Classification of the harmonic load types using multi-layer extreme learning machine

This paper focuses on the development of a fast aggregate electricity load model under steady-state and dynamic conditions. An important requirement of such a load model is that it should be robust, analytically sound, and conceptually clear. Besides, the model can be easily incorporated into existing programs of power system analysis. By using quasi-linear approximation made on the dynamic components of power demands, the general model is described in the paper by a combination of a linear system and a non-linear function. From the general model, the step response of the load is derived. The model is then reduced into a discrete one based on the step response. Successive applications of such a discrete model provide a good approximation of load dynamics. To evaluate parameters of the proposed discrete load model, an identification algorithm is developed to read data from a sliding sample window of bus voltages and load responses. By comparing the identification results with the detailed simulation of a test system, the proposed fast model is shown to provide good representation of the consumer load response under static and dynamic conditions. Furthermore, aggregate load characteristics have been taken into account in the voltage control of a study power system to demonstrate the importance of aggregate load modelling with respect to suppressing transients, reducing control errors and preventing reverse actions in voltage regulation.

A long-term capacity expansion planning model for an electric power system integrating large-size renewable energy technologies

Redundancy and hardening of components in transmission systems are practices to improve system resilience against possible adverse consequences caused by natural hazards. However, power systems planners do not usually recognize the natural hazards, so-called high-impact low-probability (HILP) events, within network investment decisions. The situation is exacerbated by an increase in the frequency and severity of power system blackouts due to HILP-type events. In this context, this paper proposes a planning problem for investment in transmission lines and hardening of substations to improve the resilience of power system considering the outage scenarios originated by earthquakes. The problem is formulated as a mixed-integer linear programming (MILP) approach. Furthermore, a probabilistic methodology is used to simulate the hazard and its impacts on the power system. Our proposal is applied on the Ecuadorian transmission system. The results demonstrate that hardening substations and investment in lines can significantly contribute towards enhancing resilience to high impact exogenous natural hazards.

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.

Recently, power system planners and operators are of interest to maximize distribution automation investments. Optimal operation of control centers of fully automated distribution systems enables it to control all switches as well as dispatching connected controllable apparatuses. Most previously enhanced distribution systems, which include additional enhancement apparatuses, such as distribution static VAr compensators, capacitor banks, and distributed generators, cannot take advantage of the features of these apparatuses because there is no efficient and reliable control. Accordingly, this article presents a robust and efficient technique using Manta ray foraging optimization algorithm (MRFOA) for optimal coordination and control of such apparatuses as well as distribution network reconfiguration simultaneously. The proposed procedure is employed for the dynamic operation of automated distribution systems, considering daily load variations, for losses minimization and emissions reduction with diverse cases of both the 33-bus and 69-bus distribution test systems. To manifest, these cases are developed to validate the proposed technique and to achieve optimum control and operation of the distribution systems. Despite the complexity of this problem, the proposed technique can achieve the optimal solution for the various considered cases. As well, the comparative assessment of the proposed MRFOA with relevant techniques demonstrates its effectiveness as an effective tool used in the control centers of modern automated distribution systems. One of the most complex challenges to distribution systems operators in the practical daily load variations is the control and optimal operation of the associated dispatchable enhancement apparatuses such as DGs and DSVCs. The DNR makes the utilization and control of such aforementioned enhancement apparatuses more feasible for distribution systems, but this is in return for increasing the complexity of the problem. Significant technical and environmental benefits have been achieved. Great power losses reduction (about 80%) and voltage enhancement are achieved using the MRFOA. In addition, the emission levels are kept at the lowest levels.

As installed capacity of wind power has been increasing in the last decade, power system planners are exposed to the several challenges, with regard to the drawbacks for wind power usage because of the existing infrastructure. The transmission expansion planning (TEP) and reactive power planning (RPP) should be specified by considering the maximization of wind power usage as well as meeting load growth. In this study, a new methodology for TEP and RPP is proposed to minimize wind energy spilled due to the power system constraints such as voltage and transmission line limitations. For this reason, the power flow equations of AC model should be used. Because of the intractable nature of large scale optimization problems, the backward search approach is utilized. AC optimal power flow based economic dispatch is solved to determine optimal dispatches. The proposed methodology is illustrated on the IEEE-24 bus reliability test system to demonstrate the results.

Probability methods are being used extensively in the area of static adequacy assessment; however, their application to power system security evaluation has not received the same degree of attention. A probabilistic approach is presented in this paper to evaluate the health of composite generation and transmission systems. The functionality of a power system is identified as being healthy, marginal or at risk. These operating states reflect the degree to which both steady state and transient state constraints are satisfied. The identified system operating states can provide power system engineers and planners with additional information that can be used in the evaluation of the required reinforcements or modification to meet the required degree of reliability. The proposed method is illustrated by assessment of composite system health and risk in a hypothetical test system.

This paper proposes a novel approach for Optimal Power Flow (OPF) problems in power systems by integrating the optimal placement of Flexible Alternating Current Transmission System (FACTS) devices with a hybrid renewable energy system comprising wind and photovoltaic (PV). The objective is to optimize the placement of FACTS devices to simultaneously minimize power loss, voltage deviation and reduce gross cost. To achieve this goal, the Enhanced Multi – Strategies Sparrow Search Algorithm (EMSSA) is employed, which combines multiple optimization strategies inspired by the collaborative foraging behavior of sparrows. By considering multiple objectives, including power loss reduction, voltage deviation reduction, and gross cost minimization, the proposed methodoloty aims to identify optimal locations for installing FACTS devices such as SVC (Static Var Compensator), TCSC (Thyristor Controlled Seris Compensator), TCPS (Thyristor Controlled Phase Shifter). The results showcase the capability of the proposed methodology to achieve significant improvements in power system performance, thereby contributing to the efficient and sustainable operation of modern power grids. This research also provides valuable insights for power system operators and planners to make decisions regarding the deployment of FACTS devices and the integration of renewable energy sources.

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.

During recent years, power system planners are becoming interested to integration of decentralized generation in radial distribution system (RDS) to withstand the electric grid issues. Integration of decentralized generation (DG) at non-optimal locations and sizes make the power system issues to be achieved such as increased system power loss and reduced bus system voltage. In this paper, the optimal locations and sizes of renewable DG in RDS are determined using chaotic moth-flame optimization (CMFO) algorithm and real power loss sensitivity factor (PLSF) to achieve the maximum reduction in active system power loss. Integration of renewable distributed generation such as solar (PV) and wind (WTG) in RDS have received great attention in many countries over the world due to its effect in minimizing the distribution feeders’ losses and the greenhouse gas emission. The proposed method is applied to one, two and three decentralized generations installation in IEEE thirty three bus RDS. The obtained results proved that, the active system power loss reduction of single, two and three integration of DG are nearly 47.4 %, 58.7 % and 65.5 % for solar (PV) and 67.8 %, 86.5 % and 94.4 % for wind (WTG). From comparative study, the simulation results illustrate the high performance of the proposed technique.

In the western United States, 27% of electricity demand is met by hydropower, so power system planners have a key interest in predicting hydropower availability under changing climate conditions. Large-scale projections of hydropower generation are often simplified based on regression relationships with runoff and they are not always ready to inform power system models due to the coarse time scale (annual) or limited number of represented power plants. We developed an enhanced process-based hydropower model to predict future hydropower generation by addressing the commonly under-represented constraints, including (1) the ecological spills, (2) penstock constraints to provide flexibility in electricity operations, and (3) biases in hydro-meteorological simulations. We evaluated the model over the western United States under two emission scenarios (RCP4.5 and RCP8.5) and ten downscaled global circulation models. At the annual time scale, potential hydropower generation is not projected to change substantially, except in California. At the seasonal time scale, systematic shifting of the generation patterns can be observed in snowmelt-dominated regions. These projected annual and regional trends are comparable to other regression-based relationships. However, the representation of more complex operations and constraints tend to reduce the uncertainties inherent to climate projections at seasonal scale. In the Pacific Northwest region where hydropower is the dominant electricity source, our predicted future change of hydropower generation is about 10% less than the regression-based projections in spring and summer. The model can also capture the seasonal non-stationary in hydrologic changes. The spatio and temporal scales of the model, increased accuracy and quantification of uncertainty allow one to use the products to inform power system models toward supporting energy sector planning activities and water-energy trade-offs.

A new index for generation capacity reliability studies—expected cost penalty

The short-term interval load forecasting accuracy of the power system has an important impact on the power system operation. A short-term load forecasting model based on ALO-ML-ELM is proposed for short-term load-interval forecasting without considering the load trend and inaccurate interval construction. Firstly, the weather, temperature, and historical load data are processed and normalized by taking the load trend influencing factors into account. Then the load power interval is constructed by fitting a normal distribution to the historical load error data, obtaining the relevant parameters, and generating random noise. Next, the short-term load-interval prediction model is established by optimizing the input layer weights of the multi-layer extreme learning machine (ML-ELM) by Ant Colony Optimization (ALO). Finally, it is validated by the actual load data of a city in China. The simulation results show that the proposed method has higher interval coverage and accuracy.

Responsive loads can act as spinning reserve which is incorporated with the conventional AGC units for the improvement of frequency reliability. In this paper, a distributed consensus‐based optimal strategy of responsive load aggregators and AGC units is presented to provide secondary frequency control in the power system. Specifically, two‐step optimal model is proposed. In the first step, Lambda‐Iteration method is used to yield the total active power command for load group and AGC group. In the second step, the distributed optimal algorithm withθ‐logarithmicbarrier cost function is proposed to allocate the active power command to multiple load aggregators and AGC units while guaranteeing consensus among individual incremental costs. In achievement of such a consensus, two virtual pinners are utilized to generate the regulation signal only shared with a part of load aggregators and AGC units, respectively. Moreover, the communication topology among load aggregators is a strongly connected digraph, the same as to the AGC units. Finally, simulation results show that the system frequency can be greatly improved, while the total cost of the whole system can be minimized with both equality and inequality constraints.

Voltage Stability Enhancement on IEEE 14 Bus System