• Energy Research

Abstract To detect and alleviate harmonic resonance in a power system have been a delicate issue. In this paper, the effect of load modeling on resonance behavior of power systems employing eigenvalue sensitivity analysis is investigated. The most influencing parameters to mitigate resonance modes are detected using the sensitivity of critical eigenvalues with respect to the network various components in a frequency range. Also, results inferred from the criteria of driving point impedance and bus participation factors are compared with those of the sensitivity analysis with different load models. Where to locate capacitors and filters to mitigate the resonance modes is obtained using these criteria. The methods are tested on the well-known New Zealand as well as IEEE-30 bus test systems. Simulation results are discussed in detail to investigate the methods’ efficiency and capability.

Abstract This paper proposes an integrated framework to design wide area measurement system (WAMS) addressing the challenges of expansion planning and unknown transformer taps. The proposed framework is formulated as a mixed-integer linear programming (MILP) problem. It minimizes the number of substations disrupted for phasor measurement unit (PMU) placement, PMU buses, and PMU measuring channels. Concepts of substations and buses are separately treated. Power system expansion planning is also considered in order to achieve an optimal solution with the least cost of disrupting existing and future substations. Observability constraints are defined to make both present and expanded grids observable since the time span of WAMS planning is usually shorter than network expansion planning. Moreover, special attention is paid to multiple voltage levels inside a substation; transformers with unknown tap settings are exempt from being treated as observability paths and locating vicious measurement channels. Furthermore, novel ideas are proposed to model branch outages using the concept of backup observability and to model PMU failure using a hybrid method which is a modified version of previous ones. Numerical results from testing the proposed framework on standard test systems verify its performance from perspectives of solution optimality and computational burden.

Abstract Increasing demand for electrical power and expensive expansion of power systems to meet this demand leads the planners to find techno-economical solutions to overcome these challenges. Using series capacitors as reactive power compensation devices in a long transmission lines is a well-known and suitable way to face these challenges in power systems. Transmitted electrical power and the stability margin of power systems are economically improved by the proposed series devices. However, employing the series capacitors in long transmission lines causes a well-known problem in power systems as Sub Synchronous Resonance (SSR). In the present work, the mitigation of SSR is formulated as a Multi Objective Problem (MOP) in a fuzzy framework by using optimal coordinated control of Superconducting Fault Current Limiter (SFCL) and Superconducting Magnetic Energy Storage (SMES). Objective functions of MOP includes the minimization of the initial energy stored in the SMES unit, the energy loss of SFCL, the kinetic energy in generator rotor, the active power deviations in faulted bus, and the rotor speed deviations. These five objective functions are scaled by a fuzzy operator, then the scaled objective functions are aggregated by the “max-geometric mean” operator to generate the multi objective function. To solve the proposed multi-objective problem, the Hybrid Big Bang-Big Crunch (HBB-BC) as a meta-heuristic optimization algorithm is used. In order to evaluate the efficiency of the proposed algorithm, it is implemented on the IEEE First Benchmark Model (FBM) for SSR studies. According to the simulation results of the present study, the proposed algorithm is more effective in reducing the SSR problem in comparison with other algorithms.

PES Open Access Paper

Distributed generations (DGs) are recently in growing attention as a solution to environmental and economical challenges caused by conventional power plants. In this study, a multi‐objective framework as a nonlinear programming (NLP) is proposed for optimal placement and sizing of DG units. Objective functions include minimising the number of DGs and power losses as well as maximising voltage stability margin formulated as a function of decision variables. The objective functions are combined into one objective function. To avoid problems with choosing appropriate weighting factors, fuzzification is applied to objective functions to bring them into the same scale. DG units are placed at more efficient buses rather than end buses of radial links as usually determined by previous methods for improving voltage stability. Also, power system constraints including branch and voltage limits are observed in the problem. The proposed method not only is able to model all types of DG technologies but also it employs adaptive reactive limits for DGs rather than fixed limits. In addition, a three‐stage procedure is proposed to gradually solve the multi‐objective problem in order to prevent infeasible solutions. Also, a new technique is proposed to formulate the number of DGs without converting the NLP problem into mixed‐integer NLP. Results of testing the proposed method show its efficiency.

Abstract Special Protection Systems (SPSs) are one of efficient tools to limit the consequences of instability in case of happening large disturbances in power systems. Generator Rejection Systems (GRSs) are the mostly used forms of SPS. Offline GRS suffers from drawback of inaccuracy and increased risk when power system operating point differs from the nominal value. By predicting the future trend of bus phase angles, online GRS offers a lower risk than the offline scheme, especially when its parameters are appropriately tuned. In this paper, the effect of online GRS parameters including the starting time and width of the data window used for prediction on its performance is thoroughly investigated. The risk analysis of online GRS is evaluated under different conditions and compared with that of offline and no GRS schemes. To enhance the efficiency and accuracy of online GRS, an optimal data window is proposed. As an intriguing result, using a wider data window does not always result in a better accuracy of online GRS and the starting point of the data window has a vital effect on the performance of online GRS. The GRS schemes are tested on the WSCC test system and results are discussed in detail.

In this paper, a multi-objective framework is proposed for simultaneous optimal network reconfiguration and DG (distributed generation) power allocation. The proposed method encompasses objective functions of power losses, voltage stability, DG cost, and greenhouse gas emissions and it is optimized subject to power system operational and technical constraints. In order to solve the optimization problem, the HBB-BC (Hybrid Big Bang-Big Crunch) algorithm as one of the most recent heuristic tools is modified and employed here by introducing a mutation operator to enhance its exploration capability. To resolve the scaling problem of differently-scaled objective functions, a fuzzy membership is used to bring them into a same scale and then, the fuzzy fitness of the final objective function is utilized to measure the satisfaction level of the obtained solution. The proposed method is tested on balanced and unbalanced test systems and its results are comprehensively compared with previous methods considering different scenarios. According to results, the proposed method not only offers an enhanced exploration capability but also has a better converge rate compared with previous methods. In addition, the simultaneous network reconfiguration and DG power allocation leads to a more optimal result than separately doing tasks of reconfiguration and DG power allocation.

The uncertainty of wind energy makes wind power producers (WPPs) incur profit/loss due to balancing costs in electricity markets, a phenomenon that restricts their participation in markets. This paper proposes a stochastic bidding strategy based on virtual power plants (VPPs) to increase the profit of WPPs in short-term electricity markets in coordination with energy storage systems and demand response. To implement the stochastic solution strategy, the Kantorovich method is used for scenario generation and reduction. The optimization problem is formulated as a Mixed-Integer Linear Programming problem. From testing the proposed method for a Spanish WPP, it is inferred that the proposed method enhances the profit of the VPP compared to previous models.

Recent complexities in interconnected power system operation call for more efficient and fast approaches for operational problems including network‐constrained unit commitment (NCUC). In this study, an algorithm called period‐elimination Benders decomposition (PEBD) is proposed to enhance the computational efficiency of AC NCUC problems. After linearising the AC NCUC, two subproblems (SPs) are defined to ensure power balance at buses as well as constraints of branches and inter‐regional transactions. In the classical Benders decomposition (BD), all time periods are solved at all iterations up to convergence. However, in the proposed PEBD, a time period that satisfies a SP in an iteration is eliminated from the solution procedure of that SP at next iterations. After both SPs are satisfied for all time periods, the solution is checked for interrelation constraints and a few more iterations may be needed to finalise the solution. The efficiency and accuracy of the proposed PEBD method are verified by examining it on two test systems. It is found that the PEBD greatly enhances the speed of NCUC, especially in larger power systems due to eliminating unnecessary calculations for satisfied time periods.

Phasor measurement units (PMUs) have made it possible to observe and control wide-area power systems. In this paper, a new redundant observability method as a mixed-integer linear programming (MILP) is presented for optimal PMU placement. Redundant observation of buses enhances measurement reliability. The proposed method improves observability redundancy by a new objective function while using the same number of PMUs as the existing methods. Because of using MILP, the global optimal integer solution is achieved with a zero optimality gap. In addition, a systematic novel approach is proposed to incorporate already installed branch flow measurements in the PMU placement problem leading to a reduced number of PMUs required for system observability. This approach is able to handle both single and multiple flow measurements incident to a bus. PMU placement in case of PMU failure or branch outage is also studied. The proposed method along with an existing method is tested on four IEEE and Polish 3375-bus test systems. Obtained results, discussed in detail, show the efficiency of the proposed method in both speed and accuracy.