Energy Hub
H. Hosseinnejad; S. Galvani; P. Alemi
Abstract
Background and Objectives: Different energy demand calls the need for utilizing Energy Hub Systems (EHS), but the economic dispatch issue has become complicated due to uncertainty in demand. So, scenario generation and reduction techniques are used to considering the uncertainty of the EH demand. Dependent ...
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Background and Objectives: Different energy demand calls the need for utilizing Energy Hub Systems (EHS), but the economic dispatch issue has become complicated due to uncertainty in demand. So, scenario generation and reduction techniques are used to considering the uncertainty of the EH demand. Dependent on the amount of fuel used, each system has various generation costs. Configuration selection stands as a challenging dilemma in the EHS designing besides economic problems. In this paper, the optimal EHS operation along with configuration issue is tackled. Methods: To do so, two EHS types are investigated to evaluate the configuration effect besides energy prices simultaneously change. Typically, the effect of the Demand Response (DR) feature is rarely considered in EHSs management which considered in this paper. Also, Metaheuristic Automatic Data Clustering (MADC) is used to reduce the decision-making problem dimension instead of using human decision makers in the subject of cluster center numbers and considering uncertainty. The "Shannon's Entropy" and the "TOPSIS" methods are also used in the decision-making. The study is carried out in MATLAB© and GAMS©. Results: In addition to minimizing the computational burden, the proposed EHS not only serves an enhancement in benefit by reducing the cost but also provides a semi-flat load curve in peak period by employing Emergency Demand Response Program (EDRP) and Time of Use (TOU). Conclusion: The results show that significant computational burden reduction is possible in the field of demand data by using automatic clustering method without human interference. In addition to the proposed configuration's results betterment, the approach demonstrated EH's configuration effect could consider as important as other features in the presence of DRPs for reaching desires of EHs customers which rarely considered. Also, "Shannon's Entropy" and the "TOPSIS" methods integration could select the best DRP scenario without human interference. The results of this study are encouraging and warrant further analysis and researches.======================================================================================================Copyrights©2020 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.======================================================================================================
Power
M. Nikzad; A. Samimi
Abstract
Background and Objectives: Suitable scheming as well as appropriate pricing of demand response (DR) programs are two important issues being encountered by system operators. Assigning proper values could have effects on creating more incentives and raising customers’ participation level as well ...
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Background and Objectives: Suitable scheming as well as appropriate pricing of demand response (DR) programs are two important issues being encountered by system operators. Assigning proper values could have effects on creating more incentives and raising customers’ participation level as well as improving technical and economical characteristics of the power system. Here, time of use (TOU) as an important scheme of DR is linearly introduced based on the concepts of self and cross price elasticity indices of load demand. Methods: In order to construct an effective TOU program, a combined optimization model over the operation cost and customers’ benefit is proposed based on the security-constrained unit commitment (SCUC) problem. Supplementary constraints are provided at each load point with 24-hour energy consumption requirement along with DR limitations. Results: IEEE 24-bus test system has been employed to investigate the different features of the presented method. By varying DR potential in the system, TOU rates are determined and then their impacts on the customers' electricity bill, operation cost, and reserve cost as well as load profile of the system are analyzed. In addition, the effect of network congestion as a technical limitation is studied. The obtained results demonstrate the effectiveness and applicability of the proposed method. Conclusion: The simulation results demonstrate that the TOU rates leads to financial profit for all customers, reduction of peak load as well as the operation cost while 24-hour energy consumptions of customers at load buses have been fulfilled. Furthermore, the operation cost decreases gradually by attaining more flat load profile. In addition, the effect of lines congestion on the proposed method has been investigated and it has been shown that lines congestion leads to profit reduction of customers at load points connected to the congested lines.======================================================================================================Copyrights©2018 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.======================================================================================================
