A comprehensive framework for optimal day-ahead operational planning of self-healing smart distribution systems

Hosseinnezhad, Vahid; Rafiee, Mansour; Ahmadian, Mohammad Taghi; Siano, Pierluigi

doi:10.1016/j.ijepes.2017.12.031

Cited by 34 publications

(29 citation statements)

References 37 publications

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“…HEMS capabilities have been progressively extended to prioritize appliances [11], include demand response combined with storage [12], and electric vehicles (EV) [13]. Hosseinnezhad et al [14][15] uses artificial intelligence techniques to solve the HEMS scheduling problem. Hosseinnezhad et al [14] demonstrates benefits of using a self-healing strategy that will sectionalize an isolated area of the distribution system into island partitions to provide reliable power supply to the critical loads continuously.…”

Section: Literature Surveymentioning

confidence: 99%

Battery Storage for Resilient Homes

Chatterji

Bazilian

2020

IEEE Access

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Small-scale battery energy storage systems (BESS), especially for behind-the-meter applications, are still relatively expensive, but we show that it can be a potent solution to render homes resilient to storm related power outages. We present a stochastic programming model formulation to optimize PV/BESS explicitly accounting for resilience benefits these investments entail, over and above their ability to reduce cost of supply. The stochastic optimization considers uncertainties around storm related grid supply failures as well as variability of solar PV. The model includes an embedded Monte Carlo simulation module that considers storm related outage risks using climate model reanalysis data. It is a least-cost planning framework that optimizes selection of BESS, solar PV, grid supply, and diesel generator, from a homeowner's perspective. We present two case studies with low and high storm risks that demonstrate how different risk exposures can impact on the selection of alternative options to build resilience. Duals, or shadow prices, of demand-supply constraints from the model for both normal days and for storm related contingencies, provide interesting insights into the marginal cost of supply that can inform innovative pricing schemes to promote customer level resilience measures. The case study results reveal significant merits of BESS, in combination with PV, to enhance resilience. We find that in low-risk areas like Bethesda, MD, incremental PV and BESS required for a more resilient system can add $79 (4%) to expected annual electricity costs for a typical household, and a considerably higher $208 (10.6%) in Miami, FL which is at a much greater risk. These options are, however, 27% (in Bethesda) and 20% (in Florida) less expensive than the conventional solution of installing a diesel generator. These results provide insights into the value of BESS as part of a resilient and clean energy solution for households.

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Section: Literature Surveymentioning

confidence: 99%

Battery Storage for Resilient Homes

Chatterji

Bazilian

2020

IEEE Access

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“…Regarding RMG operational scheduling, it is considered that a wind turbine (WT) is integrated into bus number 6 on the RMG [36]. The estimated power output of the WT as given in Table 2, including the electricity demand and power price values for a 24-h time period for that scheduling framework of RMG are the same as those in [25]. Figure 2 has been presented in the form of a graph in order to see the daily load distribution comparable with power pricing.…”

Section: Test System Featuresmentioning

confidence: 99%

“…The operating costs of the distribution network are minimized in the normal operation mode by the optimal scheduling and dispatching of controllable DGs based on the model presented in [22]. In addition, the probabilistic and intermittent characteristics of renewable DG units, including an hourly variation of the demand and power market prices in the power system, have been considered within the scope of our optimal scheduling framework [23][24][25]. Thus, the optimal operational scheduling problem, which is already a non-linear, combinatorial, and NP-hard optimization problem, becomes a more complex problem [11].…”

Section: Introductionmentioning

confidence: 99%

Optimal Operational Scheduling of Reconfigurable Microgrids in Presence of Renewable Energy Sources

2019

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Passive distribution networks are being converted into active ones by incorporating distributed means of energy generation, consumption, and storage, and the formation of so-called microgrids (MGs). As the next generation of MGs, reconfigurable microgrids (RMGs) are still in early phase studies, and require further research. RMGs facilitate the integration of distributed generators (DGs) into distribution systems and enable a reconfigurable network topology by the help of remote-controlled switches (RCSs). This paper proposes a day-ahead operational scheduling framework for RMGs by simultaneously making an optimal reconfiguration plan and dispatching controllable distributed generation units (DGUs) considering power loss minimization as an objective. A hybrid approach combining conventional particle swarm optimization (PSO) and selective PSO (SPSO) methods (PSO&SPSO) is suggested for solving this combinatorial, non-linear, and NP-hard complex optimization problem. PSO-based methods are primarily considered here for our optimization problem, since they are efficient for power system optimization problems, easy to code, have a faster convergence rate, and have a substructure that is suitable for parallel calculation rather than other optimization methods. In order to evaluate the suggested method’s performance, it is applied to an IEEE 33-bus radial distribution system that is considered as an RMG. One-hour resolution of the simultaneous network reconfiguration (NR) and the optimal dispatch (OD) of distributed DGs are carried out prior to this main study in order to validate the effectiveness and superiority of the proposed approach by comparing relevant recent studies in the literature.

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“…Another important issue to consider in this article is the use of backup feeders for system restoration. Backup feeders are only used in References for the restoration problem. In Reference , load shedding and islanding state are not considered for restoration.…”

Section: Introductionmentioning

confidence: 99%

“…In Reference , load shedding and islanding state are not considered for restoration. In References , load shedding is not considered for restoration. The cost of distributed generation rescheduling (DGR) is another important issue that is overlooked in the above literature.…”

Section: Introductionmentioning

confidence: 99%

Self‐healing optimization in active distribution network to improve reliability, and reduction losses, switching cost and load shedding

Choopani

Hedayati

Effatnejad

2020

Int Trans Electr Energ Syst

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Summary Self‐healing refers to the specific ability of the smart grid that takes precautionary actions before the fault. It also performs fault location, isolation, and service restoration to minimize network damage after the fault. This process is performed with the help of communications infrastructure and remote control. The main components of this article include the use of graph theory and binary particle swarm optimization algorithm for optimal switching, the use of particle swarm optimization algorithm and forward‐backward sweep load flow for load shedding and distributed generation rescheduling, investigating the effect of self‐healing on improved reliability, power losses reduction, and load shedding reduction and the present a new approach for self‐healing optimization and a new mathematical model. The problem of self‐healing due to nonlinear and unbounded constraints is one of the nonconvex mixed integer nonlinear programming optimization problems. The optimization problem becomes a convex mixed integer linear programming optimization problem using the proposed scheme. Due to the nonlinearity and the nonconvexity of the problem, commercial solvers are not able to solve this problem. Even the Baron solver, which is used to solve nonconvex nonlinear problems, cannot achieve the optimal global solution. However, the proposed method is easily able to achieve the optimal solution by eliminating the nonlinear and nonconvex element. A modified RBTS‐4 distribution system is used as a test system. The IEEE 33‐bus distribution system is used to validate the proposed method. The results of case studies demonstrate the effectiveness of the proposed methodology.

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A comprehensive framework for optimal day-ahead operational planning of self-healing smart distribution systems

Cited by 34 publications

References 37 publications

Battery Storage for Resilient Homes

Battery Storage for Resilient Homes

Optimal Operational Scheduling of Reconfigurable Microgrids in Presence of Renewable Energy Sources

Self‐healing optimization in active distribution network to improve reliability, and reduction losses, switching cost and load shedding

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