power system control center.
Imani, MH, Ghadi, MJ, Ghavidel, S & Li, L 2018, 'Demand Response Modeling in Microgrid Operation: a Review and Application for Incentive-Based and Time-Based Programs', Renewable and Sustainable Energy Reviews, vol. 94, pp. 486-499.View/Download from: UTS OPUS or Publisher's site
© 2018 Elsevier Ltd During recent years, with the advent of restructuring in power systems as well as the increase of electricity demand and global fuel energy prices, challenges related to implementing demand response programs (DRPs) have gained remarkable attention of independent system operators (ISOs) and customers, aiming at the improvement of attributes of the load curve and reduction of energy consumption as well as benefiting customers. In this paper, different types of DRPs are modeled based on price elasticity of the demand and the concept of customer benefit. Besides, the impact of implementing DRPs on the operation of grid-connected microgrid (MG) is analyzed. Moreover, several scenarios are presented in order to model uncertainties interfering MG operations including failure of generation units and random outages of transmission lines and upstream line, error in load demand forecasting, uncertainty in production of renewable energies (wind and solar) based distributed generation units, and the possibility that customers do not respond to scheduled interruptions. Simulations are conducted for two principal categories of DRP including incentive-based programs and time-based programs on an 11-bus MG over a 24-h period and also a 14-bus MG over a period of 336 h (two weeks). Simulation results indicate the effects of DRPs on total operation costs, customer's benefit, and load curve as well as determining optimal use of energy resources in the MG operation. In this regard, prioritizing of DRPs on the MG operation is required.
Sharifian, A, Sasansara, SF, Ghadi, MJ, Ghavidel, S, Li, L & Zhang, J 2018, 'Dynamic performance improvement of an ultra-lift Luo DC–DC converter by using a type-2 fuzzy neural controller', Computers and Electrical Engineering, vol. 69, pp. 171-182.View/Download from: UTS OPUS or Publisher's site
© 2018 Due to the uncertainty associated with the structure and electrical elements of DC–DC converters and the nonlinear performance of these modules, designing an effective controller is highly complicated and also technically challenging. This paper employs a new control approach based on type-2 fuzzy neural controller (T2FNC) in order to improve the dynamic response of an ultra-lift Luo DC–DC converter under different operational conditions. The proposed controller can rapidly stabilize the output voltage of converter to expected values by tuning the converter switching duty cycle. This controller can tackle the uncertainties associated with the structure of converters, measured control signals and measuring devices. Moreover, a new intelligent method based on firefly algorithm is applied to tune the parameters of T2FNC. In order to demonstrate the effectiveness of the proposed control approach, the proposed controller is compared to PI and fuzzy controllers under different operational conditions. Results validate efficiency of proposed T2FNC.
Bakhshipour, M, Ghadi, MJ & Namdari, F 2017, 'Swarm robotics search & rescue: A novel artificial intelligence-inspired optimization approach', APPLIED SOFT COMPUTING, vol. 57, pp. 708-726.View/Download from: UTS OPUS or Publisher's site
Ghadi, MJ, Karin, AI, Baghramian, A & Imani, MH 2016, 'Optimal power scheduling of thermal units considering emission constraint for GENCOs' profit maximization', INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, vol. 82, pp. 124-135.View/Download from: UTS OPUS or Publisher's site
Ghadi, MJ, Baghramian, A & Imani, MH 2016, 'An ICA based approach for solving profit based unit commitment problem market', APPLIED SOFT COMPUTING, vol. 38, pp. 487-500.View/Download from: Publisher's site
Ghadi, MJ, Gilani, SH, Afrakhte, H & Baghramian, A 2014, 'A novel heuristic method for wind farm power prediction: A case study', INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, vol. 63, pp. 962-970.View/Download from: Publisher's site
Gilani, SH, Ghadi, MJ & Afrakhte, H 2013, 'Optimal allocation of wind turbines considering different costs for interruption aiming at power loss reduction and reliability improvement using Imperialistic Competitive Algorithm', International Review of Electrical Engineering, vol. 8, no. 1, pp. 284-296.
In recent years, utilization of wind-based distributed generation (DG) as one of the most widespread used types of green generation technologies has attracted remarkable consideration. Recently, several contributions have accomplished in case of wind-based generation. In this research, in order to minimize the costs of annual energy losses and Energy Not Supplied (ENS) of the distribution networks, a multi-objective probabilistic based approach is proposed to determine the optimal location and capacity of wind-based units along with providing assurance for a desirable level of voltage profile. An Imperialist Competitive Algorithm (ICA) is employed to overcome non-convexity and complexity of the mixed integer optimization problem of DG allocation in the radial distribution networks. For this purpose, a Fuzzy C-Means (FCM) clustering is used to categorize historical data of load demands and output power of DG units. Then, given the fuzzy center point of the clusters and their respective probability generated from FCM clustering implementation, uncertainty consideration is involved to the probabilistic calculation of both of the wind-based DG and load demand. Moreover, during the calculation process of ENS, different costs of interruption are considered for various customers of the network. Finally, presented technique is exerted for an IEEE 33-bus standard test system subject to the system constrains under different cases and effectiveness and capability of the method is assessed. Obtained results demonstrate competence of the method to produce a significant reduction in ENS value and annual energy losses. © 2013 Praise Worthy Prize S.r.l. - All rights reserved.
Imani, MH, Yousefpour, K, Andani, MT & Ghadi, MJ 2019, 'Effect of Changes in Incentives and Penalties on Interruptible/Curtailable Demand Response Program in Microgrid Operation', 2019 IEEE TEXAS POWER AND ENERGY CONFERENCE (TPEC), IEEE Texas Power and Energy Conference (TPEC), IEEE, Texas A&M Univ, College Station, TX.View/Download from: UTS OPUS
Imani, MH, Yousefpour, K, Ghadi, MJ & Andani, MT 2018, 'Simultaneous presence of wind farm and V2G in security constrained unit commitment problem considering uncertainty of wind generation', 2018 IEEE Texas Power and Energy Conference, TPEC 2018, IEEE Texas Power and Energy Conference, IEEE, College Station, TX, USA, pp. 1-6.View/Download from: UTS OPUS or Publisher's site
© 2018 IEEE. In this paper, simultaneous employment of electrical vehicle-to-grid (V2G) and wind power generation in security constrained unit commitment (SCUC) problem are considered. SCUC problem as one of the most highlighted research area in electrical power system provides a commitment scheduling table for the generation units in which generation operator aims to maximize system security as well as to minimize generation costs along with the satisfying system and units constraints. The technology of V2G as a new energy resource absorbed remarkable consideration, recently. V2G reduces the dependence of generation procedure to the small and costly thermal units and subsequently has tremendously impact on diminishing operation costs as well as ameliorate of load vacillations management. Besides the V2G, utilization of renewable energies like wind-based generation gained considerable attention in last decade. In this paper, simultaneous employment of V2G and wind power in scheduling and operation of power systems considering the uncertainty of wind generation is presented. Numerical results of independent use of V2G and simultaneous utilization of V2G and wind-based generation are provided and effects of such the obligations on the reduction of generation costs and enhancement of operation indexes and penetration of wind farms in power systems are investigated.
Azizivahed, A, Ghavidel, S, Jabbari Ghadi, M, Li, L & Zhang, J 2018, 'Multi-Objective Energy Management Approach Considering Energy Storages in Distribution Networks with Respect to Voltage Security'.View/Download from: UTS OPUS
Rajabi, A, Li, L, Zhang, J, Zhu, J, Ghavidel, S & Ghadi, M 2017, 'A Review on Clustering of Residential Electricity Customers and Its Applications', Proceedings of the The 20th International Conference on Electrical Machines and Systems (ICEMS), International Conference on Electrical Machines and Systems, IEEE, Sydney, Australia, pp. 1-6.View/Download from: UTS OPUS or Publisher's site
Clustering is a well-recognized data mining
technique which enables the determination of underlying
patterns in datasets. In electric power systems, it has been
traditionally utilized for different purposes like defining
customer load profiles, tariff designs and improving load
forecasting. Some surveys summarized different clustering
techniques which were traditionally used for customer
segmentation and load profiling. The recent changes in
power system structure and introduction of new technologies
necessitate the new investigation of applications and benefits
of clustering methods for power systems. In this regard, this
paper aims at reviewing the new research for clustering
techniques for residential customers.
Ghavidel, S, Barani, M, Azizivahed, A, Ghadi, M, Li, L & Zhang, J 2017, 'Hybrid Power Plant Offering Strategy to Deal with the Stochastic Nature and Outage of Wind Generators', The 20th International Conference on Electrical Machines and Systems (ICEMS), International Conference on Electrical Machines and Systems, IEEE, Sydney, NSW, Australia, pp. 1-6.View/Download from: UTS OPUS or Publisher's site
The stochastic nature of wind power generators and their possible outage are crucial issues which make them difficult to participate in electricity markets. However, demand side as a decent balancing resource can be used to compensate the challenges of lack of supply-demand balance or state of outage for wind generators. This paper firstly models the outage of wind generators. Then an offering strategy with a three-stage stochastic programming is presented for a hybrid power plant which includes a wind power producer and a demand response provider. Three electricity markets are considered including day-ahead, adjustment and balancing market. The conditional value-at-risk is also added to the offering strategy to control the profit risk. The offering strategy is tested in a wind farm and electricity market located in Spain. The result shows that the hybrid power plant offering strategy can effectively assist with the balancing and outage problem of the wind power producer and increase the overall profit of the joint operation.
Abbasi, M, Rajabi, A, Taki, M, Li, L, Ghavidel, S & Ghadi, M 2017, 'Risk-Constrained Offering Strategies of a Price-Maker Demand Response Aggregator', 20th International Conference on Electrical Machines and Systems (ICEMS), International Conference on Electrical Machines and Systems, IEEE, Sydney, NSW, Australia.View/Download from: UTS OPUS or Publisher's site
Offering strategy of a price-maker demand response aggregator (DRA) in a two-settlement market is presented in this paper. The aggregator minimizes its cost by offering energy and price bids in the day-ahead market and energy bids in the balancing market. On the other hand, DRA optimally manages the aggregated demands of a large number of electric vehicles and properly distributes them through the time. The problem is formulated as a stochastic mixed-integer nonlinear optimization problem. The risk of the problem is managed by conditional value-at-risk measure and finally, the proposed approach is numerically evaluated through a detailed case study.
Azizivahed, A, Ghavidel Jirsaraie, S, Ghadi, M, Li, L & Zhang, J 2017, 'New Energy Management Approach in Distribution Systems Considering Energy Storages', 20th International Conference on Electrical Machines and Systems (ICEMS), International Conference on Electrical Machines and Systems, IEEE, Sydney, NSW, Australia.View/Download from: UTS OPUS or Publisher's site
This paper presents a new method for energy management in distribution networks in the presence of energy storage, solar photovoltaic (PV) systems and diesel generators. Achieving optimal charge and discharge pattern for batteries and optimal diesel generator output with minimal operation cost are the main goal of this paper. The innovation of this paper is to consider the network effect on the underlying method with operational limitations and power-flow constraints such as power loss, voltage and current limitation. The proposed problem is solved for 24-hour time horizon, and the modified imperialist competitive algorithm (ICA) is also developed using mutation strategy to optimally solve the problem. Finally, to show the good performance and supremacy of the proposed method, a 33-bus IEEE standard test system is applied.
Azizivahed, A, Ghavidel Jirsaraie, S, Ghadi, M, Li, L & Zhang, J 2017, 'Multi-Area Economic Emission Dispatch Considering Load Uncertainty', 20th International Conference on Electrical Machines and Systems (ICEMS), International Conference on Electrical Machines and Systems, IEEE, Sydney, NSW, Australia.View/Download from: UTS OPUS or Publisher's site
Multi-area economic emission dispatch problem provides an optimal schedule for active power of generators and interchange active power between different areas by considering the operational limitations such as balance between generation and consumption, tie-line capacity limitation, generators output constraint, and transmission losses. In this paper, a hybrid method based on shuffled frog leaping algorithm (SFLA) and particle swarm optimization (PSO) is presented to achieve better solution. Furthermore, the stochastic nature of energy consumption is modeled as uncertainty source using scenario reduction technique to achieve the results which are closer to the real condition. The performance of the proposed approach is elaborated in two test systems with different scales including 10-generator with three-area and 40-generator with four-area. The obtained results are compared with those available in the literature to show the effectiveness of the proposed approach.
Azizivahed, A, Ghavidel, S, Ghadi, MJ, Li, L & Zhang, J 2017, 'A Novel Reliability Oriented Bi-Objective Unit Commitment Problem', Proceedings of the 2017 Australasian Universities Power Engineering Conference (AUPEC), Australasian Universities Power Engineering Conference, IEEE, Melbourne, Australia, pp. 1-6.View/Download from: UTS OPUS or Publisher's site
This paper presents a new solution to unit commitment for single-objective and multi-objective frameworks. In the first step, the total expected energy not supplied (TEENS) is proposed as a separate reliability objective function and at the next step, the multi-objective Pareto front strategy is implemented to simultaneously optimize the cost and reliability objective functions. Additionally, an integer based codification of initial solutions is added to reduce the dimension of ON/OFF status variables and also to eliminate the negative influence of penalty factor. The modified invasive weed optimization (MIWO) algorithm is also developed to optimally solve the proposed problem. The obtained solutions are compared with results in the literature which confirms the applicability and superiority of the proposed algorithm for a 10-unit system and 24-hour scheduling horizon.
Ghavidel, S, Ghadi, MJ, Azizivahed, A, Barani, M, Aghaei, J, Li, L & Zhang, J 2017, 'Hybrid Power Plant Bidding Strategy Including a Commercial Compressed Air Energy Storage Aggregator and a Wind Power Producer', 2017 Australasian Universities Power Engineering Conference (AUPEC), Australasian Universities Power Engineering Conference, IEEE, Melbourne, AUSTRALIA, pp. 1-6.View/Download from: UTS OPUS or Publisher's site
In this paper, a commercial compressed air energy storage (CAES) aggregator equipped with a simple cycle mode operation having the ability to work like a gas turbine is coordinated with a wind power aggregator (WPA) as a hybrid power plant to participate in electricity markets. In the proposed approach, the WPA uses the CAES to tackle its stochastic input and uncertainties related to different electricity market prices, and CAES can also use WPA to manage its charging/discharging and simple cycle modes more economically. A three-stage stochastic decision-making method is used to model the mentioned optimization problem which considers three electricity markets including day-ahead, intraday and balancing markets. The problem is formulated as a mixed integer linear programming which can be solved with available commercial solvers. Also, conditional value-at-risk is added to the problem to control the financial risk of the problem and offer different operation strategies for different financials risk levels. The proposed method can provide both bidding quantity and bidding curves to be submitted to the electricity markets which is tested on a realistic case study based on a wind farm and electricity market located in Spain. The results confirm that the proposed method can provide extra profit in joint operation, have more flexibility and reduce the financial risks
Azizivahed, A, Ghavidel, S, Barani, M, Jabbari Ghadi, M, Li, L & Zhang, J 2017, 'Hybrid power plant offering strategy to deal with the stochastic nature and outage of wind generators'.View/Download from: UTS OPUS
Ghoreishi, H, Afrakhte, H & Ghadi, MJ 2013, 'Optimal Placement of Tie Points and Sectionalizers in Radial Distribution Network in Presence of DGs Considering Load Significance', 2013 SMART GRID CONFERENCE (SGC'13), Smart Grid Conference (SGC), IEEE, Shahid Beheshti Univ, Abbaspour Coll Technol, Tehran, IRAN, pp. 160-165.
Gilani, SH, Afrakhte, H & Ghadi, MJ 2012, 'Probabilistic Method for Optimal Placement of Wind-based Distributed Generation with Considering Reliability Improvement and Power Loss Reduction', 2012 4TH CONFERENCE ON THERMAL POWER PLANTS (CTPP), 4th Conference on Thermal Power Plants (CTPP), IEEE, Power & Water Univ Technol, Tehran, IRAN.
Ghadi, MJ, Gilani, SH, Sharifiyan, A & Afrakhteh, H 2012, 'A new method for short-term wind power forecasting', 2012 Proceedings of 17th Conference on Electrical Power Distribution, EPDC 2012.
Utilization of wind power as renewable resources of energy has been growing rapidly around the world in the last decades. Wind power generation is fluctuating due to the variation of the wind speed. Therefore, the assessment of the output power of this type of generators is always associated with some amount of uncertainties. Accurate wind power forecasting can effectively support distribution and transmission system operators to improve power network control and management. This paper presents a new Imperialistic Competitive Algorithm-Neural Network (ICA-NN) method to enhance the short-term wind power prediction accuracy at a wind farm using information from Numerical Weather Prediction (NWP) and measured data from online SCADA. In this method, first, a prediction model of wind speed is built based on Multilayer Perceptron (MLP) artificial neural network considering environmental factors (i.e. wind speed, temperature, Humidity, geographical conditions and other factors) and then, Imperialist Competitive Algorithm is used to update weights of the neural network. The proposed method is capable to deal with jumping data; and is applicable in both wind speed and wind power forecasting. © 2012 Iranian Association of Elect.