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This paper introduces a novel evolutionary algorithm namely modified exchange market algorithm (MEMA) to solve the various problems in the power system operation topics such as combined heat and power economic emission dispatch (CHPEED), and multi-area economic dispatch (MAED) in the presence of renewable energy sources (RESs). The MEMA is a new version of the EMA that has been modified to improve its performance and to find a better solution with high robustness. To scrutiny the applicability of the MEMA, it is employed to solve the different problems with considering RESs. Also, to prove the scalability and evaluate the performance of the proposed method, several large and small scale test systems are used in each case. Also, three different approaches are considered to investigate the impacts of RESs. In the first approach, RESs are assumed to be a negative load, while in the latter case, their cost impacts on the problem are also considered. Finally, in the last approach, the uncertainty of RES and its stochastic nature are considered. In all test systems, the results obtained by the proposed method are compared with the results of other suggested techniques in this era. Also, the robustness of the proposed method is investigated using parametric and non-parametric statistical tests. The comparative and statistical analysis confirm the superiority and robustness of the proposed method over the other methods in all test systems.
Hossein Nourianfar; Hamdi Abdi. Solving power systems optimization problems in the presence of renewable energy sources using modified exchange market algorithm. Sustainable Energy, Grids and Networks 2021, 26, 100449 .
AMA StyleHossein Nourianfar, Hamdi Abdi. Solving power systems optimization problems in the presence of renewable energy sources using modified exchange market algorithm. Sustainable Energy, Grids and Networks. 2021; 26 ():100449.
Chicago/Turabian StyleHossein Nourianfar; Hamdi Abdi. 2021. "Solving power systems optimization problems in the presence of renewable energy sources using modified exchange market algorithm." Sustainable Energy, Grids and Networks 26, no. : 100449.
This study deals with the optimization of battery energy storage system (BESS) data in terms of significant characteristics of life and efficiency, and their positive impacts on power system efficiency in the presence of wind power plants in a microgrid. To this end, a permanent magnet synchronous generator (PMSG) is used to convert the wind energy by connecting a three-phase dynamic load to the grid. The main novelty of the proposed method is designing a smart backup battery branch to improve the efficiency of the wind farm by maintaining the operating constraints even during the occurrence of harsh faults in the generation section. Additionally, for the first time, the characteristics of the BESS are optimized using nine evolutionary algorithms, including the genetic algorithm (GA), teachingâlearning-based optimization (TLBO), particle swarm optimization (PSO), gravitational search algorithm (GSA), artificial bee colony (ABC), differential evolution (DE), grey wolf optimizer (GWO), mothâflame optimization algorithm (MFO), and sine cosine algorithm (SCA), and the results are compared with each other. The simulation results of a case study confirm the robustness of the proposed control strategy for the BESS.
Ramin Sakipour; Hamdi Abdi. Optimizing Battery Energy Storage System Data in the Presence of Wind Power Plants: A Comparative Study on Evolutionary Algorithms. Sustainability 2020, 12, 10257 .
AMA StyleRamin Sakipour, Hamdi Abdi. Optimizing Battery Energy Storage System Data in the Presence of Wind Power Plants: A Comparative Study on Evolutionary Algorithms. Sustainability. 2020; 12 (24):10257.
Chicago/Turabian StyleRamin Sakipour; Hamdi Abdi. 2020. "Optimizing Battery Energy Storage System Data in the Presence of Wind Power Plants: A Comparative Study on Evolutionary Algorithms." Sustainability 12, no. 24: 10257.
This paper offers a novel passive islanding detection scheme for synchronous distributed generations (SDGs) based on the combination of (dδ/dt) and (dPm/dt). By the occurrence of islanding and grid-connected events, the load angle (δ) and mechanical power (Pm) of SDGs will change. These signals will oscillate during non-islanding events, while they will reach a stable point without any oscillation during islanding events. However, during some grid-connected disturbances such as high impedance faults and small capacitor switching, the oscillations of δ and Pm may not be significant. Therefore, the swing feature cannot be a reliable index and can lead to an inappropriate operation. Hence, to select the best parameters, a combination of four parameters, including dPm/dt, dδ/dt, dPm/dδ, and dδ/dPm is employed. The sensitivity analysis to discriminate islanding from non-islanding events has been reported in the presence of SDGs with various inertia constants, voltage levels, and loads. Based on the results, the combination of dδ/dt and dPm/dt has high accuracy and speed in clustering the occurred events. The efficiency of the proposed approach is tested on the IEEE 33-bus system. It is proved that the proposed approach decreases the non-detection zone to 0.01 MW and 0.01 MVAr of the powers nonconformity.
Hamdi Abdi; Ali Rostami; Navid Rezaei. A Novel Passive Islanding Detection Scheme for Synchronous-type DG using Load Angle and Mechanical Power Parameters. Electric Power Systems Research 2020, 192, 106968 .
AMA StyleHamdi Abdi, Ali Rostami, Navid Rezaei. A Novel Passive Islanding Detection Scheme for Synchronous-type DG using Load Angle and Mechanical Power Parameters. Electric Power Systems Research. 2020; 192 ():106968.
Chicago/Turabian StyleHamdi Abdi; Ali Rostami; Navid Rezaei. 2020. "A Novel Passive Islanding Detection Scheme for Synchronous-type DG using Load Angle and Mechanical Power Parameters." Electric Power Systems Research 192, no. : 106968.
The unit commitment problem is one of the most significant and basic issues in the monitor, control, and operation of modern power systems, which has always been a subject of great concern to researchers and operators as the most extensive human-made system. Before restructuring, one of the main objectives of unit commitment problem was the minimization of the total operation cost of power plants subject to various constraints, including unit and network ones. As the privatization and restructuring process became more serious, the primary purpose of the unit commitment problem has been changed to maximizing the total profit, which led to the emergence of a new concept known as profit-based unit commitment problem. Accordingly, the maximization of the profit for generation companies, all over the studied period, is a top-priority direction. This paper presents a comprehensive overview of the profit-based unit commitment problem in restructured power systems by investigating the most important studies on this topic and providing a complete classification. It also outlines the challenges facing researchers in this field, offers new insights, and suggests future directions.
Hamdi Abdi. Profit-based unit commitment problem: A review of models, methods, challenges, and future directions. Renewable and Sustainable Energy Reviews 2020, 138, 110504 .
AMA StyleHamdi Abdi. Profit-based unit commitment problem: A review of models, methods, challenges, and future directions. Renewable and Sustainable Energy Reviews. 2020; 138 ():110504.
Chicago/Turabian StyleHamdi Abdi. 2020. "Profit-based unit commitment problem: A review of models, methods, challenges, and future directions." Renewable and Sustainable Energy Reviews 138, no. : 110504.
This article presents a novel linear programming (LP) based two-stage stochastic approach for microgrids (MGs) under uncertainties. In this regard, the day-ahead programming of dispatchable resources in MG was modeled, considering the uncertainties in demand loads, and upstream network electricity price. Moreover, the inherent stochastic nature of wind and solar resources as well as the environmental aspects were modeled to find a realistic solution. Also, real-time pricing (RTP), demand response (DR) program considering the energy storage system (ESS) as a DR option was implemented on an MG as a smart customer. The extensive form of the two-stage stochastic recourse model was properly implemented for dispatchable and non-dispatchable resources. Furthermore, scenario generation and reduction procedures were realized with autoregressive and moving average (ARMA) model-based time-series (TS), and backward reduction (BR) method by the Kantorovich distance (KD), respectively. The simulations on a grid-connected MG, including micro-turbine (MT), fuel-cell (FC), wind-turbine (WT), photovoltaic module (PV), and ESS were reported in operational cases based on one month of real recorded data for wind speed, solar irradiance, demand load, and upstream network electricity price. The results for the next day in real-time confirmed the accuracy of the developed optimization methodology.
Kamran Masoudi; Hamdi Abdi. Scenario-Based Two-Stage Stochastic Scheduling of Microgrid Considered as the Responsible Load. Electric Power Components and Systems 2020, 48, 1614 -1631.
AMA StyleKamran Masoudi, Hamdi Abdi. Scenario-Based Two-Stage Stochastic Scheduling of Microgrid Considered as the Responsible Load. Electric Power Components and Systems. 2020; 48 (14-15):1614-1631.
Chicago/Turabian StyleKamran Masoudi; Hamdi Abdi. 2020. "Scenario-Based Two-Stage Stochastic Scheduling of Microgrid Considered as the Responsible Load." Electric Power Components and Systems 48, no. 14-15: 1614-1631.
In this paper, a comprehensive model is proposed for long-term planning of various combined heat and power units in an integrated heat and electricity network. The proposed model takes into account the uncertain electric loads, and market price applying the information gap decision theory. Furthermore, the security of the power grid from the voltage stability viewpoint utilizing the L-index approach is considered. The model is based on the risk-averse multi-objective combined heat and power planning methodology, which maximizes the profit of combined heat and power owners and minimizes the system operator costs over the planning horizon in the presence of the environmental emissions cost. The best compromise solution is achieved via a fuzzy logic-based min-max method. The risk-averse strategy of Information gap decision theory is applied to the obtained solution, which demonstrates the impact of data uncertainty. The proposed mixed-integer non-linear programming model is solved using the general algebraic modeling system package and tested on the IEEE 14-bus standard system. The results indicate that the risk-averse strategy improves the robustness of the network against the uncertainty. Also solving the model using the multi-objective framework gives comprehensive results, and shows that the voltage stability constraints affect the planning decisions.
Saeed Yadegari; Hamdi Abdi; Saman Nikkhah. Risk-averse multi-objective optimal combined heat and power planning considering voltage security constraints. Energy 2020, 212, 118754 .
AMA StyleSaeed Yadegari, Hamdi Abdi, Saman Nikkhah. Risk-averse multi-objective optimal combined heat and power planning considering voltage security constraints. Energy. 2020; 212 ():118754.
Chicago/Turabian StyleSaeed Yadegari; Hamdi Abdi; Saman Nikkhah. 2020. "Risk-averse multi-objective optimal combined heat and power planning considering voltage security constraints." Energy 212, no. : 118754.
The main purpose of unit commitment problem (UCP) is to find the optimal operation cost (OOC) by considering power system constraints. In this paper, an uncertain UCP (UUCP) is proposed in the presence of energy storage systems (ESSs) via applying genetic algorithm-priority list-based (GA-PLB) strategy and considering operational constraints such as power balance, minimum down time (MDT), minimum up time (MUT), and spinning reserve (SR). To find the robust optimal scheduling due to modeling the uncertainty of renewable energy sources (RESs), the Taguchi orthogonal arrays technique (TOAT) is applied. Furthermore, to consider the environmental concerns, the emission produced by thermal power plants is modeled using the emission cost function as a term of the objective function. The proposed model is tested on the standard cases of IEEE 10, and 38 units and the results are reported. The obtained results confirm that the proposed approach can effectively reduce the OOC. One of the main advantages of the proposed method is the reduction in computational burden due to the modeled uncertainties in solving the UUCP compared to the other proposed methods based on iterations while improving the final solutions.
Hamid Reza Nikzad; Hamdi Abdi. A robust unit commitment based on GA-PL strategy by applying TOAT and considering emission costs and energy storage systems. Electric Power Systems Research 2019, 180, 106154 .
AMA StyleHamid Reza Nikzad, Hamdi Abdi. A robust unit commitment based on GA-PL strategy by applying TOAT and considering emission costs and energy storage systems. Electric Power Systems Research. 2019; 180 ():106154.
Chicago/Turabian StyleHamid Reza Nikzad; Hamdi Abdi. 2019. "A robust unit commitment based on GA-PL strategy by applying TOAT and considering emission costs and energy storage systems." Electric Power Systems Research 180, no. : 106154.
In this study, the Fast Non-Dominated Time-Varying Acceleration Coefficient-Particle Swarm Optimization (TVAC-PSO) combined with Exchange Market Algorithm (EMA) is proposed to solve the economic emission dispatch problems consisting of Combined Heat and Power Economic Emission Dispatch (CHPEED) and Dynamic Economic Emission Dispatch (DEED) multi-objective optimization problems considering operational constraints. A two-stage approach has been used to select the Best Compromise Solutions (BCSs), as the best solution which minimizes the operational cost and emission, simultaneously. For this purpose, at the first stage, applying Fuzzy Clustering Mean (FCM), the obtained Pareto Optimal Front (POF) is divided into several separated clusters. Then, using the Technique for Order of Performance by Similarity to Ideal Solution (TOPSIS), a single BCS is selected among each cluster. At first, the superiority of the proposed algorithm is evaluated on a number of benchmark functions, as well as the 48-unit CHPED test case. Then, to demonstrate the ability of the proposed algorithm in solving the multi-objective problem by finding the POF, the presented method has been applied to three case cases, and the results are compared with other algorithms in this field. Furthermore, a new test case is presented to confirm the proposed algorithmâs performance. The results verify the proposed methodâs superiority over other available techniques in the literature. One of the most important novelty of this study is solving a multi-objective DEED problem considering the Ramp Rate Limits (RRLs), Valve Point Loading Effect (VPLE), power transmission loss impact, Spinning Reserve Requirements (SRRs), Prohibited Operating Zones (POZs) and Multiple Fuel Units (MFUs) simultaneously, for the first time.
Hossein Nourianfar; Hamdi Abdi. Solving the multi-objective economic emission dispatch problems using Fast Non-Dominated Sorting TVAC-PSO combined with EMA. Applied Soft Computing 2019, 85, 105770 .
AMA StyleHossein Nourianfar, Hamdi Abdi. Solving the multi-objective economic emission dispatch problems using Fast Non-Dominated Sorting TVAC-PSO combined with EMA. Applied Soft Computing. 2019; 85 ():105770.
Chicago/Turabian StyleHossein Nourianfar; Hamdi Abdi. 2019. "Solving the multi-objective economic emission dispatch problems using Fast Non-Dominated Sorting TVAC-PSO combined with EMA." Applied Soft Computing 85, no. : 105770.
The aim of transmission network expansion planning (TNEP) is providing enough capacity to transfer power from generation section to load centers in a reliable and economically efficient manner. The mission of this problem is identifying where, when, and what type of new transmission lines should be installed in transmission network. In this chapter, the robust TNEP (RTNEP) in the presence of two major uncertainties in power systems (loads and wind power generation) is studied. The robust methods of (a) information-gap decision theory (IGDT), (b) Taguchiâs orthogonal array testing (TOAT), and (c) scenario technique criteria (min-max regret criterion) are proposed and simulated here. Using each of these methods, the robust expansion plan for the modified 6-bus Garver transmission network test system is calculated. The obtained results verify the validity of the mentioned methods in RTNEP. These methods can easily be implemented on any large- and real-scale power system. Furthermore, different uncertainty types can be easily considered in this regard.
Shahriar Abbasi; Hamdi Abdi. Robust Transmission Network Expansion Planning (IGDT, TOAT, Scenario Technique Criteria). Robust Optimal Planning and Operation of Electrical Energy Systems 2019, 199 -218.
AMA StyleShahriar Abbasi, Hamdi Abdi. Robust Transmission Network Expansion Planning (IGDT, TOAT, Scenario Technique Criteria). Robust Optimal Planning and Operation of Electrical Energy Systems. 2019; ():199-218.
Chicago/Turabian StyleShahriar Abbasi; Hamdi Abdi. 2019. "Robust Transmission Network Expansion Planning (IGDT, TOAT, Scenario Technique Criteria)." Robust Optimal Planning and Operation of Electrical Energy Systems , no. : 199-218.
The purpose of this chapter is investigating the unit commitment problem (UCP) in the presence of renewable energy sources (RESs), energy storage systems (ESSs), and modeling the uncertainties arising in this regard. To achieve this goal, the following subjects are presented in detail. The classic UC formulations, the uncertaintiesâ impacts on this problem, and the new research efforts in this regard are addressed. Also, the existing optimization methods applied to solve the UCP such as robust optimization (RO), information gap decision theory (IGDT), and Taguchi orthogonal array technique (TOAT) as well as their advantages and drawbacks are described in the next section. Also, the application techniques for modeling the renewable energy sources and energy storage systems are detailed. Various models of UC problem such as thermal power plants and thermal power plants combined with RESs and ESSs considering the most important uncertainties in the inactive networks are presented. The proposed models have been tested on standard case of IEEE, 10 units, and the results are presented.
Hamid Reza Nikzad; Hamdi Abdi; Shahriar Abbasi. Robust Unit Commitment Applying Information Gap Decision Theory and Taguchi Orthogonal Array Technique. Robust Optimal Planning and Operation of Electrical Energy Systems 2019, 109 -129.
AMA StyleHamid Reza Nikzad, Hamdi Abdi, Shahriar Abbasi. Robust Unit Commitment Applying Information Gap Decision Theory and Taguchi Orthogonal Array Technique. Robust Optimal Planning and Operation of Electrical Energy Systems. 2019; ():109-129.
Chicago/Turabian StyleHamid Reza Nikzad; Hamdi Abdi; Shahriar Abbasi. 2019. "Robust Unit Commitment Applying Information Gap Decision Theory and Taguchi Orthogonal Array Technique." Robust Optimal Planning and Operation of Electrical Energy Systems , no. : 109-129.
Shahriar Abbasi; Hamdi Abdi; Sergio Bruno; Massimo La Scala. Transmission network expansion planning considering load correlation using unscented transformation. International Journal of Electrical Power & Energy Systems 2018, 103, 12 -20.
AMA StyleShahriar Abbasi, Hamdi Abdi, Sergio Bruno, Massimo La Scala. Transmission network expansion planning considering load correlation using unscented transformation. International Journal of Electrical Power & Energy Systems. 2018; 103 ():12-20.
Chicago/Turabian StyleShahriar Abbasi; Hamdi Abdi; Sergio Bruno; Massimo La Scala. 2018. "Transmission network expansion planning considering load correlation using unscented transformation." International Journal of Electrical Power & Energy Systems 103, no. : 12-20.
The economic dispatch (ED) is one of the most important short-term problems in power systems, and solving it quickly is essential. However, classical optimization tools are often too computationally demanding to be considered satisfactory. This has motivated the application of metaheuristic methods, which offer a good compromise in terms of solution quality and computation time. However, these methods have been applied in an isolated way and on different problem definitions and case studies, so that there were no clear insights on how they compared to each other. This paper fills this gap by performing an objective comparison of six metaheuristics solving the ED in several case studies under different conditions. Although mixed-integer programming performs best for small case studies, our results confirm that metaheuristics are able to efficiently solve the ED problem. Genetic algorithms emerge as the best performers in terms of solution quality and computation time, followed by PSO and TLBO.
Hamdi Abdi; Hamid Fattahi; Sara Lumbreras. What metaheuristic solves the economic dispatch faster? A comparative case study. Electrical Engineering 2018, 100, 2825 -2837.
AMA StyleHamdi Abdi, Hamid Fattahi, Sara Lumbreras. What metaheuristic solves the economic dispatch faster? A comparative case study. Electrical Engineering. 2018; 100 (4):2825-2837.
Chicago/Turabian StyleHamdi Abdi; Hamid Fattahi; Sara Lumbreras. 2018. "What metaheuristic solves the economic dispatch faster? A comparative case study." Electrical Engineering 100, no. 4: 2825-2837.
Energy storage systems (ESSs) play a major role in power system planning and operation. As evolution of the storage technologies continues, planners will be regarded the ESSs in future power systems more than ever. Simultaneous determination of size and site of ESSs is a non-deterministic, and non-convex problem, which should take into account the uncertain nature of today's power systems. This paper, for the first time, investigates uncertain optimal allocation of ESSs considering practical constraints, including prohibited zones, and ramp rate, as well as simultaneous reduction of three different and incompatible objective functions of operation cost, voltage deviation, and air emission. Due to complexity of the problem, two multi-objective hybrid algorithms called MOGSA and MOPSO-NSGA_II were proposed. Furthermore, five-point estimation method is utilized in order to model the wind power uncertain nature. The simulation results on IEEE 30-bus test system are detailed. To increase the accuracy and ensure selection of the best solution from among the set of optimal solutions, the multi-criteria decision-making techniques (TOPSIS) are used. The simulation results clearly show the efficiency and effectiveness of the proposed method.
Vahid Jani; Hamdi Abdi. Optimal allocation of energy storage systems considering wind power uncertainty. Journal of Energy Storage 2018, 20, 244 -253.
AMA StyleVahid Jani, Hamdi Abdi. Optimal allocation of energy storage systems considering wind power uncertainty. Journal of Energy Storage. 2018; 20 ():244-253.
Chicago/Turabian StyleVahid Jani; Hamdi Abdi. 2018. "Optimal allocation of energy storage systems considering wind power uncertainty." Journal of Energy Storage 20, no. : 244-253.
This paper presents a novel evolutionary optimization algorithm namely the modified crow search algorithm (MCSA) for solving the non-convex economic load dispatch (ELD) problem which improves the crow search algorithm (CSA) by an innovative selection of the crows and adaptive adjustment of the flight length. MCSA is a population-based technique based on the intelligent behavior of the crows in finding food sources. In MCSA, each crow saves its food in hiding-places for the time it needs. Also, each crow searches environment to find the better foods by stealthily following other crows to discover their hiding-places. The proposed MCSA develops the search capability of crows in the original CSA and introduces a new way by which a destination is selected by a crow to follow. To indicate the applicability of MCSA in the ELD problem, it is applied on three different well-known test systems. The results are compared in terms of the solution quality, robustness, and computing time with other methods implying that the proposed method has a superior performance than the other techniques.
Farid Mohammadi; Hamdi Abdi. A modified crow search algorithm (MCSA) for solving economic load dispatch problem. Applied Soft Computing 2018, 71, 51 -65.
AMA StyleFarid Mohammadi, Hamdi Abdi. A modified crow search algorithm (MCSA) for solving economic load dispatch problem. Applied Soft Computing. 2018; 71 ():51-65.
Chicago/Turabian StyleFarid Mohammadi; Hamdi Abdi. 2018. "A modified crow search algorithm (MCSA) for solving economic load dispatch problem." Applied Soft Computing 71, no. : 51-65.
This paper proposes a control strategy to improve adaptive virtual impedance performance. The proposed method compensates voltage drop across the output impedances of distributed generation (DG) units, intensified due to the use of virtual impedance, and regulates the voltage of busses feeding microgridâconnected loads in a desired level. In addition, it preserves the advantages of adaptive virtual impedance strategy, including proper active and reactive power sharing and decreased circulating current among parallel DGs. To compensate voltage drop proportional to load variation, the reference voltage used in the droop control strategy is adaptively regulated. To evaluate the performance and efficacy of the proposed control strategy, it was implemented on an islanded microgrid with two DGs. Simulation results show that the proposed strategy makes a compromise between control objectives, including appropriate power sharing, compensation of voltage drop, and reduction of circulating current.
Kiomars Sabzevari; Shahram Karimi; Farshad Khosravi; Hamdi Abdi. Modified droop control for improving adaptive virtual impedance strategy for parallel distributed generation units in islanded microgrids. International Transactions on Electrical Energy Systems 2018, 29, e2689 .
AMA StyleKiomars Sabzevari, Shahram Karimi, Farshad Khosravi, Hamdi Abdi. Modified droop control for improving adaptive virtual impedance strategy for parallel distributed generation units in islanded microgrids. International Transactions on Electrical Energy Systems. 2018; 29 (1):e2689.
Chicago/Turabian StyleKiomars Sabzevari; Shahram Karimi; Farshad Khosravi; Hamdi Abdi. 2018. "Modified droop control for improving adaptive virtual impedance strategy for parallel distributed generation units in islanded microgrids." International Transactions on Electrical Energy Systems 29, no. 1: e2689.
Environmental concerns have caused application of renewable energy sources as a clean appropriated alternative for conventional thermal generators in power system planning. These uncertain and variable sources have challenged the power system scheduling to ensure that it still remains reliable and economical. Also, plug-in electric vehicles may potentially alter load demand in uncontrolled charging mode and impose new challenges to the unit commitment problem. This paper demonstrates the fast heuristic method based on priority list to solve the stochastic unit commitment problem and applied it to a basic 10-unit case study system accompaniment with an electric vehicle parking lot, a wind farm and a solar farm over a 24-h time horizon. As it is reported, scenario generation with Monte Carlo simulation can relatively compensate for the intermittent behavior of renewable energy sources and result in more economical and robust planning. Moreover, the integration of plug-in electric vehicles and their controlled charging/discharging is considered. Thermal units are scheduled by priority list method with their relevant constraints such as minimum up/down time, spinning reserve, load demand and capacity limitation. The simulations results verify that the penetration of the mentioned renewable energy sources can improve the operational cost and computation time, effectively.
Maryam Shahbazitabar; Hamdi Abdi. A novel priority-based stochastic unit commitment considering renewable energy sources and parking lot cooperation. Energy 2018, 161, 308 -324.
AMA StyleMaryam Shahbazitabar, Hamdi Abdi. A novel priority-based stochastic unit commitment considering renewable energy sources and parking lot cooperation. Energy. 2018; 161 ():308-324.
Chicago/Turabian StyleMaryam Shahbazitabar; Hamdi Abdi. 2018. "A novel priority-based stochastic unit commitment considering renewable energy sources and parking lot cooperation." Energy 161, no. : 308-324.
Soheil Derafshi Beigvand; Hamdi Abdi; Massimo La Scala. Economic dispatch of multiple energy carriers. Energy 2017, 138, 861 -872.
AMA StyleSoheil Derafshi Beigvand, Hamdi Abdi, Massimo La Scala. Economic dispatch of multiple energy carriers. Energy. 2017; 138 ():861-872.
Chicago/Turabian StyleSoheil Derafshi Beigvand; Hamdi Abdi; Massimo La Scala. 2017. "Economic dispatch of multiple energy carriers." Energy 138, no. : 861-872.
Stability studies in distribution smart grids (SGs) are very important due to the increased penetration of distributed energy resource making bidirectional flow of electric power. This paper presents two novel fast voltage stability indices (VSIs) applicable to the radial distribution SGs based on the real-time measured voltage data only, obtained by the SG infrastructures such as smart metering device. Each VSI depends on the system bus voltages and does not need additional calculations such as power flow solution, converting the actual distribution network into two-bus equivalent system etc. In other words, for a radial system with n -node, the voltage stability analysis is carried out by parallel n -microprocessor units independently. The concentrator units can collect and evaluate the computational results and decide about control actions directly or indirectly depending on the system requirements. The accuracy and efficiency of the proposed indicators are tested on a 41-node radial distribution system. Six load models including constant and composite types have been considered, and also SG in various states of operation and four types of distributed generation are analysed. The comparison results demonstrate that the proposed VSIs are fast and effective to identify the most sensitive node of the radial grids to the voltage collapse based on the smart infrastructures.
Soheil Derafshi Beigvand; Hamdi Abdi; Sri Niwas Singh. Voltage stability analysis in radial smart distribution grids. IET Generation, Transmission & Distribution 2017, 11, 3722 -3730.
AMA StyleSoheil Derafshi Beigvand, Hamdi Abdi, Sri Niwas Singh. Voltage stability analysis in radial smart distribution grids. IET Generation, Transmission & Distribution. 2017; 11 (15):3722-3730.
Chicago/Turabian StyleSoheil Derafshi Beigvand; Hamdi Abdi; Sri Niwas Singh. 2017. "Voltage stability analysis in radial smart distribution grids." IET Generation, Transmission & Distribution 11, no. 15: 3722-3730.
Soheil Derafshi Beigvand; Hamdi Abdi; Massimo La Scala. Hybrid Gravitational Search Algorithm-Particle Swarm Optimization with Time Varying Acceleration Coefficients for large scale CHPED problem. Energy 2017, 126, 841 -853.
AMA StyleSoheil Derafshi Beigvand, Hamdi Abdi, Massimo La Scala. Hybrid Gravitational Search Algorithm-Particle Swarm Optimization with Time Varying Acceleration Coefficients for large scale CHPED problem. Energy. 2017; 126 ():841-853.
Chicago/Turabian StyleSoheil Derafshi Beigvand; Hamdi Abdi; Massimo La Scala. 2017. "Hybrid Gravitational Search Algorithm-Particle Swarm Optimization with Time Varying Acceleration Coefficients for large scale CHPED problem." Energy 126, no. : 841-853.
Soheil Derafshi Beigvand; Hamdi Abdi; Massimo La Scala. Multicarrier Energy System Optimal Power Flow. From Smart Grids to Smart Cities 2017, 273 -307.
AMA StyleSoheil Derafshi Beigvand, Hamdi Abdi, Massimo La Scala. Multicarrier Energy System Optimal Power Flow. From Smart Grids to Smart Cities. 2017; ():273-307.
Chicago/Turabian StyleSoheil Derafshi Beigvand; Hamdi Abdi; Massimo La Scala. 2017. "Multicarrier Energy System Optimal Power Flow." From Smart Grids to Smart Cities , no. : 273-307.