The Experts below are selected from a list of 4518 Experts worldwide ranked by ideXlab platform
David C Lineweber - One of the best experts on this subject based on the ideXlab platform.
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understanding Residential Customer support for and opposition to smart grid investments
The Electricity Journal, 2011Co-Authors: David C LineweberAbstract:Consumer research data suggest that the industry needs to think about the challenge of communicating with Residential Customers about Smart Grid investments as less one of educating them about the promised downstream benefits than reassuring them on why they can and should trust the promises made to them by their utility on these issues. The latter task is the more difficult, but must be proactively addressed if Residential Customer opposition to Smart Grid investments is going to be appropriately managed.
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Understanding Residential Customer Support for – and Opposition to – Smart Grid Investments
The Electricity Journal, 2011Co-Authors: David C LineweberAbstract:Consumer research data suggest that the industry needs to think about the challenge of communicating with Residential Customers about Smart Grid investments as less one of educating them about the promised downstream benefits than reassuring them on why they can and should trust the promises made to them by their utility on these issues. The latter task is the more difficult, but must be proactively addressed if Residential Customer opposition to Smart Grid investments is going to be appropriately managed.
A M Ranjbar - One of the best experts on this subject based on the ideXlab platform.
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dynamic load management for a Residential Customer reinforcement learning approach
Sustainable Cities and Society, 2016Co-Authors: Aras Sheikhi, Mohammad Rayati, A M RanjbarAbstract:Abstract United Nation aims to double the global rate of improvement in energy efficiency as one of the sustainable development goals. It means researchers should focus on energy systems to enhance their overall efficiency. One of the effective solution to move from suboptimal energy systems to optimal ones is analyzing energy system in Energy Hub (EH) framework. In EH framework, interactions between different energy carriers are considered in supplying the required loads. The couplings and selecting proper combinations of inputs energy carriers lead to more optimized and intelligent consumption. The appropriate combination is found by solving an optimization problem at each time step. Utilizing intelligent technologies such as Advanced Metering Infrastructures (AMIs) inevitably facilitate the decision making processes. This paper modifies the classic Energy Hub model to present an upgraded model in the smart environment entitling “Smart Energy Hub” and optimizes the operation of a Residential Customer equipped with combined heat and power (CHP), auxiliary boiler, electricity storage and heating storage in this framework. Supporting real time, two-way communication between utility companies and smart energy hubs, and allowing AMIs at both ends to manage power consumption necessitates large-scale real-time computing capabilities to handle the communication and the storage of huge transferable data. To address this concern and reduce the amount of calculations, Reinforcement Learning (RL) method is employed to find a near optimal solution, which does not need massive computations. Finally, communications to large numbers of endpoints in a secure, scalable, and highly-available environment, in this paper, we propose a cloud computing (CC) architecture. Simulation results show that by applying RL technique in smart energy hub framework for a Residential Customer, efficiency of the energy system is increased substantially and leads to decrease energy bills and electricity peak load.
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demand side management for a Residential Customer in multi energy systems
Sustainable Cities and Society, 2016Co-Authors: Aras Sheikhi, Mohammad Rayati, A M RanjbarAbstract:Abstract Today, as a consequence of the growing installation of efficient technologies, e.g. micro-combined heat and power (micro-CHP), the integration of traditionally separated electricity and natural gas networks has been attracting attentions from researchers in both academia and industry. To model the interaction among electricity and natural gas networks in distribution systems, this paper models a Residential Customer in a multi-energy system (MES). In this paper, we propose a fully automated energy management system (EMS) based on a reinforcement learning (RL) algorithm to motivate Residential Customers for participating in demand side management (DSM) programs and reducing the peak load in both electricity and natural gas networks. This proposed EMS estimates the Residential Customers’ satisfaction function, energy prices, and efficiencies of appliances based on the Residential Customers’ historical actions. Simulations are performed for the sample model and results depict how much of each energy, i.e. electricity and natural gas, the Residential Customer should consume and how much of natural gas should be converted in order to meet electricity and heating loads. It is also shown that the proposed RL algorithm reduces Residential Customer energy bill and electrical peak load up to 20% and 24%, respectively.
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Energy Hub optimal sizing in the smart grid; machine learning approach
2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2015Co-Authors: A. Sheikhi, M. Rayati, A M RanjbarAbstract:The interests in “Energy Hub” (EH) and “Smart Grid” (SG) concepts have been increasing, in recent years. The synergy effect of the coupling between electricity and natural gas grids and utilizing intelligent technologies for communicating, may change energy management in the future. A new solution entitling “Smart Energy Hub” (S. E. Hub) that models a multi-carrier energy system in a SG environment studied in this paper. Moreover, the optimal size of CHP, auxiliary boiler, absorption chiller, and also transformer unit as main elements of a S. E. Hub is determined. Authors proposed a comprehensive cost and benefit analysis to optimize these elements and apply Reinforcement Learning (RL) algorithm for solving the optimization problem. To confirm the proposed method, a Residential Customer has been investigated as an S. E. Hub in a dynamic electricity pricing market.
Aras Sheikhi - One of the best experts on this subject based on the ideXlab platform.
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dynamic load management for a Residential Customer reinforcement learning approach
Sustainable Cities and Society, 2016Co-Authors: Aras Sheikhi, Mohammad Rayati, A M RanjbarAbstract:Abstract United Nation aims to double the global rate of improvement in energy efficiency as one of the sustainable development goals. It means researchers should focus on energy systems to enhance their overall efficiency. One of the effective solution to move from suboptimal energy systems to optimal ones is analyzing energy system in Energy Hub (EH) framework. In EH framework, interactions between different energy carriers are considered in supplying the required loads. The couplings and selecting proper combinations of inputs energy carriers lead to more optimized and intelligent consumption. The appropriate combination is found by solving an optimization problem at each time step. Utilizing intelligent technologies such as Advanced Metering Infrastructures (AMIs) inevitably facilitate the decision making processes. This paper modifies the classic Energy Hub model to present an upgraded model in the smart environment entitling “Smart Energy Hub” and optimizes the operation of a Residential Customer equipped with combined heat and power (CHP), auxiliary boiler, electricity storage and heating storage in this framework. Supporting real time, two-way communication between utility companies and smart energy hubs, and allowing AMIs at both ends to manage power consumption necessitates large-scale real-time computing capabilities to handle the communication and the storage of huge transferable data. To address this concern and reduce the amount of calculations, Reinforcement Learning (RL) method is employed to find a near optimal solution, which does not need massive computations. Finally, communications to large numbers of endpoints in a secure, scalable, and highly-available environment, in this paper, we propose a cloud computing (CC) architecture. Simulation results show that by applying RL technique in smart energy hub framework for a Residential Customer, efficiency of the energy system is increased substantially and leads to decrease energy bills and electricity peak load.
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demand side management for a Residential Customer in multi energy systems
Sustainable Cities and Society, 2016Co-Authors: Aras Sheikhi, Mohammad Rayati, A M RanjbarAbstract:Abstract Today, as a consequence of the growing installation of efficient technologies, e.g. micro-combined heat and power (micro-CHP), the integration of traditionally separated electricity and natural gas networks has been attracting attentions from researchers in both academia and industry. To model the interaction among electricity and natural gas networks in distribution systems, this paper models a Residential Customer in a multi-energy system (MES). In this paper, we propose a fully automated energy management system (EMS) based on a reinforcement learning (RL) algorithm to motivate Residential Customers for participating in demand side management (DSM) programs and reducing the peak load in both electricity and natural gas networks. This proposed EMS estimates the Residential Customers’ satisfaction function, energy prices, and efficiencies of appliances based on the Residential Customers’ historical actions. Simulations are performed for the sample model and results depict how much of each energy, i.e. electricity and natural gas, the Residential Customer should consume and how much of natural gas should be converted in order to meet electricity and heating loads. It is also shown that the proposed RL algorithm reduces Residential Customer energy bill and electrical peak load up to 20% and 24%, respectively.
Fei Wang - One of the best experts on this subject based on the ideXlab platform.
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IAS - PV- Load Decoupling Based Demand Response Baseline Load Estimation Approach for Residential Customer with Distributed PV System
2019 IEEE Industry Applications Society Annual Meeting, 2019Co-Authors: Fei Wang, Kangping Li, Xinxin GeAbstract:Due to increasing installation of distributed photovoltaic systems (DPVSs), load patterns of Residential Customers become more random, which makes Customer baseline load (CBL) estimation harder. This paper proposes a PV-load decoupling approach to improve the CBL estimation accuracy in the presence of DPVSs. Firstly, K-means algorithm is used to divide the Customers in control group into k clusters. Secondly, after calculating curve similarity index, each DR participant is matched to the most similar cluster based on the similarity between its load curve and cluster centroids during periods when the distributed photovoltaic (DPV) output power is equal to zero. Then the DPV output power in DR period can be obtained through the estimation model established based on DPV output power of non-DR periods in historical non-DR days and DR event day. Finally, CBL is estimated by the difference between actual load power and DPV output power. Four well-known averaging methods are compared with the proposed approach by using a real dataset of 300 Customers in Sydney, Australia. The comparison result indicates the proposed approach shows better accuracy performance.
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PV- Load Decoupling Based Demand Response Baseline Load Estimation Approach for Residential Customer with Distributed PV System
2019 IEEE Industry Applications Society Annual Meeting, 2019Co-Authors: Fei Wang, Kangping Li, Xinxin GeAbstract:Due to increasing installation of distributed photovoltaic systems (DPVSs), load patterns of Residential Customers become more random, which makes Customer baseline load (CBL) estimation harder. This paper proposes a PV-load decoupling approach to improve the CBL estimation accuracy in the presence of DPVSs. Firstly, K-means algorithm is used to divide the Customers in control group into k clusters. Secondly, after calculating curve similarity index, each DR participant is matched to the most similar cluster based on the similarity between its load curve and cluster centroids during periods when the distributed photovoltaic (DPV) output power is equal to zero. Then the DPV output power in DR period can be obtained through the estimation model established based on DPV output power of non-DR periods in historical non-DR days and DR event day. Finally, CBL is estimated by the difference between actual load power and DPV output power. Four well-known averaging methods are compared with the proposed approach by using a real dataset of 300 Customers in Sydney, Australia. The comparison result indicates the proposed approach shows better accuracy performance.
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a baseline load estimation approach for Residential Customer based on load pattern clustering
Energy Procedia, 2017Co-Authors: Kangping Li, Bo Wang, Zheng Wang, Fei Wang, Zengqiang Mi, Zhao ZhenAbstract:Abstract Demand response (DR) is a key technology enabling reliable and flexible power system operation more economically and environment-friendly than conventional manners from supply side. Customer baseline load (CBL) estimation is an important issue in the implementation of DR programs for assessing the performance of DR programs and designing economic compensation mechanisms. The accurate estimation of CBL is critical to the success of DR programs because it involves the interests of multi-stakeholders including utilities and Customers. Motivated by the inaccuracy of existing CBL methods, this paper proposes a Residential CBL estimation approach based on load pattern (LP) clustering to improve the accuracy of CBL estimation. First, an adaptive density-based spatial clustering of applications with noise (DBSCAN) algorithm is proposed to extract typical load patterns (TLPs) of each individual Customer in order to avoid the adverse effects from aggregating many dissimilar LPs together as the real TLP. Second, K-means clustering is utilized to segment Residential Customers into several different clusters based on the similarity of LPs. Finally, CBLs for DR participants are estimated based on the actual load of non-participants at the same cluster during DR event periods. The proposed methods are compared with some traditional methods on a smart metering dataset from Ireland. The results show that the proposed methods have a better performance on accuracy than averaging and regression methods.
J Strachan - One of the best experts on this subject based on the ideXlab platform.
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operation and maintenance field experience for off grid Residential photovoltaic systems
Progress in Photovoltaics, 2005Co-Authors: S Canada, Larry Moore, Harold N Post, J StrachanAbstract:The field performance of photovoltaic systems has been studied extensively for many applications and a number of databases exist in the United States and internationally. However, these databases focus almost exclusively on the system elecrical performance. Published information on the operation and maintenance (O&M) experience and costs for photovoltaic systems is almost nonexistent. At a time when photovoltaics is being considered as a viable option for distributed energy generation, it is critical that maintenance experience be captured to identify lifecycle costs and/or levelized energy costs for these systems, as well as to identify areas for system and component improvements. This paper addresses the data collection, analysis and results of an off-grid Residential Customer service program offered by the Arizona Public Service (APS) Company over a six-year period from 1997 through 2002. Standardized, packaged photovoltaic systems were offered and operated by APS through a lease arrangement with Customers throughout the state of Arizona. The operation and maintenance records for these systems were carefully tracked and analyzed. The O&M costs, database development, cost drivers, lifecycle cost implications, and lessons learned are presented and discussed. Copyright © 2004 John Wiley & Sons, Ltd.