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In this work we present a novel technique, based on a trust-region optimization algorithm and second-order trajectory sensitivities, to compute the extreme trajectories of power system dynamic simulations given a bounded set that represents parametric uncertainty. We show how this method, while remaining computationally efficient compared with sampling-based techniques, overcomes the limitations of previous sensitivity-based techniques to approximate the bounds of the trajectories when the local approximation loses validity because of the nonlinearity. We present several numerical experiments that showcase the accuracy and scalability of the technique, including a demonstration on the IEEE New England test system. [ABSTRACT FROM AUTHOR]

The nonlinear and nonconvex nature of ac power flow challenges the analysis and optimization of unbalanced distribution networks. To tackle this issue, this paper proposes an online feedback-based linearized power flow model for unbalanced distribution networks with both wye-connected and delta-connected loads. The online feedback-based linearized model is grounded on the first-order Taylor expansion of the branch flow model (BFM), and updates the model parameters via online feedback by leveraging the instantaneous measurements of voltages and load consumption. Exploiting the connection structure of unbalanced radial distribution networks, we also provide a unified matrix-vector compact form of the model. The numerical tests on the IEEE 123-bus test system validate its accuracy and superiority compared with other offline BFM methods. Additionally, we apply the proposed model to the optimal power flow for voltage regulation, which further demonstrates its effectiveness. [ABSTRACT FROM AUTHOR]

Time-of-use (TOU) is an effective price-based demand response strategy. A reasonable design of TOU strategy can effectively reduce the peak-valley difference, and then produce a lot of benefits (such as delaying power grid investment, reducing interruption cost, and improving reliability). However, changing peak-valley period has a great influence on the peak-valley difference and power supply reliability of power system. Therefore, this paper aims to investigate the optimal modification of peak-valley period considering reliability loss under multiple TOU schemes. Firstly, this paper presents a clustering model and algorithm of optimal load curve based on a minimum error iteration method. Secondly, an optimal modification of peak-valley period based on a dynamic load point method is proposed, and the traditional peak-valley difference is replaced by the global peak-valley difference to calculate the objective function. Thirdly, this paper establishes a load–reliability relation fitting model based on the back propagation neural network. Finally, the effectiveness and correctness of the proposed method are investigated by the Roy Billinton test system and reliability test system. [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 parallel dual dynamic programming (PDDP)-based decentralized algorithm for the multi-area optimal power flow (MAOPF), which can preserve the information privacy and operational independence of each area. The MAOPF problem is decomposed into a series of subproblems for individual areas by the dual dynamic programming (DDP) algorithm, and the Benders cut-based value functions are used to reflect the impacts of one area’s decisions to the subsequent areas. The optimal solution of MAOPF can be obtained in a decentralized fashion, requiring only a limited amount of information exchange among neighbor areas. Moreover, a parallel processing technique is designed to avoid the waiting process of the basic DDP algorithm, thus accelerating the computing speed of the proposed decentralized algorithm. Compared with the existing decentralized algorithms, the proposed algorithm has better performance in terms of convergence and computational efficiency. In addition, there is no need for parameter tuning. Case studies on several IEEE test systems and a real 2298-bus system demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time coordinated scheduling method for active distribution networks (ADNs) with soft open points (SOPs) and plug-in electric vehicles (PEVs) via multi-timescale framework under uncertainties. Specifically, this method is achieved with day-ahead pre-scheduling and intra-day corrective control stages by coordinating various flexible resources at different timescales. The day-ahead stage is designed to reduce operational cost, regulate voltage profile and avoid risk exposure through joint scheduling of traditional devices, SOPs and PEVs on hourly basis. In intra-day corrective control stage, an hour is further divided into two timescales. The slow-timescale scheduling (STS) aims to corrective coordination of SOPs and across-time-and-space energy transmission of PEVs, and nested within the STS, the fast-timescale scheduling optimally coordinates the active & reactive power of SOPs and PV inverters to handle fast voltage fluctuations as well as against real-time uncertainties. The formulated three models are all transformed into second-order cone programming problems via sample weighted average approximation (SWAA), linearization and conic relaxation, which can be thus efficiently solved. Case studies based on three modified distribution systems (including two IEEE test systems and one actual distribution network) are performed to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a data-driven chance-constrained optimal gas-power flow (OGPF) calculation method without any prior assumption on the distribution of uncertainties of wind power generation. The Gaussian mixture model is employed to fit the uncertainty distribution, where the Bayesian nonparametric Dirichlet process is adopted to tune the component number. To facilitate the online application of the proposed methods, an online-offline double-track distribution construction approach is established, where the frequency of training the relatively time-consuming Dirichlet process Gaussian mixture model can be reduced. On account of the quadratic gas consumption expression of gas-fired generators as well as the linear decision rule based uncertainty mitigation mechanism, the chance constraints would become quadratic ones with quadratic terms of uncertainties, which makes the proposed model more intractable. An equivalent linear separable counterpart is then provided for the quadratic chance constraints, after which the intractable chance constraints could be converted into traditional linear ones. The convex-concave procedure is used to crack the nonconvex Weymouth equation in the gas network and the auxiliary quadratic equalities. Simulation results on two test systems validate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Extreme weather events pose a serious threat to energy distribution systems. We propose a distributionally robust optimization model for the resilient operation of the integrated electricity and heat energy distribution systems in extreme weather events. We develop a strengthened ambiguity set that incorporates both moment and Wasserstein metric information of uncertain contingencies, which provides a more accurate characterization of the true probability distribution. We first recast the proposed model into an equivalent framework which is similar to a conventional two-stage robust model and then utilize a modified column-and-constraint generation algorithm to solve the recast model. Numerical results from three test systems validate the enhanced resilience of the distributionally robust approach, the reduced conservatism of the strengthened ambiguity set, and the computational efficiency of the proposed solution algorithm. [ABSTRACT FROM AUTHOR]

The limitations of centralized optimization methods for power systems operation have led to the distributed computing paradigm, particularly in power distribution systems. The existing techniques reported in recent literature for solving distributed optimization problems are not viable for power distribution systems applications. The essential drawback remains a large number of required communication rounds, i.e., macro-iterations among the computing agents to solve one instance of the optimization problem; the typical number of macro-iterations are in the order of $10^2\sim 10^3$. In this paper, a new and scalable distributed optimization method based on Equivalent Network Approximation (ENApp) is proposed to solve optimal power flow (OPF) for a balanced radial distribution system. Specifically, the distribution system's radial topology is leveraged to reduce the decomposed systems into upstream and downstream network equivalents. The proposed innovations reduce the required number of macro-iterations/communication-rounds for convergence by order of magnitude. The approach is validated using IEEE 123-bus and IEEE 8500-node test systems and is shown to converge to the same optimal solution as obtained using an equivalent centralized OPF (C-OPF) model. [ABSTRACT FROM AUTHOR]

Demand-side operations incentivize utility customers to take part in various grid services. A demand response enabled load (DREL) is a flexible grid asset that schedules electricity consumption in response to a time-of-use (TOU) energy price. Consequently, its energy profile differs from that of a conventional load that is insensitive to price. This difference may cause new challenges for distribution system state estimation (DSSE). It is well known that DSSE often needs to use pseudo-measurements based on historic load profiles to increase system observability. However, historic profiles of conventional loads are not representative of DREL behaviors. The inaccuracy impacts DSSE results and other DSSE-dependent operations. In this paper, we propose an online pseudo-measurement generation approach for DSSE with DRELs. We formulate an optimization model to represent DRELs self-adjusting actions. Sampling-based stochastic optimization techniques are proposed to account for uncertainties in DRELs. A set of representative DREL behavior data corresponding to the samples are used to characterize DREL pseudo-measurements. Case studies with modified IEEE 123-bus test system verify the validity of the proposed work. [ABSTRACT FROM AUTHOR]

In order to quantify the impact of uncertainties on power systems resulted from renewable energy, a novel interval power flow method based on multi-dimensional holomorphic embedding method (MDHEM) and tight semidefinite relaxation technique are proposed. First, the MDHEM is used to address the spatial correlation of renewable energy basis, and the analytical expressions of bus voltages and active branch flows are derived. Second, the general interval power flow (IPF) model can be cast as a box constrained polynomial optimization problem. Third, a tight semidefinite relaxation is designed to solve this special optimization problem. Simulation results on several large-scale test systems show the effectiveness and computational tractability of the proposed model. [ABSTRACT FROM AUTHOR]

The precise fluid and gas energy flow equations (FEFEs) are difficult to formulate due to the uncertain parameters. This paper proposes a data-driven approach to fit the FEFEs by polynomial functions through experimental data. Furthermore, a convex optimization model is set up to find the solution of the FEFEs, and a tight reformulation is proposed to exactly reformulate the proposed model as a second-order cone programming (SOCP) that can be tractably solved. Numerical results on several test systems show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a new distributed dispatch scheme to realize efficient coordinated real-time dispatch of the coupled transmission grid and active distribution grids (ADGs). In the proposed scheme, on the one hand, the concept of optimal power exchange interval is introduced to coordinate the transmission grid and ADGs so that the reserve capacity support from ADGs can be incorporated in the dispatch optimization of the transmission grid. On the other hand, uncertainties of renewable distributions are considered to ensure the robustness of dispatch decisions. With the analytical target cascading (ATC) method, the centralized distributionally robust dispatch model for the integrated transmission-distribution system is decoupled, leading to a number of independent small local optimization problems for the transmission grid and ADGs. Meanwhile, the diagonal quadratic approximation is adopted to develop an iterative coordination strategy where all local optimization problems are solved in a parallel manner, increasing the computational efficiency. By using the constrained cost variable technique (CCV) and a new affine policy, the original non-convex dispatch model is reformulated as a linear optimization problem, which ensures the convergence of the iterative process and further reduces the computational burden. Case studies on three test systems verify the effectiveness and efficiency of the proposed scheme. [ABSTRACT FROM AUTHOR]

With the increasing integration of distribution generations (DGs), active distribution networks (ADNs) need to address the new challenges in voltage control. Meanwhile, the traditional centralized method in reactive power optimization usually leads to a heavy computational burden for large-scale distribution networks. A distributed voltage control model with a novel network partitioning method is proposed in this study, where the capacity curve of DGs and the regulation of electrical storage systems (ESS) are specially addressed. According to the number of decision variables and the number of constraints in the optimization model, the computational complexity can be utilized in the network partitioning method with the power balance and the electrical distance to automatically partition a distribution network in an optimal manner. Taking the coordination between two boundary sub-areas into consideration, a distributed voltage control strategy is developed and solved by a distributed solution method based on Lagrangian dual relaxation. Finally, numerical studies based on two IEEE test systems and a practical 152-bus system are performed to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The real-time preventive control is particularly critical because the integration of renewable energy aggravates the random fluctuation of large power grid operating state. An online preventive control optimization method for static voltage stability is proposed based on the Thevenin equivalent parameter identification. According to the definition of the stability margin, its quantitative relationship with the control variable is derived to meet the real-time and reliability requirements of online applications. The preventive control optimization model is constructed to minimize control cost and consider voltage stability margin and operation constraints under multiple critical contingencies. The generator terminal voltage and shunt capacitor in this paper are taken as continuous and discrete control variables respectively. The derived analytical relationship between stability margin and control variable is linearized to form linear security constraints, and the optimization model is transformed into a mixed-integer linear programming problem, which can be solved by CPLEX quickly and reliably. The accuracy and rapidity of the proposed method are verified on several test systems. [ABSTRACT FROM AUTHOR]

This paper presents a measurement-based method to determine distributed energy resource (DER) active- and reactive-power setpoints that minimize bus voltage deviations from prescribed reference values, bus active- and reactive-power deviations from desired setpoints, as well as cost of DER outputs. Central to the proposed method is the estimation of a linear sensitivity model from synchronized voltage and power-injection data collected from distribution-level phasor measurement units installed at only a subset of buses in the distribution system. As new measurements become available, the linear sensitivity model is updated via the recursive weighted partial least-squares estimation method. The estimated sensitivity model is then embedded as an equality constraint in a convex quadratic optimization problem, which can be solved via, e.g., the alternating direction method of multipliers. Numerical simulations involving the IEEE 33-bus distribution test system illustrate key benefits of the proposed method, including (i) eliminating the need for an accurate offline system model, (ii) adapting to online network-topology and operating-point changes, and (iii) being robust against delays potentially attributed to communication, computation, and actuation. Additional numerical simulations involving larger test systems demonstrate computational scalability. [ABSTRACT FROM AUTHOR]

This letter presents a multi-objective probabilistic model for the optimal selection of pilot buses in secondary voltage regulation (SVR) of transmission grids. The pilot-bus selection (PS) problem is formulated in the form of a stochastic multi-objective optimization problem (SMOP). The resultant SMOP is solved toward the non-dominated sorting genetic algorithm (NSGA-II) together with a fuzzy set theory. The applicability of the proposed PS model is examined on a classical test system as well as a practical transmission grid. [ABSTRACT FROM AUTHOR]

With the advent of Smart Grids and advanced communication technologies, the self-healing scheme has become a desirable function of the operation and planning of electrical distribution systems (EDSs). In the presence of a permanent fault, an optimized self-healing scheme minimizes the unsupplied demand while maintaining the faulted section of the network isolated. The service restoration of the self-healing scheme is a combinatorial optimization problem whose computational complexity grows exponentially with the number of binary variables. To resolve this issue, a distributed optimal service restoration strategy is developed based on the alternating direction method of multipliers (ADMM). The service restoration problem is formulated as a mixed-integer second-order cone programming (MISOCP) problem. The decision variables of the problem are the status of the remote-controlled switches, load zones and load shedding at each controllable demand. Operational constraints, such as current and voltage magnitude constraints, distributed generation (DG) capacity constraints and radial topology constraints, are respected in the optimization problem. Through the ADMM, the optimization problem is distributed among the zones of the EDS, without requiring a central controller. Two test systems, an unbalanced 44-node system and the IEEE 123-node system, were used to conduct case studies. Results show that the proposed method can provide optimal service restoration solutions in reasonable time without a central controller. [ABSTRACT FROM AUTHOR]

The identification of a tie-line power transmission region is crucial for the secure operation of interconnected power grids. The characterization of a tie-line power transmission region with regional operational constraints is inherently a multi-parametric programming problem, which greatly suffers from a heavy computational burden. In this paper, we propose a fast boundary search approach to efficiently characterize the tie-line power transmission region in AC/DC hybrid grids. The multi-parametric programming method is improved in the following three aspects: (1) the search direction is fixed along the boundary of a region, which avoids the search inside a region; (2) the abundant search of subregions that share a boundary facet is avoided; and (3) a fast guess of the Karush-Kuhn-Tucker (KKT) condition is presented, which reduces the number of optimization calculations. Furthermore, an expansion strategy is presented to enlarge the tie-line power transmission region via quantifying the impact of system parameters on the transmission region. Simulations based on the IEEE 118-bus test system and a 661-bus utility system show that (1) the proposed method can be over 30 times faster than existing methods and (2) the proposed method successfully expands the tie-line power transmission region by identifying the key parameters. [ABSTRACT FROM AUTHOR]

Over the last years, extreme weather events have caused extensive damages in power systems, leaving millions of customers without electricity and therefore highlighting the necessity to enhance power system resilience. This paper proposes a resilience constrained day-ahead unit commitment framework for increasing resiliency of a power system exposed to an extreme weather event. The weather-dependent failure probabilities of the transmission lines are taken into account in order to decide the scheduling of generators that minimizes load shedding in the most efficient way, while respecting operating limits of the system. The problem is formulated as a tri-level optimization problem that is transformed to a bi-level problem using duality theory and linearization techniques. The problem is solved as a two-stage robust optimization problem using a Column & Constraint Generation based decomposition algorithm. The master problem provides the unit commitment and the subproblem identifies the worst damage scenario due to weather event. A Sequential Monte Carlo simulation of a modified IEEE Reliability Test System and IEEE 118-bus System is applied to illustrate and validate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

This paper proposes optimal operation scheduling of a Microgrid (MG) and battery swapping stations (BSSs) as two independent stakeholders with inherently conflicting objectives. In this regard, a bi-level scheduling framework for optimal decision making of MG and BSSs is presented. Moreover, battery degradation cost is explicitly modeled based on the depth of discharge and the cycle life's intrinsic behavior of batteries. In order to tackle both historical data-based and human-related uncertainties under incomplete information including load demand of MG, photovoltaic generation, wholesale market prices, and swapping requests, a hybrid probabilistic-possibilistic approach considering correlation among uncertainties has been developed. To solve the proposed MG-BSS optimization problem, alternative direction method of multipliers (ADMM) with restart algorithm in a fully decentralized fashion is implemented. The effectiveness of the proposed model is demonstrated on a real-test MG system under different scenarios. Moreover, to compare the computational complexity of the proposed algorithm with the standard ADMM and investigate the scalability of the algorithm, extensive simulations are carried out on different standard test system. [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]

This paper introduces an empirical approach to dispatch resources in real-time power system operation with growing levels of uncertainties emerging from intermittent and distributed energy resources in the supply and the demand side. It is shown that by taking empirical data of specific sizes, the dispatch results can lead to a quantifiable and rigorous bound on the risk of violating constraints at the implementation stage. In particular, we formulate the look-ahead real-time economic dispatch problem using the scenario approach. This approach takes empirical data as input and guarantees a tunable probability of violating the constraints according to the input data size. By exploiting the structure of the economic dispatch, we show that in the absence of transmission constraints, the number of samples that is required by the theory does not grow with the size of the problem. In the more general case with transmission constraints, it is shown that the posterior bound on the risk of dispatch can be quantified and can be much smaller than the risk bound before solving the dispatch. Numerical examples based on a standard test system suggest that the scenario approach can provide a practically attractive solution with theoretically rigorous properties for risk-limiting power system operations. [ABSTRACT FROM AUTHOR]

This paper proposes a new decentralized restoration scheme in order to realize efficient coordinated restoration of the coupled transmission and distribution (TS-DS) system. First, considering the separated operation of the TS operator (TSO) and DS operators (DSOs) and to avoid the TSO data processing burden, the TS-DS system is divided into several subsystems according to their physical connection. Then, in order to implement the independent but coordinated decision-making of the subsystems, a decentralized decision-making framework is constructed by model decoupling and iterative interaction between TS and DS. Furthermore, for the sake of the convergence of the iterative process with non-convex models, a new decentralized optimization method is developed to obtain the optimal coordinated restoration strategy of the TS-DS system. The proposed decentralized restoration scheme achieves independent decision-making of the TSO and DSOs in the coupled TS-DS system, and solves the non-convergence problem of the decentralized optimization method. The effectiveness of the proposed method is validated using two IEEE standard test systems and a real-world power system. [ABSTRACT FROM AUTHOR]

This paper addresses the multistage or dynamic investment decision-making problem arising in a distribution system within a transactive energy environment. Under such a framework, three agents are involved, namely the distribution company (DISCO), the owner of distributed generation (DG) units (DGENCO), and the independent distribution system operator (IDSO). Here, we propose characterizing this planning problem as an instance of trilevel programming. In the upper level, the DISCO identifies the optimal investment plan in network assets and the best potential locations for DG in order to supply the customer demand at maximum profit. In the middle level, the DGENCO determines the best location, sizing, and timing for DG installation so that the corresponding profit is maximized. Finally, in the lower level, the IDSO is responsible for the optimal operation of the expanded distribution system. The resulting mixed-integer trilevel program is solved by a novel approach relying on Benders decomposition. The performance of the proposed approach has been illustrated with a case study based on a 37-node test system. [ABSTRACT FROM AUTHOR]

Operating reserves serve as efficient tools to respond to the sudden outage of power system components caused by either natural events or intentional attacks. Contrary to the natural threats, the intentional attacks exhibit no stochastic behavior and are launched to inflict as much damage as possible to system. Moreover, these attacks are strongly influenced by the measures taken by the system operator. As a consequence, the probabilistic methods of operating reserve procurement and allocation do not prove to be efficient to confront the intelligent attacks. In this paper, the interaction between the system operator as the defender and the intelligent attacker is investigated within a linear two-person zero-sum game model. In this model, the system is scheduled and the operating reserves are allocated and deployed in a limited number of time intervals in such a way that the system incurs the least cost for the worst-case attack. The efficacy of the proposed method is examined by conducting case studies of single- and multiple-target attacks on two test systems. The results indicate that the solutions obtained by the proposed method can result in a considerable reduction in the system costs in comparison to those obtained when the system is scheduled by probabilistic methods and an intelligent attack is launched. [ABSTRACT FROM AUTHOR]

The decentralized unit commitment problem (DUCP) solved by autonomous distributed agents has been studied by many researchers. Several methods based on traditional distributed optimization approaches have been proposed for solving the DUCP. However, these existing methods do not determine the unit commitment (UC) decisions in a distributed manner or cannot theoretically guarantee convergence and optimality. This paper proposes a method for formulating and solving the DUCP using a recently published parallelizable augmented Lagrangian method, referred to as SDM-GS-ALM. SDM-GS-ALM is a parallelizable method for solving large-scale non-convex problems, and under mild conditions, its optimality and convergence have been proven. The DUCP can be modelled as a mixed-integer linear programming problem, and in this case, its global optimal solution can be obtained via SDM-GS-ALM. This paper focuses on the DUCP in integrated heat and electricity systems. The proposed model and solution method are both general and can be applied directly to solve the DUCP in power systems. The optimality and convergence of the proposed method are demonstrated in case studies. In three test systems, the gaps between the optimal objective values of the centralized UC problem and the DUCP are less than 0.001%. [ABSTRACT FROM AUTHOR]

Optimal power flow (OPF) is an important power systems optimization tool. However, due to the inherent nonlinearity of the solution space, OPF methods may yield a local optimal solution, leading to economic loss. For this reason, an OPF remains a major research topic. Changing power balance equations can change the solution space, alter solution time, and create a chance of reaching a better solution. In this paper, a new simple line-wise optimal power flow (LWOPF) formulation is proposed using line-wise power balance equations and solved using the successive linear programming (SLP) technique. The study results on nine different test systems, up to a 9 241-bus real power system, show that the proposed method is accurate, provides monotonic convergence, scales well for large systems and is consistently faster, up to twice the speed of MATPOWER and other proven bus-wise SLP approaches to solve the OPF. [ABSTRACT FROM AUTHOR]

The paper introduces a new flexible alternating current transmission systems (FACTS) controller based on pulse width modulated series compensator (PWMSC) for damping inter-area oscillations in power systems. The PWMSC can modulate the impedance of a transmission line through the variation of the duty cycle of AC link. The main purpose of this work is to verify the capability of the PWMSC to mitigate low frequency oscillations. For this reason, a novel current injection model of PWMSC is proposed and incorporated in the system model. This study proposes a novel tuning strategy for robust PWMSC damping controller based on the augmented Lagrangian particle swarm optimization (ALPSO) satisfying the multiple H\infty performance criteria. Proposed method does not need any conservative assumption unlike the conventional techniques, and often enables us to find the desired parameters of controller just by solving the constrained optimization problem in a straightforward way. In the paper, a verification of the designed controller by means of digital simulations of single machine and four-machine power systems is presented. The net results show that the proposed controller improves the stability of both test systems significantly in a number of fault cases at different levels of power flow in tie-line. [ABSTRACT FROM PUBLISHER]

This work proposes a novel heuristic-hybrid optimization method designed to solve the nonconvex economic dispatch problem in power systems. Due to the fast computational capabilities of the proposed algorithm, it is envisioned that it becomes an operations tool for both the generation companies and the TSO/ISO. The methodology proposed improves the overall search capability of two powerful heuristic optimization algorithms: a special class of ant colony optimization called API and a real coded genetic algorithm (RCGA). The proposed algorithm, entitled GAAPI, is a relatively simple but robust algorithm, which combines the downhill behavior of API (a key characteristic of optimization algorithms) and a good spreading in the solution space of the GA search strategy (a guarantee to avoid being trapped in local optima). The feasibility of the proposed method is first tested on a number of well-known complex test functions, as well as on four different power test systems having different sizes and complexities. The results are analyzed in terms of both quality of the solution and the computational efficiency; it is shown that the proposed GAAPI algorithm is capable of obtaining highly robust, quality solutions in a reasonable computational time, compared to a number of similar algorithms proposed in the literature. [ABSTRACT FROM PUBLISHER]

We address the generation investment problem faced by a strategic power producer and consider a detailed description of the uncertain parameters involved, namely, rival producer investment and market offering, and demand growth. To identify optimal investment decisions, we consider a target year and propose a bilevel model whose upper-level problem determines investment and offering decisions to maximize expected profit, and whose many lower-level problems represent market clearing conditions per demand block and scenario. Since the producer total expected profit is sufficiently convex with respect to investment decisions, a Benders' decomposition approach is proposed that results in a tractable formulation even if hundred of scenarios are used to describe uncertain parameters. Extensive numerical simulations based on realistic case studies show the good performance of the proposed decomposition approach. [ABSTRACT FROM PUBLISHER]