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This paper presents a novel modular voltage control algorithm for optimal scheduling of a distribution system's load tap changers to minimize the number of tap changes while maintaining a voltage deviation (VD) around a desired target. To this end, a bi-objective optimal voltage regulation (OVR) problem is addressed in two distinct stages. First, the operational constraint on the load tap changer is removed to form a single-objective OVR problem relating to the voltage. The solution obtained in this stage is ultimately utilized to determine the penalty value assigned to the distance from the optimal (solely in terms of voltage) control value. In the second stage, the optimal scheduling problem is formulated as a minimum-cost-path problem, which can be efficiently solved via dynamic programming. This approach allows the identification of optimal scheduling that considers both the voltage-related objective as well as the number of load tap changer switching operations with no added computational burden beyond that of a simple voltage optimization problem. The method imposes no restriction on the load tap changer's operation and is tested under two different target functions on the standard IEEE-123 test case. The first attains a nominal voltage with a 0.056 p.u. voltage deviation and the second is the well-known conservation voltage reduction (CVR) case with a 0.17 p.u. voltage deviation. The method is compared to an evolutionary-based algorithm and shows significant improvement in the voltage deviation by a factor of 3.5 as well as a computation time acceleration of two orders of magnitude. The paper demonstrates the effectiveness and potential of the proposed method as a key feature in future cutting-edge OVR methods. [ABSTRACT FROM AUTHOR]

We devise an algorithm for finding the global optimal solution of the so-called optimal power flow problem for a class of power networks with a tree topology, also called radial networks, for which an efficient and reliable algorithm was not previously known. The algorithm we present is called the tree reduction/expansion method, and is based on an equivalence between the input network and a single-node network. Finally, our numerical experiments demonstrate the reliability and robustness of our algorithm. [ABSTRACT FROM AUTHOR]

As a response to increased power demand, electrical utilities have started to use Demand Response (DR) programs to improve the stability, efficiency and reliability of their networks. One type of DR, an Emergency Demand Response Program (EDRP), addresses stability challenges by sending curtailment orders to consumers associated with the program. There are different methods for calculating the amount of power that should be curtailed and how it should be divided among consumers. An important aspect of curtailment assignment is fairness, yet there is no agreed-upon manner of achieving fairness in curtailment allocation. This paper presents a new fairness-based EDRP for a residential area. The fairness is manifested in the algorithm by taking into account the number of times and the amount of energy a customer is asked to curtail for a predetermined period of time. The proposed algorithm is demonstrated and verified using simulations. • A set of four criteria are presented for fair curtailment of loads in emergency demand response operation. • Algorithm that takes into consideration the above criteria is developed. • The algorithm is based on a weigh function for deciding the chosen concentrator by scores. • The algorithm takes into account the time interval for each request. • Two test cases are simulated to support the theoretical method. [ABSTRACT FROM AUTHOR]