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Electric springs with capacitive energy storage (ES-1) have been proposed as a continuous demand-side management (DSM) technology to address the intermittency of renewables in power systems. The enabled smart load technology distinguishes itself from other DSM technologies with the unique function of achieving active power manipulation via reactive power control. However, the embedded coupled relationship between the active and reactive powers of the smart load usually results in the contradiction between grid frequency regulation and economic power flow with the conventional control scheme. To address this issue, a centralized control framework is proposed in this paper to achieve the collaborative operation of multiple ES-1s for frequency stabilization and economic power flow in microgrids. The proposed control framework exploits the power variations of the deferrable loads and enables the minimum reactive power flows in the microgrids, which leads to reduced storage requirement and minimum distribution loss. It is the first control framework to address the active and reactive power coupling issue of the ES-1 based smart loads, leading to the independent control of system frequency and optimal reactive power reallocation. The generalized and practical steady-state model of the ES-1 based smart load is investigated. The formulation for achieving the dual-objective optimization is established. Case studies based on experiments and simulations of a 110 V AC microgrid are performed to verify the effectiveness of the proposed control framework. [ABSTRACT FROM AUTHOR]

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 propose a distributed online voltage control algorithm for distribution networks with multiple photovoltaic (PV) systems based on dual-ascent method. Conventional distributed algorithms implement voltage control only when the algorithms converge. However, our proposed algorithm is able to carry out voltage control immediately. In particular, we derive a closed-form solution for PV controllers to locally update the active and reactive power set points aiming at minimizing the total loss and maintaining bus voltages within the acceptable ranges. The optimality is guaranteed and the convergence is established analytically. Moreover, our proposed algorithm only requires the information exchange between neighboring PV systems, thus reducing communication complexity. Finally, numerical tests on IEEE 37-bus distribution system verify the effectiveness and robustness of our proposed 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]

• The SLM is proposed to solve the RPOIU model. • A modified SLM (MSLM) is proposed to improve SLM. • The results obtained by SLM and MSLM is compared with those acquired by the ISQPM. With the integration of high penetration renewable power generation, the voltage security of power systems is threatened. The reactive power optimization including interval uncertainty (RPOIU) model is applied for developing a voltage control strategy to ensure that the state variables of a power grid reside within their safe operating limits under uncertainty of renewable power generation and load demand input data. However, the RPOIU model is not solved properly in terms of solution efficiency and optimization effect at present. This paper proposes a novel method for solving the RPOIU model, which is denoted as the security limits method (SLM). The proposed SLM first computes the security limits of the state variables of the RPOIU model using an interval power flow method, i.e., the optimizing-scenarios method, and then transforms the RPOIU model into a deterministic model whose constraints conform to the established security limits. The deterministic model is then efficiently solved using the interior point method. We verify that the voltage control strategy obtained by solving the deterministic model is able to ensure that the values of the state control variables satisfy the constraints of the original RPOIU model, but with more conservative security limits. This issue is addressed by modifying the security limits of the SLM through defining an interval ratio, and applying an iterative predictor-corrector procedure. The result obtained by the proposed SLM and modified SLM based on numerical simulations are compared with those obtained by a previously proposed method for effectively solving the RPOIU model, and analysis of the results demonstrates the advantages, effectiveness, and good applicability of the proposed SLM. [ABSTRACT FROM AUTHOR]