Search

The unbalanced nature of the distribution networks (DNs) and communication asynchrony pose considerable challenges to the distributed voltage regulation. In this paper, two distributed voltage control algorithms are proposed to overcome these challenges in multiphase unbalanced DNs. The proposed algorithms can be leveraged in online implementations to cope with the fast-varying system operating conditions. By adopting the linearized multiphase DistFlow model, the voltage control problem is formulated as a convex quadratic programming problem for which a synchronous distributed algorithm is developed based on the dual ascent method. To account for communication delays, an asynchronous distributed algorithm is proposed evolving from the synchronous one by incorporating an event-triggered communication protocol. Furthermore, closed-form solutions to the optimization subproblems are derived to enhance the computational efficiency, and communication complexity is reduced significantly to the extent that only neighborhood information exchange is required. Finally, the convergence of the proposed algorithms to the global optimality is established analytically. Numerical tests on the IEEE 123-bus network not only corroborate that our proposed algorithms are more efficient in eliminating voltage violations and minimizing network loss compared with two benchmarks but also validate their effectiveness for online implementations. [ABSTRACT FROM AUTHOR]

In this paper, we study the optimal sizing planning of renewable distributed generation (RDG) in distribution networks to minimize the long-term cost, including the investment cost, maintenance cost, and operating cost. In particular, the operating cost itself is optimized by solving an optimal power flow (OPF) problem at each time $t$ based on uncertain time-varying RDG output and load demand. As a result, the sizing planning problem is a bilevel stochastic programming problem, which is hard to solve. Instead of resorting to conventional meta-heuristic algorithms, this paper first proposes a novel data-driven approach based on the philosophy of online convex optimization to solve the problem with drastically lower complexity. As a key step to facilitate the algorithm, we derive a closed-form expression to iteratively update the sizing solution upon drawing each data sample. With sufficient data samples, the proposed algorithm guarantees to converge to the global optimal solution regardless of the underlying probabilistic distribution of RDG output and load demand. Numerical results on the IEEE 13-bus test feeder, the IEEE 33-bus test feeder, and the Southern California Edison (SCE) 56-bus feeder show that our data-driven method drastically outperforms the other methods in terms of both the solution optimality and computational complexity. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel transactive energy trading (TET) framework to deal with the economic issues in energy trading and the technical issues in distribution system operation in a holistic manner. In particular, we innovatively integrate a bilateral energy trading mechanism with the optimal power flow (OPF) technique to increase economic benefits to individual participants, and meanwhile ensure the reliability and security of the system operation. In order to resolve the inherent conflict of interests, Nash bargaining theory is used to model the TET problem, which is further decomposed into a multiperiod OPF problem and a payment bargaining problem. Moreover, we develop an efficient distributed algorithm for solving the TET problem base on alternating direction method of multipliers (ADMM). Instead of directly solving optimization subproblems like most ADMM-based distributed algorithms, we derive closed-form solutions to all subproblems to significantly improve the computational efficiency. Finally, numerical tests on the IEEE 37-bus and 123-bus distribution systems demonstrate the effectiveness of our proposed framework and the efficiency of our distributed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we study the optimal location planning of renewable distributed generation (RDG) units by taking into account the random uncertainties of renewable generation and load demand. In presence of the random uncertainties, location planning problem is naturally a two-stage stochastic mixed integer nonlinear programming problem, which is hard to solve efficiently. Instead of using traditional sampling methods or robust optimization methods, we propose a novel analytical approach in this paper to solve the problem efficiently and optimally. In particular, analytical expressions are derived for efficiently evaluating the performance of a candidate RDG placement decision. In this way, the stochastic mixed integer nonlinear programming problem is equivalently transformed into a deterministic integer problem, which can be solved efficiently using off-the-shelf tools. Numerical results show that the optimal RDG placement can save up to $4.2\%$ of the long-term average cost and $80.59\%$ of the line losses on the IEEE 13-bus test feeder. In addition, our proposed approach effectively reduces the computational time by $99.51\%$ on the IEEE 123 node test feeder compared with other traditional sampling-based metaheuristic approaches. [ABSTRACT FROM PUBLISHER]

The ramp events of photovoltaic (PV) generation will cause severe voltage variations in a distribution network. To address this issue, a two-stage robust optimization based intra-hour dispatch model is proposed for the coordination of the on-load tap changer and smart PV systems. Hence, the distribution network security can be ensured by adjusting substation voltage and controlling PV systems. Case study on 33-bus system verifies the effectiveness of the proposed model. [ABSTRACT FROM PUBLISHER]