Penetration Of Renewable Power Sources Research Articles

Entirely Coupled Recurrent Neural Network-Based Backstepping Control for Global Stability of Power System Networks

In an interconnected power system network, global performance is affected by various unknown power system parameters subjected to system uncertainties. These unknown parameters in the control expression deteriorate the performance of the power system network. Therefore, these terms associated with the control expression must be estimated precisely. This paper proposes an adaptive backstepping scheme using an entirely coupled recurrent neural network (ECRNN) to estimate these control terms. Three continuous differentiable functions are used to design control input, virtual signals, and adaptive control laws to achieve global performance. The objective of ECRNN is to reduce the control complexity by estimating the associated nonlinear term in the control expression. It will facilitate the complex formulation of the controller and improve system performance. The robust functional estimation ability of ECRNN will make the system immune to uncertainties that may appear due to external disturbances. In ECRNN, feedback loops are added to each neuron layer to achieve additional estimation accuracy. The proposed adaptive law enhances the online weight update speed and accuracy. Verification of the proposed scheme is carried out in MATLAB/Simulink. It is also verified in the real-time digital simulator (RTDS) platform using the IEEE standard New England 39-bus, 10-machine power system model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the existing limitations in the global performance of a multimachine power system network due to uncertainty in the system parameters. Uncertainties in the power system network primarily appeared due to penetration of renewable power sources, load uncertainty, or occurrence of an uncertain fault in the network. As power system networks are complex nonlinear interconnected systems, these perturbations in the generator states may cause economic losses, load demand uncertainty, or electric scarcity. Thus, a control scheme is required to address the global performance of such networks. This article proposes uniform ultimate bounded stability of synchronous generators in a multimachine power system network with a robust ECRNN-based backstepping control design. The other available technique has not adequately addressed the global performance under uncertainties. The efficacy of the proposed scheme is verified in a real-time environment via a real-time digital simulator which may be a feasible solution for designing excitation control for the synchronous machine in an extensive industrial network.

Optimal Power Flow of Hybrid Wind/Solar/Thermal Energy Integrated Power Systems Considering Costs and Emissions via a Novel and Efficient Search Optimization Algorithm

The OPF problem has significant importance in a power system’s operation, planning, economic scheduling, and security. Today’s electricity grid is rapidly evolving, with increased penetration of renewable power sources (RPSs). Conventional optimal power flow (OPF) has non-linear constraints that make it a highly non-linear, non-convex optimization problem. This complex problem escalates further with the integration of renewable energy resource (RES), which are generally intermittent in nature. This study suggests a new and effective improved optimizer via a TFWO algorithm (turbulent flow of water-based optimization), namely the ITFWO algorithm, to solve non-linear and non-convex OPF problems in energy networks with integrated solar photovoltaic (PV) and wind turbine (WT) units (being environmentally friendly and clean in nature). OPF in the energy networks is an optimization problem proposed to discover the optimal settings of an energy network. The OPF modeling contains the forecasted electric energy of WT and PV by considering the voltage value at PV and WT buses as decision parameters. Forecasting the active energy of PV and WT units has been founded on the real-time measurements of solar irradiance and wind speed. Eight scenarios are analyzed on the IEEE 30-bus test system in order to determine a cost-effective schedule for thermal power plants with different objectives that reflect fuel cost minimization, voltage profile improvement, emission gases, power loss reduction, and fuel cost minimization with consideration of the valve point effect of generation units. In addition, a carbon tax is considered in the goal function in the examined cases in order to investigate its effect on generator scheduling. A comparison of the simulation results with other recently published algorithms for solving OPF problems is made to illustrate the effectiveness and validity of the proposed ITFWO algorithm. Simulation results show that the improved turbulent flow of water-based optimization algorithm provides an effective and robust high-quality solution of the various optimal power-flow problems. Moreover, results obtained using the proposed ITFWO algorithm are either better than, or comparable to, those obtained using other techniques reported in the literature. The utility of solar and wind energy in scheduling problems has been proposed in this work.

Stochastic Security-Constrained Economic Dispatch of Load-Following and Contingency Reserves Ancillary Service Using a Grid-Connected Microgrid during Uncertainty

In the context of the growing penetration of renewable power sources in power systems causing probabilistic contingency conditions, a suitable economic dispatch model is decisively needed. There is a lack of research in the field of probabilistic mathematical formulation considering the uncertainties due to the stochastic nature of renewables and contingency occurrence, as it is a very complex problem to be solved. The most appropriate model is the stochastic security-constrained economic dispatch (SSCED) model for optimized economic dispatch decisions during uncertainty. However, because of its complexity, it is rarely employed. This paper attempts to solve the complex SSCED problem in the presence of the uncertainty of resources and probabilistic contingency conditions, which is a novel effort in this regard. The SSCED is carried out over multiple periods to provide the load-following or contingency reserves. In the proposed SSCED, the uncertainty problem is addressed by modeling the stochastic wind energy power source by using “probability transition scenarios”. The uncertainty caused by probabilistic contingency conditions in the dispatch schedule is approximated using a “state-specific transition matrix”. The frequency control reserves in contingency conditions are co-optimized with energy, and stochastic security-constrained economic dispatch is achieved. The efforts are put forward to suggest a new market model in the presence of the uncertainty of renewable energy availability. Case studies are examined to show the potential technical and financial advantages of the proposed SSCED through co-optimization. Grid-connected microgrid owners offer frequency control ancillary services by providing load-following ramping reserves in the normal state and contingency reserves in the state of contingency. The probabilistic contingencies considered are generator failure and an underloading condition. A modified “IEEE 30 bus system” is considered a grid-connected microgrid for testing the proposed SSCED. The results show that the greater the flexibility of the resources, the greater the technical and economic benefits. The increase in ramping flexibility of a wind source results in almost an 8.1% reduction in operational costs compared to the base case. The contingency condition analysis shows that the presence of ramping reserves in the system enhances the power system performance, avoiding the cascading effects that ultimately cause a power system failure.

Technical feasibility of Japan-Taiwan-Philippines HVdc interconnector to the Asia Pacific Super Grid

High voltage direct current (HVdc) interconnector is an essential component for large-scale high voltage infrastructure expansion, duplication, and integration of existing power grids. High-voltage alternating current (HVac) grids are increasingly facing hybridization with HVdc, due to the augmented penetration of renewable power sources, and the technological maturation of Offshore Power Systems (OffPS). The authors investigate a submarine interconnector route, poorly explored by the literature, linking Japan, Taiwan, and the Philippines. This proposed offshore HVdc interconnector would support power trading between Japan, Taiwan, Philippines, Indonesia, and Australia. The proposal circumvents some contested territorial disputes in the East and South China Seas. The outcome of this research investigation is a Super-Grid topology that unveils some basic requirements of technical feasibility and reveals enhanced technical and environmentally-friend performance as compared to existing isolated national power grids of the affected countries. The proposed interconnector route lays down in seabed depth not exceeding 3 km. It aims at transmitting tens of gigawatts, clearing the path for a more sustainable and renewable-energy society in Japan, Taiwan, and the Philippines. This HVdc interconnector is still in the initial stage of feasibility studies, and it inspires further research on a possible and future Asia Pacific Super Grid.

Power‐current coordinated control without sequence extraction under unbalanced voltage conditions

The increasing penetration of renewable power sources into power system is calling for an evolution of grid interconnection control strategy. In particular, during asymmetrical grid faults, power turbulence and current distortion of three-phase inverter cannot be balanced unless coordinated control is adopted. Several coordinated control strategies are usable for operating under unbalanced grid voltage nowadays. These control strategies usually require a real-time sequence detection block for reference generation. This study explores a coordinated control strategy without real-time sequence detection. It has three theoretical innovations: (i) the internal mathematical connection between power fluctuation and current quality is derived; (ii) a control strategy with a continuous adjustable factor is proposed. The power oscillations and the current quality can be controlled flexibly by adjustment factor without using the filter and sequence extraction calculation; (iii) the method for selecting the continuous adjustable factor is also presented. The experiment and simulation results prove that these innovations facilitate power-current coordinated control with simple reference selection criteria and excellent performance.

A Novel Hybrid Framework for Co-Optimization of Power and Natural Gas Networks Integrated With Emerging Technologies

In a power system with high penetration of renewable power sources, gas-fired units can be considered as a back-up option to improve the balance between generation and consumption in short-term scheduling. Therefore, closer coordination between power and natural gas systems is anticipated. This article presents a novel hybrid information gap decision theory (IGDT)-stochastic cooptimization problem for integrating electricity and natural gas networks to minimize total operation cost with the penetration of wind energy. The proposed model considers not only the uncertainties regarding electrical load demand and wind power output, but also the uncertainties of gas load demands for the residential consumers. The uncertainties of electric load and wind power are handled through a scenario-based approach, and residential gas load uncertainty is handled via IGDT approach with no need for the probability density function. The introduced hybrid model enables the system operator to consider the advantages of both approaches simultaneously. The impact of gas load uncertainty associated with the residential consumers is more significant on the power dispatch of gas-fired plants and power system operation cost since residential gas load demands are prior than gas load demands of gas-fired units. The proposed framework is a bilevel problem that can be reduced to a one-level problem. Also, it can be solved by the implementation of a simple concept without the need for Karush–Kuhn–Tucker conditions. Moreover, emerging flexible energy sources such as the power to gas technology and demand response program are considered in the proposed model for increasing the wind power dispatch, decreasing the total operation cost of the integrated network as well as reducing the effect of system uncertainties on the total operating cost. Numerical results indicate the applicability and effectiveness of the proposed model under different working conditions.

Modeling of a microgrid’s power generation cost function in real-time operation for a highly fluctuating load

Since the monitored operation of microgrids is a leading trend in the aspect of applying the smart grid concepts, it has become a necessity to maximize the penetration of renewable power resources. In this paper, a novel real-time monitoring operation model of a microgrid is suggested, where the overall cost is mathematically modeled and included in the proposed model. The operation is achieved by assigning switching procedures of the generation sources situated within the microgrid. A continuous mathematical model is proposed for the cost function of the microgrid containing conventional fuel power plants, wind and solar renewable power generation, and battery storage systems. The inclusion of imported power from neighboring microgrids is implied in the mathematical model as well as the exported power to those grids. The mathematical model needs to be discretized in order to imply the separate time slots of the day in the model; hence the introduction of the discretized model is imperative. A simulation test case is carried on the MATLAB software platform and the simulation results are illustrated taking into consideration different practical cases of wind speed, solar irradiance, and batteries state of charge. The studied system is designed to provide electric power for a highly fluctuating load, which is a steel factory located in Jordan. The simulation test runs indicate the importance of energy storage to increase the penetration of renewable power sources in the microgrids.

Policy recommendations for using cool thermal energy storage to increase grid penetration of renewable power sources (1607-RP)

The majority of U.S. states have adopted policies intended to increase renewable energy generation. Because of the intermittent nature of wind and solar resources, in particular, several states have also adopted energy storage policies. Energy storage serves to bridge mismatches between intermittent electricity production and demands from end-use electricity consumers. Because building air-conditioning loads commonly drive peak electric grid demands, storage in the form of thermal energy is an attractive alternative to electric storage technologies. Cool thermal energy storage systems have been used successfully for decades by building owners seeking to shift air-conditioning system electric consumption to off-peak time periods when electricity rates are low. In the current article, the authors present a strategy for controlling cool thermal energy storage systems to enable an increased penetration of intermittent wind and solar generation. It was found that cool thermal energy storage systems operated using this strategy have not only reduced life-cycle costs compared to air-conditioning systems without cool thermal energy storage, but they more effectively utilize the renewable resource. Because cool thermal energy storage is a mature technology and the results show that cool thermal energy storage is more cost-effective than electric energy storage technologies currently in widespread use, policies that aim to increase renewable energy generation should also support increased deployment of cool thermal energy storage as an enabling technology.

A Generation Adjustment Methodology Considering Fluctuations of Loads and Renewable Energy Sources

Increasing penetration of renewable power sources (RESs) is bringing more uncertainties to power systems. Real-time economic dispatch should be able to handle the fluctuations caused by the load as well as RESs, and obtain new optimal operating points after deviating from original generation schedules. This paper proposes a generation adjustment algorithm based on the base point and participation factor method. The objective of the method is to obtain fast adjustments to generators’ real power outputs in real-time economic dispatch. The proposed method achieves significant and consistent improvements comparing to the original participation factor method by using a more precise model of transmission losses in the optimization process. The computational burden and execution time of the proposed method is much less than OPF and heuristic economic dispatch methods. The proposed method and its effectiveness are tested using IEEE 30-bus system, IEEE 118-bus system, and GuangDong 1018-bus system. Simulation results are demonstrated with comparisons and discussions.

Thermodynamic analysis of an integrated energy system based on compressed air energy storage (CAES) system and Kalina cycle

High penetration of renewable power sources into power system leads to significant challenge in balancing of power generation and consumption due to the highly erratic nature of renewable energies. Integrating the energy storage system (ESS) with power system can weaken these negative effects effectively. Compressed air energy storage (CAES) system as one of the grid-scale ESS technologies has grown rapidly in the past few years. However, the temperature of exhaust from low pressure turbine during discharge process is still high enough to utilize. An integrated energy system consisting of a CAES system and a Kalina cycle system 6 (KCS6) is proposed to recover this waste heat. The thermodynamic analyses including energy analysis and exergy analysis are evaluated by using steady-state mathematical model and thermodynamic laws. The second law efficiency of the proposed CAES–KCS6 system can be improved nearly 4% compared to that of the single conventional CAES system. Meanwhile, the parametric analysis is also carried out to evaluate the effects of some key parameters on system performance, such as the turbine inlet temperature (TIT), inlet pressure of low pressure turbine and the air storage cavern temperature. Results show that all of these parameters have positive effect on system exergy efficiency.

A sufficient stochastic framework to capture the uncertainty of load models in the management of distributed generations in power systems

With the high penetration of renewable power sources in the form of distributed generations (DGs), the amount of uncertainty in the power systems is increased greatly. This high uncertainty has affected most of the grid operation strategies including the optimal management of DGs. One significant source of uncertainty is the forecast error in the modeling of the future active and reactive load values. In order to deal with this problem, this paper suggests a new stochastic framework based on the scenario generation process and roulette wheel mechanism. This method converts the stochastic problem into a number of deterministic problems with different probabilities. Since the problem investigated is a complex nonlinear optimization problem, a sufficient optimization algorithm based on the bio-inspired krill herd algorithm is proposed to solve the problem effectively. The satisfying performance of the proposed method is examined on the IEEE standard test system.