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This paper investigates a comprehensive optimization method of determining the size and location of dynamic voltage restorers (DVRs) in distribution network considering customers’ demand for sag mitigation. The objective is to minimize the investment of DVRs subject to customers’ satisfaction. In the objective function, the unit price per kVA of DVR was described by a continuous function of the capacity to calculate the investment. In the constraints, a mitigation expectation index (MEI) was proposed to assess the customers’ demand for mitigation. The MEI indicates the proportion among all sag events affecting customers, which were expected to be mitigated by the customers. It is represented by the cumulative probability of residual voltage of sag events. To improve the computational efficiency, the binary encoding for the locations and real encoding for the output voltage of DVRs are combined as chromosomes in multiple population Genetic Algorithm (MPGA). The proposed method was testified with the IEEE 33 bus distribution system with different sensitive loads. Optimal results were obtained to satisfy different customers’ demand for sag mitigation efficiently. [ABSTRACT FROM AUTHOR]

Though the convex optimization has been widely used in power systems, it still cannot guarantee to yield a tight (accurate) solution to some problems. To mitigate this issue, this paper proposes an ensemble learning based convex approximation for alternating current (AC) power flow equations that differs from the existing convex relaxations. The proposed approach is based on three-phase quadratic power flow equations in rectangular coordinates. To develop this data-driven convex approximation of power flows, the polynomial regression (PR) is first deployed as a basic learner to fit convex relationships between the independent and dependent variables. Then, ensemble learning algorithms such as gradient boosting (GB) and bagging are introduced to combine learners to boost model performance. Based on the learned convex approximation of power flow, optimal power flow (OPF) is formulated as a convex quadratic programming problem. The simulation results on IEEE standard cases of both balanced and unbalanced systems show that, in the context of solving OPF, the proposed data-driven convex approximation outperforms the conventional semi-definite programming (SDP) relaxation in both accuracy and computational efficiency, especially in the cases that the conventional SDP relaxation fails. [ABSTRACT FROM AUTHOR]

This letter presents a novel optimization framework of formulating the three-phase optimal power flow that involves uncertainty. The proposed uncertainty-aware optimization (UaO) framework is: 1) a deterministic framework that is less complex than the existing optimization frameworks involving uncertainty, and 2) convex such that it admits polynomial-time algorithms and mature distributed optimization methods. To construct this UaO framework, a methodology of learning-aided uncertainty-aware modeling, with prediction errors of stochastic variables as the measurement of uncertainty, and a theory of data-driven convexification are proposed. Theoretically, the UaO framework is applicable for modeling general optimization problems under uncertainty. [ABSTRACT FROM AUTHOR]

In this paper, distributed energy storage (DES) devices, like batteries and ultra-capacitors, are used to alleviate detrimental impacts of high penetration photovoltaic (PV) resources on distribution systems. The impacts are studied at mainly two time resolutions—one minute and one hour. To determine accurately the size of the required DES for the purpose of mitigating the impacts of large-scale distributed PV, sizing procedures based on OpenDSS are proposed. After determining the total size of the required DES, optimization techniques can be used to choose the optimal locations for the DES along the feeder, which is a continuous optimization problem taking into account equality constraints of the AC power flow. The continuity of the problem and the radial network structure make it possible to apply a convex optimization technique called second order cone programming (SOCP) relaxation to obtain the globally optimal solution and avoid the problem of NP-hardness. The exactness of the introduced SOCP relaxation is sensitive to the chosen objective function and additional quadratic equalities. The necessary and sufficient condition of exactness for the SOCP relaxation of the DES optimal allocation and operation in radial distribution systems is studied. The proposed methods are applied to an actual feeder in the southwestern US with high penetration of PV using actual measured data. The simulation results demonstrate the efficacy of the proposed approaches. [ABSTRACT FROM PUBLISHER]

Information framework of optimal reactive power dispatch (ORPD) and the essential characteristics of coordinated reactive power optimization (RPO) in multi-area large-scale power systems are introduced in this paper. We consider that the change of optimal solutions (control settings) cannot be captured by simply using an equality constraint of the boundary values as which the voltages at boundary buses and power flows in tie lines are referred to. While many previous methods were proposed by using change of the boundary values to evaluate the change of optimal solutions, which might lead to failure in convergence, this paper puts forward a coordination strategy according to the correspondence between optimal solutions and boundary values based on a probing mechanism. This method is applied to a 472-bus power system; the simulation results show that the RPO scheme based on this strategy has strong convergence capability and high efficiency. Moreover, the proposed coordination strategy allows the transmission system operators (TSOs) to choose objective function and optimization algorithm independently. [ABSTRACT FROM PUBLISHER]

Internet of things for Aquaculture is an integrated modern system based on computing and communications technology like smart sensor technology, reliable telecommunication, intelligent information processing, which can collect data and images, transmit and process data intelligently, forecast future trend and early-warning for decision support. First of all, it is a key issue to establish scientific and rational index system and evaluation method for internet of things for aquaculture to guarantee its effectiveness. With the rapid development of information technology in China, the internet of things for aquaculture has been promoted and applied in Jiangsu, Shandong, Hunan, Hubei, Zhejiang and Guangdong. However, the internet of things for aquaculture in China is at an early stage and there are some problems, which cause negative impact on the promotion and application of the system, for examples, redundant functions, high cost, unstable performances, and so on. Secondly, in order to assess internet of things for aquaculture system, index system to assess internet of things for aquaculture was built in this paper by indicator optimization model to solve randomicity, redundancy, cross-connection and overlap caused subjective selection. Three steps composed of the selection process: 1) first round selection, three categories indices of function indicator, performance indicator and effectiveness indicator, targeted to 40 indicators were selected; 2) second round selection, 40 indicators representing perception layer, transmission layer and application layer, were optimized to 26 by method proposed by Dale and Beyeler, in which the standard conformity degree of each indicator was checked one by one and indicators need to meet at least 5 standards, otherwise they will be eliminated; 3) indicator screening model, by which 26 indicators were reduced to 14, with only 35% of total indicators representing 88.45% of total information, capturing the requirements of completeness and simplicity. Thirdly, fuzzy comprehensive evaluation approach, which comprise of three levels fuzzy comprehensive evaluation, was established to assess the application of internet of things for aquaculture. Most of the 14 indicators got in the first phase are qualitative factors and difficult to be quantified. That is why fuzzy comprehensive evaluation approach was used in this paper. Meanwhile, it is better to adopt multi-level factors as weights are not easy to be assigned reasonably when factors are too many, which leads unreasonable and wrong results. Multi-level fuzzy comprehensive method works well to solve this problem and can be applied to assess function characteristics, performance and effectiveness of the system of internet of things for aquaculture. Multi-level fuzzy comprehensive was built by steps of establishing comment set, membership and got quantitative score A, which can fall into five levels of excellence (A⩾82.5), good (82.5>A⩾67.5), satisfactory (67.5>A⩾52.5), barely adequate (52.5>A⩾35) and fail (A <35). Finally, case studies were carried out in Yixing of crab cultivation, Jiangsu province and Zhanjiang of white shrimp cultivation, Guangdong province, in which the index system and multi-level fuzzy comprehensive method were tested based on the internet of things for aquaculture system developed independently by China Agricultural University (CAU). Results showed that the score in Yixing was 87.371 indicating application of internet of things for aquaculture system in Yixing is excellent, meanwhile, the score in Zhanjiang is 74.921, which is good. Both of the two results are consistent with the actual situation of two bases, showing that index system and multi-level fuzzy comprehensive method proposed in this paper are feasible and reasonable for evaluation of application of internet of things for aquaculture and can guide its construction and improvement. [ABSTRACT FROM AUTHOR]