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The distributed Volt/Var control solution is a promising one for active distribution networks (ADNs), but a critical issue is how to improve the distributed algorithm's convergence with less communication burden. In this paper, we propose a bi-level consensus alternation direction multiplier method-based fully distributed Volt/Var optimization algorithm (B-FADMM) for ADNs by exploiting inverter-based rapid control devices. In the first level, the Volt/Var optimal solution of subpartitions is obtained in parallel, and the second level completes the variable increment of each partition to correct the search direction of the first level. In the algorithm, firstly, an adaptive matrix $\omega $ initialization strategy is proposed to adapt to the different magnitude of variables, which avoids non-convergence or slow convergence of the algorithm. Secondly, the null-space method is used in the second level of the algorithm to obtain the null-space basis matrix on the active constraint of the subpartition, which reduces the dimensions of the problem and the computational complexity. In addition, the conjugate gradient (CG) algorithm is used to update the dual multiplier of each partition only by exchanging coupling information among partitions, which protects information privacy. Experiments showed the proposed approach attains the same result as the centralized, the computational performance of the proposed method is far superior to some popular other distributed methods in terms of accuracy, computational efficiency, and scalability. [ABSTRACT FROM AUTHOR]

The penetration of photovoltaics (PVs) has been increasing in active distribution networks (ADN), which leads to severe voltage violation problems. PV inverters can provide fast and flexible reactive power support and are now allowed to participate in the voltage regulation process. This paper proposes a real-time combined central and local Volt/Var control (VVC) strategy to mitigate voltage violation problems while minimizing the network power loss. Based on the historical PV and load data, the load flow and optimal power flow are performed in the centralized controller (CC) to obtain multiple voltage and optimal power settings for each PV system. The local controller (LC) then generates voltage control curves with these optimal scatters. To improve the voltage control effect, a novel 3-Dimension voltage control curve is proposed considering both the measurements of node voltage and PV generation. Moreover, a data-driven deep convolution neural network is designed and trained to generate optimal local voltage control curves without prior knowledge of specific curve functions. The proposed approach is tested on the IEEE 33-bus distribution system and simulation results verify the effectiveness in voltage control compared with the optimal Q(V) and Q(P) method. [ABSTRACT FROM AUTHOR]

With increasing penetration of solar PV, some distribution feeders are experiencing highly variable net-load flows and even reverse flows. To optimize distribution systems under such conditions, the scheduling of mechanical devices, such as OLTCs and capacitor banks, needs to take into account forecasted solar PV and actual grid conditions. However, these legacy switching assets are operated on a daily or hourly timescale, due to the wear and tear associated with mechanical switching, which makes them unsuitable for real-time control. Therefore, there is a natural timescale-separation between these slower mechanical assets and the responsive nature of inverter-based resources. In this paper, we present a network admissible convex formulation for holistically scheduling controllable grid assets to position voltage optimally against solar PV. An optimal hourly schedule is presented that utilizes mechanical resources to position the predicted voltages close to nominal values, while minimizing the use of inverter-based resources (i.e., DERs), making them available for control at a faster time-scale (after the uncertainty reveals itself). A convex, inner approximation of the OPF problem is adapted to a mixed-integer linear program that minimizes voltage deviations from nominal (i.e., maximizes voltage margins). The resulting OPF solution respects all the network constraints and is, hence, robust against modeling simplifications.Simulation based analysis on IEEE distribution feeders validates the approach. [ABSTRACT FROM AUTHOR]

Load tap changers (LTCs) are commonly used for voltage control in distribution systems. The mechanical relays on LTCs are controlled in discrete steps; hence, distribution optimal power flow (DOPF) formulations require LTCs to be modeled with integer variables. Integer variables are generally ignored or relaxed and rounded off to reduce the computational burden in DOPF models. However, in this paper, we highlight and overcome complex infeasibility issues caused by rounding methods. Moreover, recent advances in convex optimization have improved the computational tractability of the DOPF problem. In this paper, we develop and analyze numerical efficiency of different but exact formulations for incorporating LTCs as integer control assets in a relaxed second-order cone program (SOCP) version of the DOPF model. The resulting formulation becomes a mixed-integer SOCP (MISOCP), which is computationally tractable compared to mixed-integer non-linear program (MINLP). To recover the exact optimal solution in the proposed MISOCP-DOPF model, a McCormick relaxation is employed within a sequential bound-tightening algorithm. We show that solutions obtained from MISOCP-DOPF are always ac feasible. Thus, the MISOCP-DOPF yields optimal and realistic ac solutions, and is validated in large distribution feeders and is compared to MINLP counterpart. [ABSTRACT FROM AUTHOR]

Voltage control plays an important role in the operation of electricity distribution networks, especially with high penetration of distributed energy resources. These resources introduce significant and fast varying uncertainties. In this paper, we focus on reactive power compensation to control voltage in the presence of uncertainties. We adopt a chance constraint approach that accounts for arbitrary correlations between renewable resources at each of the buses. We show how the problem can be solved efficiently using historical samples analogously to the stochastic quasi-gradient methods. We also show that this optimization problem is convex for a wide variety of probabilistic distributions. Compared to conventional per-bus chance constraints, our formulation is more robust to uncertainty and more computationally tractable. We illustrate the results using standard IEEE distribution test feeders. [ABSTRACT FROM AUTHOR]

Conservation voltage reduction (CVR) uses Volt-VAR optimization (VVO) methods to reduce customer power demand by controlling feeder's voltage control devices. The objective of this paper is to present a VVO approach that controls system's legacy voltage control devices and coordinates their operation with smart inverter control. An optimal power flow (OPF) formulation is proposed by developing linear and nonlinear power flow approximations for a three-phase unbalanced electric power distribution system. A bi-level VVO approach is proposed, where Level 1 optimizes the control of legacy devices and smart inverters using a linear approximate three-phase power flow. In Level 2, the control parameters for smart inverters are adjusted to obtain an optimal and feasible solution by solving the approximate nonlinear OPF model. Level 1 is modeled as a mixed integer linear program (MILP) while Level 2 as a nonlinear program with linear objective and quadratic constraints. The proposed approach is validated using 13-bus and 123-bus three-phase IEEE test feeders and a 329-bus three-phase PNNL taxonomy feeder. The results demonstrate the applicability of the framework in achieving the CVR objective. It is demonstrated that the proposed coordinated control approach help us to reduce the feeder's power demand by reducing the bus voltages; the proposed approach maintains an average feeder voltage of 0.96 p.u. A higher energy saving is reported during the minimum load conditions. The results and approximation steps are thoroughly validated using OpenDSS. [ABSTRACT FROM AUTHOR]

The ever-increasing utilization of sensitive loads in the industrial, commercial, and residential areas in distribution networks requires enhanced reliability and quality of supply. This can be achieved, thanks to self-healing features of smart grids that already include the control technologies necessary for the restoration strategy in case of a fault. In this paper, an analytical and global optimization model is proposed for the restoration problem. A novel mathematical formulation is presented for the reconfiguration problem reducing the number of required binary variables while covering more practical scenarios compared to the existing models. The considered self-healing actions besides the network reconfiguration are the nodal load-rejection, the tap setting modification of voltage regulation devices (incl. OLTCs, SVR, and CBs), and the active/reactive power dispatch of DGs. The voltage dependency of loads is also considered. Thus, the proposed optimization problem determines the most efficient restoration plan minimizing the number of de-energized nodes with the minimum number of self-healing actions. The problem is formulated as a Mixed-Integer Second Order Cone Programming (MISOCP) and solved using the Gurobi solver via the MATLAB interface YALMIP. A real 83–node distribution network is used to test and verify the presented methodology. [ABSTRACT FROM AUTHOR]

To solve the coordinated dispatch and time-space coupling problem in the distribution network with distributed generations (DGs), a novel two-stage (Heuristic search and Variable correction) dynamic reactive power dispatch strategy is proposed in this paper. Meanwhile, according to the analysis of Gauss-type and Z-type membership function, a new objective function is put forward to achieve the balance between network loss and voltage deviation. First, the pre-optimization is made in Niche genetic algorithm to coordinate on-load tap changer (OLTC), capacitor banks (CBs), and DGs. Second, the sequential fuzzy c-means (SFCM) based on artificial bee colony (ABC) algorithm is proposed and then the Heuristic search based on this method is used to formulate the day-ahead plans of OLTC and CBs. Finally, the dispatch plans of OLTC and CBs are applied to recalibrate the day-ahead dispatch plans of DGs. Comparing with existing schemes, strategies and optimization approaches based on diverse evaluation indicators, optimization results on IEEE 33-bus, IEEE 14-bus, and PG&E 69-bus test systems verify the practicality and efficiency of the proposed method. In addition, the effects on the optimization results with different adjustment step and action time of the discrete adjust devices are discussed by the IEEE 33-bus test system. [ABSTRACT FROM AUTHOR]

Higher levels of renewable electricity generation increase uncertainty in power system operation. To ensure secure system operation, new tools that account for this uncertainty are required. In this paper, we adopt a chance-constrained AC optimal power flow formulation, which guarantees that generation, power flows, and voltages remain within their bounds with a predefined probability. We then discuss different chance-constraint reformulations and solution approaches for the problem. We first describe an analytical reformulation based on partial linearization, which enables us to obtain a tractable representation of the optimization problem. We then provide an efficient algorithm based on an iterative solution scheme which alternates between solving a deterministic AC optimal power flow problem and assessing the impact of uncertainty. The flexibility of the iterative scheme enables not only scalable implementations, but also alternative chance-constraint reformulations. In particular, we suggest two sample-based reformulations that do not require any approximation or relaxation of the AC power flow equations. In a case study based on four different IEEE systems, we assess the performance of the method, and demonstrate scalability of the iterative scheme. We further show that the analytical reformulation accurately and efficiently enforces chance constraints in both in- and out-of-sample tests, and that the analytical reformulations outperforms the two alternative, sample-based chance constraint reformulations. [ABSTRACT FROM PUBLISHER]

This paper develops a real-time algorithmic framework for aggregations of distributed energy resources (DERs) in distribution networks to provide regulation services in response to transmission-level requests. Leveraging online primal-dual-type methods for time-varying optimization problems and suitable linearizations of the nonlinear AC power-flow equations, this work establishes a system-theoretic foundation to realize the vision of distribution-level virtual power plants. The optimization framework controls the output powers of dispatchable DERs such that, in aggregate, they respond to automatic generation control and/or regulation-services commands. This is achieved while concurrently regulating voltages within the feeder and maximizing customers’ and utility's performance objectives. Convergence and tracking capabilities are analytically established under suitable modeling assumptions. Simulations are provided to validate the proposed approach. [ABSTRACT FROM AUTHOR]

This paper focuses on distribution systems featuring renewable energy sources (RESs) and energy storage systems, and presents an AC optimal power flow (OPF) approach to optimize system-level performance objectives while coping with uncertainty in both RES generation and loads. The proposed method hinges on a chance-constrained AC OPF formulation, where probabilistic constraints are utilized to enforce voltage regulation with prescribed probability. A computationally more affordable convex reformulation is developed by resorting to suitable linear approximations of the AC power-flow equations as well as convex approximations of the chance constraints. The approximate chance constraints provide conservative bounds that hold for arbitrary distributions of the forecasting errors. An adaptive strategy is then obtained by embedding the proposed AC OPF task into a model predictive control framework. Finally, a distributed solver is developed to strategically distribute the solution of the optimization problems across utility and customers. [ABSTRACT FROM PUBLISHER]

In this paper, a novel solution to the optimal reactive power dispatch (ORPD) problem is proposed. The nonlinearity of the power flow equations is handled by a new successive linear approximation approach. For the voltage magnitude terms, a mathematical transformation that improves the accuracy and facilitates the linear modeling of shunt capacitors is used. Without loss of accuracy, the load tap changers and shunt capacitors are both modeled by linear constraints using discrete variables, which facilitates the linearly constrained mixed-integer formulation of the proposed ORPD model. An efficient iterative solving algorithm is introduced. The obtained solution strictly satisfies the power flow equations. Case studies on several IEEE benchmark systems show that the proposed algorithm can efficiently provide near-optimal solutions with the error of the objective functions of less than 0.1%. Compared with several commercial solvers, the proposed method shows distinct advantages in terms of both robustness and efficiency. Moreover, based on the round-off results, a heuristic method that reduces the optimization ranges of the discrete control variables is proposed. This method can further improve the computational efficiency with small losses in accuracy. [ABSTRACT FROM AUTHOR]

Power flow analysis is one of the tools that is required in most of the distribution system studies. An important characteristic of distribution systems is the load unbalance in the phases and a three-phase power flow analysis is needed. In this paper, a three-phase linear power flow (3LPF) formulation is derived based on the fact that in a typical distribution system, voltage angles and magnitudes vary within relatively narrow boundaries. The accuracy of the proposed 3LPF is verified using several test cases. Potential applications of the proposed method are in distribution systems state estimation and volt-VAR optimization. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a method to optimally set the reactive power contributions of distributed energy resources (DERs) present in distribution systems with the goal of regulating bus voltages. For the case when the network is balanced, we use the branch power flow modeling approach for radial power systems to formulate an optimal power flow (OPF) problem. Then, we leverage properties of the system operating conditions to relax certain nonlinear terms of this OPF, which results in a convex quadratic program (QP). To efficiently solve this QP, we propose a distributed algorithm based on the Alternating Direction Method of Multipliers (ADMM). Furthermore, we include the unbalanced three-phase formulation to extend the ideas introduced for the balanced network case. We present several case studies to demonstrate the method in unbalanced three-phase distribution systems. [ABSTRACT FROM PUBLISHER]

This paper proposes a tractable and scalable algorithm to identify and analyze bifurcation points of a large-scale power system model, which are directly related to dynamic voltage instability problems. Different types of bifurcations are analyzed, including: saddle-node (fold), Hopf, singularity-induced and limit-induced. An algorithm that combines optimization and predictor-corrector procedure is proposed for equilibrium tracing. The algorithm is based on calculation of only critical (rightmost and closest-to-zero) eigenvalues. The proposed algorithm is extended to the case of dynamic voltage stability assessment for power systems with optimized topology (simultaneously subjected to the topology control changes and generation re-dispatch). The proposed approach is illustrated on two (medium- and large-scale real-world) test power systems. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a method to optimally set the tap position of voltage regulation transformers in distribution systems. We cast the problem as a rank-constrained semidefinite program (SDP), in which the transformer tap ratios are captured by 1) introducing a secondary-side “virtual” bus per transformer, and 2) constraining the values that these virtual bus voltages can take according to the limits on the tap positions. Then, by relaxing the non-convex rank-1 constraint in the rank-constrained SDP formulation, one obtains a convex SDP problem. The tap positions are determined as the ratio between the primary-side bus voltage and the secondary-side virtual bus voltage that result from the optimal solution of the relaxed SDP, and then rounded to the nearest discrete tap values. To efficiently solve the relaxed SDP, we propose a distributed algorithm based on the alternating direction method of multipliers (ADMM). We present several case studies with single- and three-phase distribution systems to demonstrate the effectiveness of the distributed ADMM-based algorithm, and compare its results with centralized solution methods. [ABSTRACT FROM AUTHOR]

This paper presents a new control strategy for high voltage direct current (HVDC) transmission based on the synchronverter concept: the sending-end rectifier controls emulate a synchronous motor (SM), and the receiving end inverter emulates a synchronous generator (SG). The two converters connected with a DC line provide what is called a synchronverter HVDC (SHVDC). The structure of the SHVDC is firstly analyzed. It is shown that the droop and voltage regulations included in the SHVDC structure are necessary and sufficient to well define the behavior of SHVDC. The standard parameters of the SG cannot be directly used for this structure. A specific tuning method of these parameters is proposed in order to satisfy the usual HVDC control requirements. The new tuning method is compared with the standard vector control in terms of local performances and fault critical clearing time (CCT) in the neighboring zone of the link. The test network is a 4-machine power system with parallel HVDC/AC transmission. The results indicate the contribution of the proposed controller to enhance the stability margin of the neighbor AC zone of the link. [ABSTRACT FROM AUTHOR]

In this paper, we establish a mathematical model of the multi-objective reactive power optimization (MORPO) problem in an ac–dc hybrid power system. To get an evenly distributed set of Pareto solutions of this MORPO problem throughout a whole day, we propose a piecewise normalized normal constraint (PNNC) method. Based on this method, the Pareto frontier is divided into four arcs so that the fluctuation of angle between the tangent line at each Pareto point and its sub-Utopia line is reduced. By doing so, the original MORPO problem is transformed into four sets of single objective optimization problems, which can be solved efficiently by a nonlinear primal-dual interior-point method. Performance of the PNNC method is tested on a real ac–dc interconnection system. The results demonstrate that it has the capability to generate a more evenly distributed set of Pareto solutions when compared with the normalized normal constraint (NNC) and weighted sum (WS) methods. In addition, a fuzzy-based membership value assignment method is employed to derive 96 optimal compromise solutions corresponding to 96 time periods in a whole day; the numerical outcomes show that a saving in active power loss and an improvement in the voltage profile can be gained simultaneously. [ABSTRACT FROM PUBLISHER]