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This paper proposes an optimization approach for day-ahead reactive power planning to improve voltage security in transmission networks. The problem is formulated as a voltage-secure multi-period optimal reactive power dispatch (MP-ORPD) problem. The optimization approach searches for optimal set-points of dynamic and static reactive power (var) resources. Specifically, the output includes set-points for switching shunts, transformer taps, and voltage magnitudes at the regulated buses. The primary goal is to maximize the dynamic reactive power reserve of the system, by minimizing the reactive power supplied by synchronous generators. As the size of the MP-ORPD problem increases significantly with increasing number of contingencies and time periods, efficiency is crucial for practical applications. In this paper, a decomposition technique based on consensus and alternating optimization, where integer variable targets are obtained via MILP, is used to partition the MP-ORPD problem into a set of subproblems, which can be solved in parallel to reduce the computation time. The proposed MP-ORPD problem and its solution algorithm are integrated into the EPRI-VCA software. The results of various power networks of large electric utilities in the Eastern interconnection demonstrate the effectiveness of the proposed algorithm in providing preventive control schedules. [ABSTRACT FROM AUTHOR]

Volt/VAr Optimization (VVO) is becoming increasingly crucial in distribution management systems with renewable sources that require setting their reactive power in coordination with legacy voltage and reactive power control devices. This paper presents a VVO method that operates in complex variables based on the Wirtinger calculus; it employs a penalty method that keeps voltage magnitudes and controller parameters within limits together with a probabilistic rounding technique for handling switched device variables. The complex variable implementation permits using the compensation technique when solving the Karush-Kuhn Tucker (KKT) system, thus achieving a significant speedup due to limiting the number of linear system factorizations. The proposed method is contrasted with a classical VVO implementation employing discrete coordinate search from the current operating point and an enhanced version that uses sensitivity information. Numerical results on distribution networks having up to 3147 nodes show that the proposed method is significantly faster than classical methods and gives operating points free of voltage magnitude violations and lower power loss. [ABSTRACT FROM AUTHOR]

The synchronization requirement is a bottleneck of many distributed optimization algorithms, particularly for solving problems with computationally heterogeneous subproblems and during the occurrence of communication failure/delay. This paper presents a double-loop learning-aided asynchronous alternating direction method of multipliers (LA-ADMM) that has information prediction capability and handles a considerable level of asynchrony between subproblems. A momentum-extrapolation prediction-correction technique is developed to enable subproblems to predict their neighbors missing shared variable information instead of using the latest received values. An online streaming-based anomaly classification is designed to observe the performance of predicted data and control Lagrange multipliers update over the course of iterations. The proposed LA-ADMM reduces under-utilization of computation resources, especially if subproblems are computationally heterogeneous. This algorithm also enhances distributed optimization robustness against communication failure/delay that may result in a considerable level of asynchrony between subproblems. LA-ADMM is applied to solve the optimal power flow problem for several test systems. Promising results are obtained as compared to the classical synchronous ADMM and asynchronous ADMM without the anomaly switch control. [ABSTRACT FROM AUTHOR]

In this paper, we propose a topology-cognizant optimal power flow (OPF) paradigm with additional safety constraints for multi-terminal direct current (MTDC) grids. The resulting formulation is concerned with the optimization of controller set-points, i.e., voltage and power levels at each bus, and the switching status of transmission lines that collectively referred to as grid topology. A pair of additional safety constraints are integrated into the problem formulation to prevent voltage violations caused by power fluctuations in between two controller set-point updates. Searching for a grid topology that offers more efficient operation leads to a mixed-integer nonlinear program (MINLP) which is computationally challenging due to: i) Non-convex power flow equations, (ii) Non-convex converter loss equations, and iii) Binary variables accounting for the operational status of transmission lines. Non-convexities of power flows and converter loss equations are tackled by means of a mixed-integer second-order cone programming (MISOCP) relaxation, while the optimal switching status of transmission lines are determined via a branch-and-bound search. Numerical results for the modified IEEE 14, 30, and 57-bus systems are used to verify the merits of the proposed method. Furthermore, this method is experimentally validated using the CIGRE B4 DC grid benchmark in a real-time hardware-in-the-loop platform. [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]

In this paper, we present a two-stage model of zonal electricity markets with day-ahead market clearing and real-time re-dispatch and balancing that accounts for transmission line switching at both stages. We show how the day-ahead problem with switching can be formulated as an adaptive robust optimization problem with mixed integer recourse and present a new algorithm for solving the adversarial max-min problem that obeys the structure of an interdiction game. We apply the model on a realistic instance of the Central Western European system and comment on the impacts of both proactive and reactive transmission switching on the operating costs of the system. Part I presents day-ahead models of a short-term zonal electricity market with switching, and describes our algorithmic approach for solving these models efficiently. Part II describes variants of the real-time model, and presents the results of our case study on the Central Western European market. [ABSTRACT FROM AUTHOR]

In this paper, we present a two-stage model of zonal electricity markets with day-ahead market clearing, and real-time re-dispatch and balancing that accounts for transmission line switching at both stages. We show how the day-ahead problem with switching can be formulated as an adaptive robust optimization problem with mixed integer recourse, and present a new algorithm for solving the adversarial max-min problem that obeys the structure of an interdiction game. We apply the model on a realistic instance of the Central Western European system, and comment on the impacts of both proactive, and reactive transmission switching on the operating costs of the system. Part I presents day-ahead models of a short-term zonal electricity market with switching, and describes our algorithmic approach for solving these models efficiently. Part II describes variants of the real-time model, and presents the results of our case study on the Central Western European market. [ABSTRACT FROM AUTHOR]

Boosting the resilience of power systems is one of the core requirements of smart grid. In this paper, an integrated resilience response framework is proposed, which not only links the situational awareness with resilience enhancement, but also provides effective and efficient responses in both preventive and emergency states. The core of the proposed framework is a two-stage robust mixed-integer optimization model, whose mathematical formulation is presented in this paper as well. To solve the above model, an algorithm based on the nested column-and-constraint generation decomposition is provided, and computational efficiency improvement techniques are proposed. Preventive response in this paper considers generator re-dispatch and topology switching, while emergency response includes generator re-dispatch, topology switching and load shedding. Several numerical simulations validate the effectiveness of the proposed framework and the efficiency of the solution methodology. Key findings include the following: 1) in terms of enhancing power grid resilience, the integrated resilience response is preferable to both independent preventive response and independent emergency response; 2) the power grid resilience could be further enhanced by utilizing topology switching in the integrated resilience response. [ABSTRACT FROM PUBLISHER]

The distribution network restoration problem is by nature a mixed integer and non-linear optimization problem due to the switching decisions and Optimal Power Flow (OPF) constraints, respectively. The link between these two parts involves logical implications modelled through big-M coefficients. The presence of these coefficients makes the relaxation of the mixed-integer problem using branch-and-bound method very poor in terms of computation burden. Moreover, this link inhibits the use of classical Benders algorithm in decomposing the problem because the resulting cuts will still depend on the big-M coefficients. In this paper, a novel decomposition approach is proposed for the restoration problem named Modified Combinatorial Benders (MCB). In this regard, the reconfiguration problem and the OPF problem are decomposed into master and sub problems, which are solved through successive iterations. In the case of a large outage area, the numerical results show that the MCB provides, within a short time (after a few iterations), a restoration solution with a quality that is close to the proven optimality when it can be exhibited. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for generating restoration sequences in distribution systems and microgrids with emphasis on deriving optimal switching times for remote control switches. Microgrid architecture considered are assumed to be operating in islanded single master mode. Control methods for handling the unbalance inherent in distribution systems using single-phase controlled PQ inverters were developed and incorporated in the proposed method. The problem of considering simultaneously both energy optimization and dynamic stability of the system, which is lacking in existing methods, was addressed by studying the transient behavior of microgrids based on extended electromagnetic transients program (EMTP) simulations and then approximate and incorporate these behaviors into the optimization formulation for restoration. The approach is based on multi-objective optimization of final restored load and total restoration time. The methodology was studied on a modified IEEE 123 node test feeder. An EMTP simulation in PSCAD of the formed microgrids was used to validate the accuracy of the dynamic and steady-state characteristics of the resulting systems. By considering the transient behavior of distributed generators (DGs) during restoration, a better restoration sequence that does not trigger protection relays could be realized. [ABSTRACT FROM AUTHOR]

Intentional controlled islanding (ICI) and black start allocation (BSA) are two examples of problems in the power systems literature that have been formulated as mixed integer programs (MIPs) and allow reconfiguration of the power system topology. A key consideration in both of these problems is that each island that appears after a reconfiguration must have at least one energized generator. In this paper, we examine three alternative MIP formulations for this restriction, show their equivalence, and prove that two of them are stronger in terms of their linear programming relaxation than the formulation most commonly used in the power systems literature. Since the time to solve MIPs can vary significantly between equivalent formulations, we also present computational experiments on the IEEE test systems for the ICI and BSA problems. We observe that a polynomially separable, exponential in size, strong formulation yields the best performance for the BSA problem and exhibits a comparable performance to a linear in size, weak formulation for the ICI problem. [ABSTRACT FROM AUTHOR]

An optimal planning model of the sectional switch and tie line for the distribution network is proposed considering the tradeoff between the system reliability and economy. First, the fault incidence matrices are established for the distribution network, and the explicit analytical expression of the system reliability index is realized by the algebraic operation of the fault incidence matrices and fault parameter vectors. Then, the 0–1 integer quadratically constrained optimization model is established for the sectional switch configuration considering the capacity constraint of the tie line. Finally, the sectional switches and tie lines are optimized jointly to ensure the economic investment and maximize the system reliability. The distribution network in Taiwan Power Company is used to verify the applicability of the proposed optimization method. Compared with the intelligent optimization algorithm, the mathematical programming method in this paper can always find the global optimal switch configuration quickly. In addition, the influence of the tie line capacity constraints on the sectional switch configuration is also considered, which can provide practical reference for the power network planners in the optimization design of the distribution network. [ABSTRACT FROM AUTHOR]

The new IEEE 1547-2018 standard includes dynamic Volt/VAr control for photovoltaic (PV) smart inverters. While recent research has addressed the problem of optimal inverter dispatch, the interaction between inverters and the classical Volt/VAr control (VVC) regulation system needs further study. This paper proposes an approach that builds on the classical VVC solution with PVs, by computing for each inverter a rule that modulates the smart inverter reactive power in function of its real power. Two approaches are presented for computing the inverter's reactive power equation slope: the first approach is based on the robust minimization of the absolute voltage magnitude deviation via a linear program, whereas the second approach yields closed-form solutions inspired from distributionally robust chance constraints. Numerical results are presented on weakly meshed distribution networks having up to 3146 nodes; they demonstrate that the voltage violations due to intermittent real power variations are significantly reduced by the decision rules as compared to maintaining the last computed VVC set-points, even when the inverters operate at constant power factor or with default Volt/VAr settings proposed in the literature; additionally, the reactive power decision rules maintain a network loss level that is close to the average from a centralized solution. [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]

Smart inverters provide additional control capability to help optimize the operation of distribution systems. This paper proposes a framework for exact optimal active and reactive power dispatch of distributed photovoltaic (PV) generation, switched capacitors, and voltage regulators in large multi-phase unbalanced distribution systems. The objectives of the optimal dispatch are minimization of the energy loss, PV active power curtailment, and operations of capacitors and voltage regulators, in addition to elimination of voltage violations and reverse power flow. The optimization problem is formulated in rectangular coordinates as a nonlinear, nonconvex problem. Effective computational strategies are proposed to allow the application of predictor-corrector primal-dual interior point method to solve optimization problems in real-time with a large number of constraints and variables, including discrete variables corresponding to switched capacitors and voltage regulators. The accuracy of the numerical solution and the ability to implement the proposed framework are validated using the unbalanced multi-phase IEEE 34-bus and EPRI 2,998-bus distribution systems with 15-minute load and PV data. The results show a significant loss reduction and elimination of both voltage violations and reverse power flow. [ABSTRACT FROM AUTHOR]

Inadequate frequency response can arise due to a high penetration of wind turbine generators (WTGs) and requires a frequency support function to be integrated in the WTG. The appropriate design for these controllers to ensure adequate response has not been investigated thoroughly. In this paper, a safety supervisory control (SSC) is proposed to synthesize the supportive modes in WTGs to guarantee performance. The concept, region of safety (ROS), is stated for safe switching synthesis. An optimization formula is proposed to calculate the largest ROS. By assuming a polynomial structure, the problem can be solved by a sum of squares program. A feasible result will generate a polynomial, the zero sublevel set of which represents the ROS and is employed as the safety supervisor. A decentralized communication architecture is proposed for small-scale systems. Moreover, a scheduling loop is suggested so that the supervisor updates its boundary with respect to the renewable penetration level to be robust with respect to variations in system inertia. The proposed controller is first verified on a single-machine three-phase nonlinear microgrid, and then implemented on the IEEE 39-bus system. Both results indicate that the proposed framework and control configuration can guarantee adequate response without excessive conservativeness. [ABSTRACT FROM AUTHOR]

Static security constraint management, formulated as security constrained optimal power flow (SCOPF), is one of the critical tasks performed during planning and operation. These constraints at any operating point can be divided into noncritical and critical constraints, where former can be managed with preventive controls, while the latter cannot. The violation of at least one critical constraint indicates that network is overstressed and needs a prompt activation of remedial actions to preserve system integrity. Accordingly, critical constraint violations (CCVs) must be identified by solving related SCOPF problem, as information on their types and locations can help to devise and activate optimal remedial actions. However, when dealing with overstressed systems, conventional SCOPF methods may not be able to identify CCVs due to problem infeasibility and there is no commonly accepted method to find related CCVs. Extending previously developed metaheuristic approach, this paper first identifies the CCVs and then proposes a novel methodology to devise the most effective remedial actions to mitigate overstressed system operating conditions. The practical relevance of the remedial actions is demonstrated using IEEE 30-bus, 39-bus, and 57-bus test networks. [ABSTRACT FROM AUTHOR]

High congestion costs and new congestion patterns, formed by intermittent renewable generation, demand for more efficient utilization of the transmission system. Transmission switching (TS) and variable-impedance series FACTS devices are two technologies that provide such efficiency gains through controlling the power flows. As the system operators begin to utilize these tools, it is essential to understand the interdependence between them at various stages, such as planning and operation. This paper develops an optimal power flow framework that incorporates both FACTS and TS to study the link between the two power flow control technologies. The simulation results on an RTS 96-bus system suggest that: 1) substantial economic savings can be achieved through utilization of both TS and FACTS, beyond the independent capabilities of each technology; 2) performing TS actions affect the optimal location and set point of FACTS devices; and 3) operation of FACTS devices affect the location and frequency of TS actions. It is, thus, essential to acknowledge the interdependence between the two technologies both at the planning and operation stage. Failure to do so will lead to economic inefficiencies that can be avoided through co-optimization of generation dispatch, TS, and FACTS. [ABSTRACT FROM PUBLISHER]

Microgrid formation is a potential solution in postdisaster electric grid recovery efforts. Recent works propose a distribution level microgrid formation model that applies to radial distribution systems characterized by directed power flows. However, with high renewable penetration levels in future power systems, the flows are expected to be undirected even in distribution grids. In this paper, we develop a model to deal with the restoration process of future power systems, embedding some of the characteristics these systems are likely to have. More specifically, our formulation can deal with radial and meshed topologies, and it requires little preprocessing of the input data. Additionally, we extend the model to allow for possible mobile and fixed distributed generation technologies and distributed energy resources, and explicitly include demand responsive loads with a minimum satisfiability constraint. Thus, this extended formulation can be used as an operation and a short term planning tool for the DG scenario-based location problem. We compare our extended formulation results to those obtained for the radial cases found in the literature. Also, we apply the proposed approach to other instances of the problem based on the IEEE 37-bus, 30-bus, and 118-bus systems. [ABSTRACT FROM PUBLISHER]

Voltage instability occurs when a power system is unable to meet the reactive power demand, and is typically corrected by switching on additional reactive power devices such as capacitor banks. Real-time monitoring and communication technologies can potentially improve voltage stability by enabling the rapid detection of low voltages and the implementation of corrective actions. These corrective actions, however, will only be effective in restoring stability if they are chosen in a timely, scalable manner. In this paper, we propose a submodular optimization approach for designing a control strategy that prevents voltage instability. Our key insight is that the voltage deviation from the desired level is a supermodular function of the set of reactive power injections that are employed, leading to computationally efficient control algorithms for stabilization with provable optimality guarantees. This submodular control framework is tested on the IEEE 300-bus transmission system. [ABSTRACT FROM PUBLISHER]

This paper presents a methodology for integrated planning of real-life medium-voltage networks based on the utility planning concepts. The research is motivated by the need to develop a methodology that would line-up with utility day-to-day businesses and could be applied in real-life. Its core is a two-stage optimization process, where the first stage solves the static investment optimization and the second stage considers operational problem. A probabilistic decision tree approach is proposed for the solution of the entire problem to consider uncertainties in the planning period. The overall formulation is given first, which is followed by details of the investment model and outlines of the proposed operation planning. The novelty of the investment problem, which determines optimal network reinforcements, is explicit modeling of network security constraints of radially operated networks, whilst considering different operating regimes. Additional novel features include modeling of real-life supply restoration rules through network reconfiguration and optimal placement of new switching devices, as well as consideration of “customer flows” on the network. Connection of new distributed generation and demand centers and construction of circuits on new corridors are also included. Two investment models are formulated as mixed-integer nonlinear optimization problems, tested on several MV networks and compared with established methods. The proposed operational problem is solved in two stages, quality-of-supply and operation cost optimization. Computational aspects are also presented. [ABSTRACT FROM PUBLISHER]

The discrete coordinate-descent algorithm is a practical approach that is currently used in centralized Volt/VAr Control (VVC) implementations, mainly due to its good performance and speed for real-time applications. Its viability is however challenged by the increasing number of distributed generation that contribute to the VVC solution, in addition to the conventional transformer taps and switched capacitors. This paper presents the exact computation of sensitivity factors that speed up the discrete coordinate-descent implementation, by significantly reducing the number of forward/backward substitutions in the current injection power flow method; the speed up is achieved without affecting the control setting quality of the original implementation. The optimality of the discrete coordinate-descent solutions is investigated by computing the gaps relative to mixed-integer linear programming set-points, derived from a polyhedral reformulation of the VVC problem. The sensitivity-based discrete coordinate-descent algorithm is tested starting from two initial points, the default one given by the current control set-points, and a continuous solution obtained from a linear approximation of the VVC problem. Numerical results on networks with up to 3145 nodes show that the sensitivity-based approach significantly improves the runtime of the discrete coordinate-descent algorithm, and that the linear programming initialization leads to VVC solutions with gaps relative to the mixed-integer set-points that are less than 0.5%. [ABSTRACT FROM PUBLISHER]

Reserve requirements serve as a proxy for N-1 reliability in the security-constrained unit commitment (SCUC) problem. However, there is no guarantee that the reserve is deliverable for all scenarios (post-contingency states). One cheap way to improve reserve deliverability is to harness the flexibility of the transmission network. Flexible AC transmission system (FACTS) devices are able to significantly improve the transfer capability. However, FACTS utilization is limited today due to the complexities these devices introduce to the DC optimal power flow problem (DCOPF). With a linear objective, the traditional DCOPF is a linear program (LP); when variable impedance based FACTS devices are taken into consideration, the problem becomes a nonlinear program (NLP). A reformulation of the NLP to a mixed integer linear program, for day-ahead corrective operation of FACTS devices, is presented in this paper. Engineering insight is then introduced to further reduce the complexity to an LP. Although optimality is not guaranteed, the simulation studies on the IEEE 118-bus system show that the method finds the globally optimal solution in 98.8% of the cases. Even when the method did not find the optimal solution, it was able to converge to a near-optimal solution, which substantially improved the reliability, very quickly. [ABSTRACT FROM PUBLISHER]

This paper introduces a new method of removing thermal overloads and voltage limits in an electric power system by means of the Evolution of Corrective and Preventive control Actions (ECPA). The goal is to find the minimum number of control actions that solve the identified limit violations at minimum cost. A recombination operator based on form theory allows the codification of control actions in a natural and simple way. ECPA has been tested on the IEEE 30-bus and the IEEE 118-bus systems. The limit violations are solved at minimum cost and with fewer control actions on average than alternative methods. [ABSTRACT FROM AUTHOR]

Most existing approaches for distribution network reconfiguration assume that the distribution system is (three-phase) balanced and a single-phase equivalent is used. However, distribution feeders are usually unbalanced due to a large number of single-phase loads, nonsymmetrical conductor spacing, and three-phase line topology. This paper builds on our previous work and studies feeder reconfiguration for unbalanced distribution systems with distributed generation (DG). The three-phase bus locations and sizes of DG units are determined using sensitivity analysis and nonlinear programming, respectively, and distribution feeders are reconfigured every hour based on the status of time-varying loads, output power from DG units and faults on the network while minimizing the energy loss costs and DG operating costs. Simulation results show the effectiveness and computational efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive mathematical model to solve the restoration problem in balanced radial distribution systems. The restoration problem, originally modeled as mixed integer nonlinear programming, is transformed into a mixed integer second-order cone programming problem, which can be solved efficiently using several commercial solvers based on the efficient optimization technique family branch and bound. The proposed mathematical model considers several objectives in a single objective function, using parameters to preserve the hierarchy of the different objectives: 1) maximizing the satisfaction of the demand, 2) minimizing the number of switch operations, 3) prioritizing the automatic switch operation rather than a manual one, and 4) prioritizing especial loads. General and specialized tests were carried out on a 53-node test system, and the results were compared with other previously proposed algorithms. Results show that the mathematical model is robust, efficient, flexible, and presents excellent performance in finding optimal solutions. [ABSTRACT FROM PUBLISHER]

A smart distribution system should restore service to interrupted customers as quickly as possible after an outage. Upgrading manual switches to remote-controlled switches (RCSs) enhances restoration capability. The placement of RCSs should consider both functional and economic requirements. This paper presents a systematic method to determine the set of switches to be upgraded for an existing distribution system. The maximum restoration capability is achieved by upgrading a near-minimum number of manual switches to RCSs. The RCS placement problem is formulated as a weighed set cover (WSC) problem. A greedy algorithm with a polynomial-time computational efficiency is designed to generate a near-optimal solution for the WSC problem. A 3-feeder 9-node test system and a 4-feeder 1069-node unbalanced test system with microgrids are used to validate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper presents a hierarchical decentralized agent-based network reconfiguration methodology to minimize power losses for smart distribution systems. A novel formulation of the network reconfiguration problem is given in which the switch states are the decision variables, and a single-loop branch-exchange heuristic algorithm is used to solve the optimization problem. The entire distribution system is decomposed into subsystems based on the connectivity of areas, and an individual agent is assigned to each decomposed subsystem. A two-stage method is defined for coordinating the reconfigurations of decomposed subsystems. The optimal configuration of the entire system results from collaborations of individual agents, and the decentralized approach significantly reduces the computation time. A 118-bus distribution system is used as the example to illustrate the implementation of the method. The development of a demonstration system and more detailed simulation studies are given in Part II to further illustrate the application of the proposed decentralized approach. [ABSTRACT FROM PUBLISHER]

This paper proposes a new transmission-constrained unit commitment method that combines the cost-efficient but computationally demanding stochastic optimization and the expensive but tractable interval optimization techniques to manage uncertainty on the expected net load. The proposed hybrid unit commitment approach applies the stochastic formulation to the initial operating hours of the optimization horizon, during which the wind forecasts are more accurate, and then switches to the interval formulation for the remaining hours. The switching time is optimized to balance the cost of unhedged uncertainty from the stochastic unit commitment against the cost of the security premium of the interval unit commitment formulation. These hybrid, stochastic, and interval formulations are compared using Monte Carlo simulations on a modified 24-bus IEEE Reliability Test System. The results demonstrate that the proposed unit commitment formulation results in the least expensive day-ahead schedule among all formulations and can be solved in the same amount of time as a full stochastic unit commitment. However, if the range of the switching time is reduced, the hybrid formulation in the parallel computing implementation outperforms the stochastic formulation in terms of computing time. [ABSTRACT FROM PUBLISHER]

This paper deals with the management of voltage constraints in active distribution systems that host a significant amount of distributed generation (DG) units. To this end we propose a centralized optimization approach which aims at minimizing the amount of MW curtailment of non-firm DG to remove voltage constraints. The salient feature of this approach is that it comprehensively and properly models the full variety of possible control means (i.e., DG active/reactive power including DG shut-down, on load tap changing transformer ratio, shunt capacitor, and remotely controlled switches or breakers), most of which having a discrete behavior. We develop and compare the performances of two optimization models on a snapshot basis for various distribution systems up to 1089 buses. In particular we show that the use of remotely controlled switches so as to transfer DG between feeders in case of voltage constraints may lead to significant reduction of the DG curtailment. [ABSTRACT FROM AUTHOR]

This paper proposes a two-stage robust optimization approach to solve the N-k contingency-constrained unit commitment (CCUC) problem. In our approach, both generator and transmission line contingencies are considered. Compared to the traditional approach using a given set of components as candidates for possible failures, our approach considers all possible component failure scenarios. We consider the objectives of minimizing the total generation cost under the worst-case contingency scenario and/or the total pre-contingency cost. We formulate CCUC as a two-stage robust optimization problem and develop a decomposition framework to enable tractable computation. In our framework, the master problem makes unit commitment decisions and the subproblem discovers the worst-case contingency scenarios. By using linearization techniques and duality theory, we transform the subproblem into a mixed-integer linear program (MILP). The most violated inequalities generated from the subproblem are fed back into the master problem during each iteration. Our approach guarantees a globally optimal solution in a finite number of iterations. In reported computational experiments, we test both primal and dual decomposition approaches. Our computational results verify the effectiveness of our proposed approach. [ABSTRACT FROM PUBLISHER]

This second part of a two-part paper presents the implementation and simulations of a risk-based optimization approach for distribution expansion planning with distributed generation (DG), as described in Part I. The proposed approach is first applied to a test system in order to test convergence of the optimization algorithm and then comparing its numerical results with the ones from the exhaustive method. Later, the expansion planning of a typical Latin American distribution network is assessed and analyzed. For this, two expansion optimizations are performed: one, taking into account traditional expansion alternatives (without DG) and, the other, considering DG as flexible expansion options. Finally, the computational effort is analyzed. Results show that the greatest contribution of DG lies in the flexibility it gives to expansion planning, mainly by deferring network reinforcements. [ABSTRACT FROM PUBLISHER]

Black start is the primary procedure deployed for rapid recovery from a complete blackout. For isolated power systems such as the Taiwan power system (TPS), a reliable and efficient black start procedure is more important than interconnected power systems. In this paper, a black start decision-supporting system (BSS) with an interactive graphical user interface (GUI) has been developed. BSS can rapidly generate optimal black start strategies according to the updated system data configuration and objective function; furthermore, BSS can automatically simulate the strategies and visualize the results. By applying the BSS to evaluate the black start strategies for TPS, the effectiveness of BSS has been demonstrated. The BSS has been utilized by Taiwan Power Company (TPC) for black start planning with comparison to traditional manual planning. With the aid of BSS, the dispatchers are equipped with more support and the restoration risk can be much alleviated. [ABSTRACT FROM PUBLISHER]

This paper presents two hybrid heuristic methods for reconfiguration of the radial electrical distribution system. The first stage of the proposed techniques is a circular minimum-branch-current updating mechanism to bypass the local optimum points. Then, by a circular neighbor-chain updating technique, the best known configuration is obtained. The proposed approaches have been tested on the six well-known electrical distribution networks. The results show that the proposed methods have remarkably achieved the best-known optimum solution with more reliability, accuracy and efficiency than many other published methods. [ABSTRACT FROM PUBLISHER]

In power system operation, transmission congestion can drastically limit more economical generation units from being dispatched. In this paper, optimal transmission switching as a congestion management tool is utilized to change network topology which, in turn, would lead to higher electricity market efficiency. Transmission switching (TS) is formulated as an optimization problem to determine the most influential lines as candidates for disconnection. In order to relieve congestion without violating voltage security, TS is embedded in an optimal power flow (OPF) problem with AC constraints and binary variables, i.e., a mixed-integer nonlinear programming (MINLP) problem, solved using Benders decomposition. Also, a methodology is presented which provides a guideline to the system operator showing the order of switching manoeuvres that have to be followed in order to relieve congestion. It is also shown that TS based on DC optimal power flow (DCOPF) formulation as used in the literature may jeopardize system security and in some cases result in voltage collapse due to the shortcomings in its simplified models. In order to evaluate the applicability and effectiveness of the proposed method, the IEEE 57-bus and IEEE 300-bus test systems are used. [ABSTRACT FROM PUBLISHER]

In power system reconfiguration, the status (ON/OFF) of switches are optimized such that maximum power is delivered to loads after the occurrence of a fault. The optimized reconfiguration is achieved by prioritizing power delivered to vital loads over semi-vital and nonvital loads. The formulation presented in this paper considers a new balanced hybrid (AC and DC) shipboard power system (SPS). Analysis of the nonconvex reconfiguration formulation is done by an appropriate nonconvex solver and by convex approximation. Unlike the nonconvex solution that is based on branch-and-bound methods, convex approximation significantly reduces complexity. It is shown that for the hybrid SPS reconfiguration problem, low complexity convex approximations are effective in finding optimal solutions. Cumulative distribution function (CDF) of the power delivered to loads is presented to showcase the system robustness against random fault scenarios. A combined objective of maximizing power delivery and minimizing the number of switching actions is included in the analysis. Tradeoff between power delivered and number of switching operations after reconfiguration has been discussed at steady state. A separate analysis is also included to observe the intermediate dynamic switch states while the reconfiguration is in progress to capture the trade-off more prominently. [ABSTRACT FROM PUBLISHER]

Electrical distribution network reconfiguration is a complex combinatorial optimization process aimed at finding a radial operating structure that minimizes the system power loss while satisfying operating constraints. In this paper, a harmony search algorithm (HSA) is proposed to solve the network reconfiguration problem to get optimal switching combination in the network which results in minimum loss. The HSA is a recently developed algorithm which is conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search which eliminates the need for derivative information. Simulations are carried out on 33- and 119-bus systems in order to validate the proposed algorithm. The results are compared with other approaches available in the literature. It is observed that the proposed method performed well compared to the other methods in terms of the quality of solution. [ABSTRACT FROM AUTHOR]

The authors discuss an article by M. Lavorato and colleagues on imposing radiality constraints in power distribution system optimization problems. They claim that the paper lacks any reference where an alternative mathematical formulation is proposed to deal with transfer nodes or zero-injection nodes. In their opinion, the possibility for transfer nodes is not realistic, being in opposition to the usual operation of the medium voltage (MV) systems where all the transfer nodes are connected to.