1,372 research outputs found

    Large Scale Integration of Electric Vehicles into the Power Grid and Its Potential Effects on Power System Reliability

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    In this thesis, the potential effects of large scale integration of electric vehicles into the power grid are discussed in both the beneficial and detrimental aspects. The literature review gives a comprehensive introduction about the existing smart charging algorithms. According to the system structure and market mechanism, the smart charging algorithms can be divided into centralized and distributed method. With the knowledge of driving patterns and charging characteristics of electric vehicles, both the centralized and decentralized smart charging algorithms are studied in this research. Based on the smart charging pricing and sequential price update mechanism, a multi-agent based distributed smart charging algorithm is used in this research to flatten the load curve and therefore mitigate the potential detrimental effects caused by uncoordinated charging. Each EV agent has some extent of intelligence to solve its own charging scheduling problem. The optimization method used in this research is the binary hybrid GSA-PSO algorithm, which combines the merits of particle swarm optimization (PSO) and gravitational search algorithm (GSA), and has very good exploration and exploitation abilities. A V2G enabled centralized smart charging algorithm is also introduced in this thesis, each EV can earn revenues by discharging power into the grid. The dominant search matrix is used to resolve the \u27\u27curse of dimensionality\u27\u27 problem existing in the centralized optimization problems. Numerical case studies show both the distributed and V2G enabled smart charging algorithms can effectively transfer the charging load from the peak load period to the load valley hours. Because of the limited integration ratio of electric vehicles, most power system reliability methods do not evaluate the charging load of EVs separately in their analytical procedures. However, with a fast increasing integration level, the potential effects of large scale integration of EVs on the power system reliability should be comprehensively evaluated. The effects of EV charging on power system reliability in the planning phase is analyzed in this research based on the RBTS. The results show the uncontrolled charging will deteriorate the reliability level while the smart charging can effectively decrease the detrimental effect. The potential application of aggregated EV providing operating reserve to the grid as a kind of ancillary service is also discussed, and the related effects on power system reliability in operating phase are calculated using the modified PJM method. The case study shows the unit commitment risk of the system can decrease to a very low level with the additional operating reserve capacity provided by aggregated EVs, which can not only improve the system\u27s reliability level but also save the cost

    An efficient energy management in office using bio-inspired energy optimization algorithms

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    Energy is one of the valuable resources in this biosphere. However, with the rapid increase of the population and increasing dependency on the daily use of energy due to smart technologies and the Internet of Things (IoT), the existing resources are becoming scarce. Therefore, to have an optimum usage of the existing energy resources on the consumer side, new techniques and algorithms are being discovered and used in the energy optimization process in the smart grid (SG). In SG, because of the possibility of bi-directional power flow and communication between the utility and consumers, an active and optimized energy scheduling technique is essential, which minimizes the end-user electricity bill, reduces the peak-to-average power ratio (PAR) and reduces the frequency of interruptions. Because of the varying nature of the power consumption patterns of consumers, optimized scheduling of energy consumption is a challenging task. For the maximum benefit of both the utility and consumers, to decide whether to store, buy or sale extra energy, such active environmental features must also be taken into consideration. This paper presents two bio-inspired energy optimization techniques; the grasshopper optimization algorithm (GOA) and bacterial foraging algorithm (BFA), for power scheduling in a single office. It is clear from the simulation results that the consumer electricity bill can be reduced by more than 34.69% and 37.47%, while PAR has a reduction of 56.20% and 20.87% with GOA and BFA scheduling, respectively, as compared to unscheduled energy consumption with the day-ahead pricing (DAP) scheme

    Closed-loop elastic demand control under dynamic pricing program in smart microgrid using super twisting sliding mode controller

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    Electricity demand is rising due to industrialisation, population growth and economic development. To meet this rising electricity demand, towns are renovated by smart cities, where the internet of things enabled devices, communication technologies, dynamic pricing servers and renewable energy sources are integrated. Internet of things (IoT) refers to scenarios where network connectivity and computing capability is extended to objects, sensors and other items not normally considered computers. IoT allows these devices to generate, exchange and consume data without or with minimum human intervention. This integrated environment of smart cities maintains a balance between demand and supply. In this work, we proposed a closed-loop super twisting sliding mode controller (STSMC) to handle the uncertain and fluctuating load to maintain the balance between demand and supply persistently. Demand-side load management (DSLM) consists of agents-based demand response (DR) programs that are designed to control, change and shift the load usage pattern according to the price of the energy of a smart grid community. In smart grids, evolved DR programs are implemented which facilitate controlling of consumer demand by effective regulation services. The DSLM under price-based DR programs perform load shifting, peak clipping and valley filling to maintain the balance between demand and supply. We demonstrate a theoretical control approach for persistent demand control by dynamic price-based closed-loop STSMC. A renewable energy integrated microgrid scenario is discussed numerically to show that the demand of consumers can be controlled through STSMC, which regulates the electricity price to the DSLM agents of the smart grid community. The overall demand elasticity of the current study is represented by a first-order dynamic price generation model having a piece-wise linear price-based DR program. The simulation environment for this whole scenario is developed in MATLAB/Simulink. The simulations validate that the closed-loop price-based elastic demand control technique can trace down the generation of a renewable energy integrated microgrid

    Heuristic-based programable controller for efficient energy management under renewable energy sources and energy storage system in smart grid

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    An operative and versatile household energy management system is proposed to develop and implement demand response (DR) projects. These are under the hybrid generation of the energy storage system (ESS), photovoltaic (PV), and electric vehicles (EVs) in the smart grid (SG). Existing household energy management systems cannot offer its users a choice to ensure user comfort (UC) and not provide a sustainable solution in terms of reduced carbon emission. To tackle these problems, this research work proposes a heuristic-based programmable energy management controller (HPEMC) to manage the energy consumption in residential buildings to minimize electricity bills, reduce carbon emissions, maximize UC and reduce the peak-to-average ratio (PAR). We used our proposed hybrid genetic particle swarm optimization (HGPO) algorithm and existing algorithms like a genetic algorithm (GA), binary particle swarm optimization algorithm (BPSO), ant colony optimization (ACO), wind-driven optimization algorithm (WDO), bacterial foraging algorithm (BFA) to schedule smart appliances optimally to attain our desired objectives. In the proposed model, consumers use solar panels to produce their energy from microgrids. We also perform MATLAB simulations to validate our proposed HGPO-HPEMC (HHPEMC), and results confirm the efficiency and productivity of our proposed HPEMC based strategy. The proposed algorithm reduced the electricity cost by 25.55%, PAR by 36.98%, and carbon emission by 24.02% as compared to the case of without scheduling

    Reliability Constrained Unit Commitment Considering the Effect of DG and DR Program

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    Due to increase in energy prices at peak periods and increase in fuel cost, involving Distributed Generation (DG) and consumption management by Demand Response (DR) will be unavoidable options for optimal system operations. Also, with high penetration of DGs and DR programs into power system operation, the reliability criterion is taken into account as one of the most important concerns of system operators in management of power system. In this paper, a Reliability Constrained Unit Commitment (RCUC) at presence of time-based DR program and DGs integrated with conventional units is proposed and executed to reach a reliable and economic operation. Designated cost function has been minimized considering reliability constraint in prevailing UC formulation. The UC scheduling is accomplished in short-term so that the reliability is maintained in acceptable level. Because of complex nature of RCUC problem and full AC load flow constraints, the hybrid algorithm included Simulated Annealing (SA) and Binary Particle Swarm Optimization (BPSO) has been proposed to optimize the problem. Numerical results demonstrate the effectiveness of the proposed method and considerable efficacy of the time-based DR program in reducing operational costs by implementing it on IEEE-RTS79

    Energy management for user’s thermal and power needs:A survey

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    The increasing world energy consumption, the diversity in energy sources, and the pressing environmental goals have made the energy supply–demand balance a major challenge. Additionally, as reducing energy costs is a crucial target in the short term, while sustainability is essential in the long term, the challenge is twofold and contains clashing goals. A more sustainable system and end-users’ behavior can be promoted by offering economic incentives to manage energy use, while saving on energy bills. In this paper, we survey the state-of-the-art in energy management systems for operation scheduling of distributed energy resources and satisfying end-user’s electrical and thermal demands. We address questions such as: how can the energy management problem be formulated? Which are the most common optimization methods and how to deal with forecast uncertainties? Quantitatively, what kind of improvements can be obtained? We provide a novel overview of concepts, models, techniques, and potential economic and emission savings to enhance energy management systems design

    Exploiting multi-verse optimization and sine-cosine algorithms for energy management in smart cities

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    [EN] Due to the rapid increase in human population, the use of energy in daily life is increasing day by day. One solution is to increase the power generation in the same ratio as the human population increase. However, that is usually not possible practically. Thus, in order to use the existing resources of energy efficiently, smart grids play a significant role. They minimize electricity consumption and their resultant cost through demand side management (DSM). Universities and similar organizations consume a significant portion of the total generated energy; therefore, in this work, using DSM, we scheduled different appliances of a university campus to reduce the consumed energy cost and the probable peak to average power ratio. We have proposed two nature-inspired algorithms, namely, the multi-verse optimization (MVO) algorithm and the sine-cosine algorithm (SCA), to solve the energy optimization problem. The proposed schemes are implemented on a university campus load, which is divided into two portions, morning session and evening session. Both sessions contain different shiftable and non-shiftable appliances. After scheduling of shiftable appliances using both MVO and SCA techniques, the simulations showed very useful results in terms of energy cost and peak to average ratio reduction, maintaining the desired threshold level between electricity cost and user waiting timeUllah, B.; Hussain, I.; Uthansakul, P.; Riaz, M.; Khan, MN.; Lloret, J. (2020). Exploiting multi-verse optimization and sine-cosine algorithms for energy management in smart cities. Applied Sciences. 10(6):1-21. https://doi.org/10.3390/app1006209512110

    Recent techniques used in home energy management systems: a review

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    Power systems are going through a transition period. Consumers want more active participation in electric system management, namely assuming the role of producers–consumers, prosumers in short. The prosumers’ energy production is heavily based on renewable energy sources, which, besides recognized environmental benefits, entails energy management challenges. For instance, energy consumption of appliances in a home can lead to misleading patterns. Another challenge is related to energy costs since inefficient systems or unbalanced energy control may represent economic loss to the prosumer. The so-called home energy management systems (HEMS) emerge as a solution. When well-designed HEMS allow prosumers to reach higher levels of energy management, this ensures optimal management of assets and appliances. This paper aims to present a comprehensive systematic review of the literature on optimization techniques recently used in the development of HEMS, also taking into account the key factors that can influence the development of HEMS at a technical and computational level. The systematic review covers the period 2018–2021. As a result of the review, the major developments in the field of HEMS in recent years are presented in an integrated manner. In addition, the techniques are divided into four broad categories: traditional techniques, model predictive control, heuristics and metaheuristics, and other techniques.info:eu-repo/semantics/publishedVersio

    Demand-side management in industrial sector:A review of heavy industries

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