Power system planning for maximizing intermittent energy sources using AC model

A linearized formulation of AC multi-year transmission expansion planning: A mixed-integer linear programming approach

Guest Editorial: Situational awareness of integrated energy systems

Transmission expansion planning with AC model based on generalized Benders decomposition

This paper presents a neural network approach intended to aid electricity consumers or power system planner in the task of classification of harmonic load types using the measurements (data samples) collected from one of the power station in Malaysia. In order to allow the classification of the type of harmonic loads, harmonic currents order produced by aggregate harmonic loads and the level of emission are modelled using the Multi-Layer Extreme Learning Machine with autoencoder (hereinafter denoted as ML-ELM-AE). The feasibility of ML-ELM-AE on the classification of the Harmonic empirical data set will be probed, for when the classification results of the Harmonic load types become available, it can come in handy to power system analyst or engineers for analysis in later stage. They can use it to determine the harmonic distortion patterns and to characterize the harmonic currents at network buses. Depending on whether the aggregate load is residential, commercial, or industrial, the load characteristics in terms of its harmonic contents are likely to be different. The achieved results demonstrate the effectiveness of the investigated technique in dealing with the real world power system application of harmonic load type classification, that can be useful in providing good indication to the power system or distribution network planner.

With increasing heating demand and installed capacity of wind farms, combined heat and power (CHP) systems with inflexible generation are confronted with a major challenge for the efficient utilization of the wind power. Distributed heat sources (DHSs), such as electric boilers and heat storage, offer an effective way to enhance wind power penetration in a combined heat and power system by reducing the electric-heat coupling of the CHP unit. In this paper, an assessment approach for the wind power penetration of a CHP system with DHSs is proposed, and a two-stage multiple types of DHSs optimal configuration strategy to realize the minimum construction cost at a certain permissible wind power curtailment rate is established. After that, the proposed strategy is applied to a case study of a practical CHP system with in-depth analysis and detailed discussion, which reveals the effectiveness of the proposed strategy and indicates its promising applications for DHS configuration.

Voltage deviation and stability constrained VAr planning or reactive power planning (RPP) is an important challenging issue in power systems. This paper presents a new hybrid technique for modeling and solving RPP problem taking into account the static voltage stability constraint. First, the uncertain fuzzy clustering theory is employed to select new candidate VAr source locations. Then, modified gray code is applied and used to represent a series of non-uniform VAr capacity intervals at different candidate buses. Based on the new ordering of the VAr capacity intervals, a simplified piecewise linear function between the total transfer capability and new VAr capacity is derived and applied as static voltage stability constraint in RPP problem. Last, the RPP optimization problem is solved by a self adaptive fuzzy chaotic interactive honey bee mating optimization (FCIHBMO) technique taking advantage of the modified gray code. In the FCIHBMO algorithm, a modified definition of the updating factors on generation solution is proposed. In the case study, uncertain fuzzy clustering mechanism, the modified gray code, and the modified HBMO are applied to the IEEE 118-bus and IEEE 300-bus systems. Test results conclude that the proposed hybrid technique is a simplified and effective approach for voltage stability constrained VAr planning with contingency considered.

Transmission expansion planning (TEP) is an important and computationally expensive decision‐making process in power systems. Expression of the power flows using power transfer distribution factors (PTDFs) could potentially reduce the size and computational cost of the TEP. However, this formulation does not lend itself easily for this problem as the system topology changes in the course of the optimisation procedure. This study presents a PTDF‐based TEP model devoid of voltage angles and nodal power balances, while producing the same solution as angle‐based TEP models. The proposed model is capable of monitoring a limited number of transmission lines that have significant impact on the system's reliable and optimal operation. It thus considerably reduces the number of constraints and results in a scalable optimisation problem. The proposed model is highly advantageous especially for the TEP of large‐scale power systems. The numerical results provide an indication for the effectiveness of the proposed model.

The usage of wind power is much more popular nowadays although the intermittent nature of wind makes the wind power generation unreliable and stochastic to a large extent. With the uncertainty of wind, the power system planners are faced with a big challenge to design a sound model of power systems. So, reliability estimation of a wind farm has become a great challenge for the researcher. In this article, Markov Reward method has been proposed for developing a realistic model of a wind farm in respect to the wind farm relative availability function. According to the proposed model, the performance of a wind farm has been assessed in terms of different reliability indices. The novelty of this model is that it can be capable of counting any level of power generation made by the wind farm irrespective of failure and success condition reported in the previous literature. A comparison of this study reveals the efficacy of the proposed model over the existing General Markov Reward method. From a case study, the practical validation of this model has been carried out.

Voltage stability constrained reactive power planning (RPP) or VAr planning is a very challenging issue in power systems. This paper proposes a new approach for modeling and solving VAr planning problem taking into account the static voltage stability constraint. First, the fuzzy clustering method is employed to select new candidate VAr source locations. Then, modified Gray code is proposed and used to represent a series of non-uniform VAr capacity intervals at different candidate buses. Under the new ordering of the VAr capacity intervals, a simplified piecewise linear function between the total transfer capability (TTC) and new VAr capacity is derived and applied as static voltage stability constraint in RPP. Finally, the RPP optimization model is solved by an enhanced simulated annealing (SA) algorithm taking advantage of the modified Gray code. In the SA algorithm, a modified definition of the neighborhood selection and a novel approach to generate new random solutions are proposed. In the case study, fuzzy clustering method, the modified Gray code, and the improved SA are applied to the IEEE 30-bus system. Test results conclude that the proposed method is a simplified and effective approach for voltage stability constrained VAr planning with contingency considered.

To encourage and support innovation, synthetic electric grids are fictional, designed systems that mimic the complexity of actual electric grids but contain no confidential information. Synthetic grid design is driven by the requirement to match wide variety of metrics derived from statistics of actual grids. In order to scale these systems to 10,000 buses or more, robust reactive power planning is needed, accounting for power flow convergence issues. This paper addresses reactive power planning and power flow convergence in the context of large synthetic power grids. The iterative algorithm presented by this paper supplements a synthetic transmission network that has been validated by a dc power flow with a realistic set of voltage control devices to meet a specified voltage profile, even with the constraints of difficult power flow convergence for large systems. The algorithm is illustrated with an example new synthetic 10,000 bus system, geographically situated in the western United States, which is publicly available and useful for a variety of research studies. An analysis is shown validating the synthetic system with actual grid characteristics.

05/01591 Novel approach considering load-relative factorsin short-term load forecasting

In this paper, the short term transmission network expansion planning (STTNEP) is solved through a specialized genetic algorithm (SGA). A complete AC model of the transmission network is used, which permits the formulation of an integrated power system transmission network expansion planning problem (real and reactive power planning). The characteristics of the proposed SGA to solve the STTNEP problem are detailed and an interior point method is employed to solve nonlinear programming problems during the solution steps of the SGA. Results of tests carried out with two electrical energy systems show the capabilities of the SGA and also the viability of using the AC model to solve the STTNEP problem.

This paper studies transmission expansion planning (TEP) problem considering effects of wind farms on transmission network congestion, power system reliability, line repair, and operational costs of wind and generating units in a deregulated environment. Transmission congestion is modeled using difference between local marginal prices (LMPs) of start and end buses of each line that is multiplied by its power flow. In this way, wind units can efficiently affect network congestion through decrease in lines loading and LMPs of buses. Transmission system reliability is modeled using load shedding (LS) index as well as outage rate and outage duration of a single line or simultaneous outage of a line and a wind unit. Also, generation reliability is formulated using loss of load, value of lost load, and LS criteria due to outage rates and outage durations of generating and wind units considering line limits. In addition, the effect of wind units on repair cost of transmission lines is modeled by the relationship among wind generation, line failure rates and lines loading. Operational cost includes cost of network losses, pollution penalty, fuel expenses, and generation cost of wind units. In order to more efficiently investigate the effect of wind power on TEP problem, the proposed model was tested on the IEEE Reliability Test System (IEEE RTS) for different scenarios, and the results were discussed.

A high increase in the installed capacities of Renewable Energy Sources (RES) is expected by 2020 and beyond in Europe, and power systems planners and regulators have to make sure that the power systems are designed and operated to make optimal use of the potential of these clean energy sources. This paper investigates the consequences of integrating large-scale solar and wind power in the future Northwestern European power system. A zonal market model is used, where unit commitment and economic dispatch simulations for a given development scenario of load and generation, including wind power and solar power time series and inter-zonal transmission constraints, are with hourly resolution. The ability of the power system to absorb the available “green” energy is assessed. Moreover, factors that can hamper or improve the RES integration are identified by performing a sensitivity analysis on a subset of the scenario attributes.

This study presents a distributed collaborative transmission expansion planning (TEP) algorithm for interconnected multi‐regional power systems. The proposed algorithm is a multi‐agent‐based TEP. A local TEP is formulated for each region (agent) with respect to the region's local characteristic and interactions (i.e. tie‐line flows) with its neighbours. Nodal power balances at border buses are modified to model the interactions. Realistic planning constraints and objectives such as budget constraints, operational costs, N − 1 security criterion, and uncertainties are modelled in the local TEPs. The information privacy is respected as each local planner needs to share limited information related to cross‐border tie lines with other planners. To coordinate the local planners, a two‐level distributed optimisation algorithm is proposed based on the concept of analytical target cascading (ATC) for multidisciplinary design optimisation. While the upper level solves the local TEPs in parallel, the lower level seeks to coordinate neighbouring regions. The lower‐level problem is further replaced in the upper‐level optimisation by Karush–Kuhn–Tucker conditions to relax the need for any form of central coordinator. This makes the proposed ATC‐based TEP a fully parallelised distributed optimisation algorithm. An initialisation strategy is suggested to enhance the performance of the distributed TEP.