Severe peak rebounds are likely in absence of a system-wide coordination among customers participating in demand response programs. This paper aims to establish a decentralized system-wide framework to coordinate demand response of residential customers in a smart grid. The objective of the framework is to modify system load profile provided that customers' payments are minimized, and their comfort and privacy are preserved. Home load management (HLM) modules, embedded in customers' smart meters are autonomous agents of the framework. The energy service provider iteratively exchanges load information with HLM modules in the hope of achieving his desired load profile. In each iteration, the service provider announces system load profile to HLM modules. The modules, keeping in mind their own financial and comfort constraints, nonsequentially send back load reschedule proposals to modify system load profile. The received proposals are judged whether they improve system load profile or not. HLM modules with accepted proposals apply their proposed schedules. The modified system load profile is then released, and HLM modules' new proposals are gathered and judged. This procedure is repeated to the point at which no further improvement in the system load profile can be experienced. Performance of the framework is shown by applying it to a system with 50 customers.

Natural calamities always have been a serious threat to energy systems. In this regard, this paper constitutes a stochastic mixed integer linear programming (SMILP) model to enhance the resilience of power distribution systems to deal with disastrous events. In particular, the proposed model is developed to enhance both survivability and restoration capability of distribution systems. In this regard, to increase the preparedness of the power distribution system, the system operator reconfigures the network by utilizing remote-control switches (RCSs), manual switches (MSs), and distributed generations (DGs) before the natural calamity hits. The proposed model contemplates the traveling time of crew teams to the MS sites in the transportation system in order to achieve the best switching sequence. In addition, it pre-positions the crews and mobile emergency generators (MEGs) in staging locations to hasten the likely post-disturbance operations. To do so, likely post-event operations are included in the model using the scenario generation technique. To validate the performance of the developed model a distribution system is employed. The results of simulations confirm the effectiveness of the proposed approach in declining the interruption of electric energy for customers.

Automation systems provide substantial improvement in service reliability through speeding up fault management procedure. However, this is at expense of high investments whose justification needs thorough cost/worth analyses. This paper introduces a mathematical model to optimally place automation system devices within distribution networks. The model establishes a trade-off between service reliability improvements and the relevant costs. Among different automation system devices, fault indicators and remote controlled switches are considered here. Also, the impact of manual switches is regarded since their number and location significantly affect the solution of the placement problem. The proposed model is developed in the mixed integer programming format which can be effectively solved via available solvers. The effectiveness of the proposed model is demonstrated through a test system and a real-life distribution network. The obtained results indicate the great performance of the model in enhancing service reliability as well as reducing system costs.

Both frequency and intensity of natural disasters have intensified in recent years. It is, therefore, essential to design effective strategies to minimize their catastrophic consequences. Optimizing recovery tasks, including distribution system reconfiguration (DSR) and repair sequence optimization (RSO), are the key to enhance the agility of disaster recovery. This article aims to develop a resilience-oriented DSR and RSO optimization model and a mechanism to quantify the recovery agility. In doing so, a new metric is developed to quantify the recovery agility and to identify the optimal resilience enhancement strategies. The metric is defined as “the number of recovered customers divided by the average outage time of the interrupted customers.” A Monte-Carlo-based methodology to quantify the recovery agility of different DSR plans is developed. It will be shown that if the total number of interrupted customers over the recovery horizon is minimized, the metric will be maximized. Accordingly, the DSR and RSO optimization models are modified to maximize the introduced metric. The proposed optimization model is formulated as a mixed-integer linear programming model that can be solved via commercial off-the-shelf solvers. Finally, the proposed methodology is applied to several case studies to examine its effectiveness. It will be also shown how the proposed methodology can be utilized for distributed generator (DG) and tie-line placement problems in planning for enhanced structural resilience.

With anticipated proliferation of electric vehicles and distributed generations in near future, dynamic thermal rating (DTR) as a tool for unlocking network capacities, is becoming critical for distribution network operators. DTR is gaining a great and still growing focus of attention in today's power industries. However, potential benefits of DTR, although have been envisioned to be significant, have not yet been studied quantitatively. This paper intends to comprehensively assess the potential impacts of DTR on the performance of a realistic Finnish distribution network. For doing so, first a step-by-step procedure is devised. Then, weather data and loading information of circuits in the network are gathered by advanced metering infrastructure available in the network. Thereafter, the gathered data are fed to mathematical models provided by the IEEE Standard 738-2006 and component dynamic thermal models to give thermal rating of circuits. The ratings are then used in reliability analysis where likely system states are simulated. This paper is performed for several cases and the results are discussed.

Sectionalizing switches (SSs) are often presumed to function fully reliable. However, SSs are exposed to have some malfunctions, whereby undermining their ability to improve service reliability. This paper presents a model to integrate SS malfunction probability into SS placement problem. The model aims to minimize the total SSs costs as well as customers interruption cost. The proposed model is formulated in mixed integer programing, which can be easily solved via several available solvers and gain global optimal solution. The proposed model indicates the extent to which the placement problem is affected by uncertainty associated with SSs functionality, and how not considering SSs malfunction overestimates their benefits. The effectiveness of the proposed method is demonstrated through various case studies and sensitivity analyses. Moreover, the applicability of the proposed model in practice is examined by applying it on a real-life distribution system.