Dynamic economic emission dispatch with load dema nd management for the load demand of electric vehicles during crest shaving and valley filling in smart cities environment

Bahrani, Loau Tawfak Al; Horan, Ben; Seyedmahmoudian, Mehdi; Stojcevski, Alex

doi:10.1016/j.energy.2020.116946

Cited by 36 publications

(24 citation statements)

References 63 publications

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“…The OC (C 1 ) of the MG is expressed: (10) where C Fuel , C OM , C GRID , C LS , and M are fuel cost; O&M cost; interaction cost between MG and power grid (PG); compensation cost for interruptible load; grid connection/ disconnection indication respectively. M = 1 indicates MG is connected with PG, while M = 0 shows a standalone mode of the MG.…”

Section: Modeling Of Multi-objective Cost Functions a Cost Funcmentioning

confidence: 99%

“…Many researchers have chosen multi-objective function with consideration of different objectives, which include operating costs along with the emission and environmental considerations with the help of various dispatch algorithms mentioned in [9], [10]. Many kinds of researches have been carried out about the ED as mentioned above problems which include minimum fuel cost, low SO2 emissions, low NOx emissions in [11].…”

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confidence: 99%

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Energy Cost Optimization of Hybrid Renewables Based V2G Microgrid Considering Multi Objective Function by Using Artificial Bee Colony Optimization

et al. 2020

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The worldwide demand for reduction of CO2 pollution, with more penetration of renewable energy sources and an increased number of electric vehicles (EVs), demonstrates the importance of economic dispatch (ED) with taking into account the reduction of CO2 emission. ED is a classical problem in which EVs impose more penetration as a dynamic load, and its impact as vehicle-to-grid (V2G) is the possible future trend with cost minimization. Based on the integration of EVs and hybrid renewable sources concerning both economic dispatch and pollution minimization, the multi-objective function is converted into a single comprehensive objective by using the judgment matrix methodology. In this paper, the investigation involves the minimization of the cost of all three objectives viz. operation cost, pollution cost, and carbon emissions with ED by incorporating V2G technology. The algorithms which include particle swarm optimization, as well as artificial bee colony, are applied under various operation and control strategies. The proposed models are verified and analyzed with different case studies. In terms of operation economics, the simulation results validate the superior performance of EVs based microgrid (MG) model in the coordinated charging and discharging mode. Further, the comparison of both algorithms shows better results with the ABC algorithm in terms of cost minimization of all objectives. ABC is better in V2G based microgrid with coordinated charging and discharging mode while its performance is significant during a large number of EVs (i.e., 700 EVs). Moreover, the load shedding scenarios are integrated which enables the MG system to operate in dual mode (i.e., seamless transition). In this paper, the main contribution involves penetration of EVs as dynamic load and its V2G impact in a coordinated or uncoordinated way, application of ABC algorithm for this particular load problem with improved results, and inclusion of short-term load shedding scenarios. INDEX TERMS Vehicle-to-grid (V2G), electrical vehicles (EVs), particle swarm optimization (PSO), artificial bee colony (ABC), dynamic ED, operating cost (OC), pollutant treatment cost (PTC), carbon dioxide emission (CE). NOMENCLATURE ABBREVIATIONS PG Power Grid WT Wind Turbine MT Micro Gas Turbine The associate editor coordinating the review of this manuscript and approving it for publication was Yanbo Chen .

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Section: Modeling Of Multi-objective Cost Functions a Cost Funcmentioning

confidence: 99%

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confidence: 99%

Energy Cost Optimization of Hybrid Renewables Based V2G Microgrid Considering Multi Objective Function by Using Artificial Bee Colony Optimization

et al. 2020

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“…The optimization techniques are the focal point of methodologies related to EV problems (for example see [1]- [13]). Some of the most frequently used optimization techniques in literature addressing various EV related problems include Monte Carlo simulation [3]- [4], Grasshooper optimization algorithm [5], particle swarm optimization (PSO) [6]- [7], CPLEX [8]- [9], genetic algorithm [10], activeset algorithm [11], and K-means cluster [12]. The Markov and Monte Carlo simulation are used in [3] and [4] to model the urban driving cycle and to simulate the EV travel patterns and charging demand, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…In [6], a multi-objective PSO and geographic information system are used for the planning of CSs with a focus on the underlying economic impacts. PSO is also used in [7] for dynamic economic emission dispatch with load demand management where a large penetration during crest-and-valley is considered. The CPLEX solver is used in [8] to construct an integer linear program in order to address the EV salesman problem constrained to a predefined windows of waiting times.…”

Section: Introductionmentioning

confidence: 99%

Robust Placement and Sizing of Charging Stations From a Novel Graph Theoretic Perspective

et al. 2020

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This paper proposes analytical approaches to extend the capacity of existing networks of electric vehicles (EVs) by placement of additional charging stations (CSs) as well as determining the sizes of existing and new CSs in order to handle future expansions of EVs. The EV flow at CSs is modeled by a graph where nodes are potential locations for CSs and edges are uncertain parameters representing the variable EV flow at CSs. The required extra CS locations are explored by transforming the CS placement problem into a controllability framework addressed by maximum matching principle (MMP). To find the sizes of each CS, the graph of CS network is partitioned featuring only one CS in each subgraph. The size of CS in each subgraph is then determined by transforming the problem into the problem of robust stability of a system with uncertain parameters where each parameter is associated with an edge of subgraph. The zero exclusion principle is then tested for the related Kharitonov rectangles and polygonal polynomials of closed loop system with selected feedback gain as CS capacity. The proposed analytical approach is tested on the existing Tesla CS Network of Sydney. The locations of extra required CSs as well as the sizes of existing and new CSs are determined to maintain the waiting times at all stations below the threshold level.

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“…The works presented so far do not try to handle the peak demand. In [42]- [45], DED including PEVs is studied by considering various charging and discharging profiles. The method presented in [46] solved the problem of DED in presence of EVs.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Economic Dispatch Incorporating Commercial Electric Vehicles and Fluctuating Energy Sources

et al. 2020

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Increase in utilization of electric vehicles and penetration of Renewable Energy Sources (RES) creates a greater impact on traditional power systems. Intermittent output from renewable energy resources and uneven usage pattern of electric vehicles creates a greater impact on the economic operation of power system. In this paper, a stochastic Dynamic Economic Dispatch (DED) problem incorporating Commercial Electric Vehicles (CEVs) and intermittent renewable energy resources is addressed. The charging pattern of CEVs as well as intermittency of solar and wind resources creates a significant impact on power system peak demand. The behaviour of these CEVs and RES in various seasons is considered and the same is tested on a real time south Indian practical test system. An attempt is made to improve the swarm-based Moth Flame Optimization (MFO), named as IMFO is developed and utilized for solving the considered problem. The efficacy of the said method is tested and necessary validations are carried out in this article.

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Dynamic economic emission dispatch with load dema nd management for the load demand of electric vehicles during crest shaving and valley filling in smart cities environment

Cited by 36 publications

References 63 publications

Energy Cost Optimization of Hybrid Renewables Based V2G Microgrid Considering Multi Objective Function by Using Artificial Bee Colony Optimization

Energy Cost Optimization of Hybrid Renewables Based V2G Microgrid Considering Multi Objective Function by Using Artificial Bee Colony Optimization

Robust Placement and Sizing of Charging Stations From a Novel Graph Theoretic Perspective

Stochastic Economic Dispatch Incorporating Commercial Electric Vehicles and Fluctuating Energy Sources

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