Renewable Energy Penetration Level Research Articles

The curtailment paradox in the transition to high solar power systems

Summary Rising penetrations of variable renewable energy (VRE) in power systems are expected to increase the curtailment of these resources because of oversupply and operational constraints. We evaluate the effect on curtailment from various flexibility approaches, including storage, thermal generator flexibility, operating reserve eligibility rules, transmission constraints, and temporal resolution, by using a highly resolved realistic system. Results reveal two aspects of a curtailment paradox as the system evolves to higher solar penetration levels. First, thermal generator parameters, especially in restricting minimum operating levels and ramp rates, affect VRE curtailment more in mid-PV penetration levels (∼25%–40%) but much less at lower (∼20%) or higher (∼45%) PV penetration levels. Second, although allowing VRE and storage to provide operating reserve results in significant operating costs and curtailment benefits, the price suppression effect from these resources reduces incentives for PV to provide operating reserves with curtailed energy.

A DC chopper‐based fast active power output reduction scheme for DFIG wind turbine generators

Increasing penetration level of renewable energy results in unprecedented operational challenges of AC grids due to generation uncertainty and reduced inertia. In grid emergencies, Fast active Power output Reduction (FPR) is demanded to drop wind turbine generator (WTG) power outputs faster than usual. However, the power mismatch between WTG mechanical power input and reduced electrical power output resulted from FPR can lead to rotor overspeed, which cannot be timely nullified by slow-responding pitch control. Therefore, this paper proposes a novel FPR scheme for doubly-fed induction generator (DFIG)-based WTG through coordinated control of DC chopper and pitch angle. Specifically, the FPR command is executed by DFIG rotor-side converter control, while rotor overspeed is restricted by triggering the DC chopper to dissipate the mismatched power. The pitch angle is actuated at a maximal rate to eliminate the mismatched power and switch off the DC chopper. The effectiveness of the proposed FPR scheme is verified through comparative simulation studies under FPR command execution and a grid fault. It shows the proposed scheme can achieve FPR in tens of milliseconds, avoid rotor overspeed and provide low-voltage ride-through capability in a fully-controlled manner.

Optimal operation of integrated energy system considering virtual heating energy storage

The integrated energy system (IES) is the physical carrier of the Energy Internet, whose optimal operation has become a hot topic because of its effectiveness in improving energy utilization efficiency. This paper proposes an optimal operation model of integrated energy system, where the internal energy flow transmission characteristic of the heating network is modeled as virtual heating energy storage by the node method to enhance the operating flexibility of IES. The second-order cone relaxation method and the incremental formulation for the piecewise linearization method are utilized to transform the proposed nonlinear model into mixed integer quadratic constraint programming model, which can be effectively solved by the commercial solver. Case studies show that the proposed model effectively reduces the operating costs and improves the renewable energy penetration level of IES.

Fuzzy Controller-Based Self-Adaptive Virtual Synchronous Machine for Microgrid Application

Low-inertia microgrids are prone to large frequency fluctuations due to high penetration of inverter-fed renewable sources. A virtual synchronous machine (VSM) is capable of handling these frequency fluctuations. VSM imparts inertial behaviour in grid-connected inverters with the help of an electric storage device. Most of the reported VSM control schemes use a constant inertia parameter that leads to an inadequate dynamic frequency response. In this paper, a fuzzy controller-based self-adaptive VSM control scheme is proposed. Here, the value of the inertia parameter is decided using fuzzy rules which take the penetration index of renewable energy, frequency deviation and rate-of-change-of-frequency as the input parameters. These rules are formulated with the help of the sample data obtained from simulations done in MATLAB/Simulink, where the inertial parameter is optimized using a genetic algorithm for various cases. The proposed method is validated for solar photovoltaic-fed microgrid test systems using power-hardware-in-the loop and the results indicate a reduction in maximum frequency deviation compared to the constant inertia parameter based VSM schemes. The introduction of renewable energy penetration level and fuzzy expert system in modifying the inertia parameter are the novel contributions in this paper.

Hosting capacity enhancement of renewable-based distributed generation in harmonically polluted distribution systems using passive harmonic filtering

In spite of being economically viable for numerous applications, renewable energy cannot realise its true penetration potential until some of the barriers are not tackled with excellence. Such barriers are harmonic distortion, distribution cable’s ampacity and voltage rise limits, those put bound on the maximum allowable penetration level of renewable energy. This paper formulates the enhancement of power quality-constrained hosting capacity (HC) as a multi-objective optimization (MOO) problem under several constraints of system’s performance indices (PIs). The considered performance indices are individual order and total harmonic distortion in the line current and point of common coupling (PCC)’s voltage, load power factor (PF), distribution line’s ampacity and steady-state voltage profile. In the formulated multi-objective optimization model, an optimal design of a third-order damped filter and size of the distributed generation (DG) unit is simultaneously determined for achieving maximum hosting capacity and power factor at the PCC while keeping system’s other indices such as total voltage harmonic distortion (TVHD) and total filter cost (FC) incurred at a minimum by obtaining a best-compromised solution using the newly proposed Pareto-based multi-objective firefly algorithm (MOFA). The extension of the multi-objective firefly algorithm is considered for producing the Pareto optimal front and various conclusions are drawn by analysing the trade-offs among the objectives by plotting the same on different 2-axis planes. The optimization efficiency and superiority of the proposed multi-objective firefly algorithm based hosting capacity enhancement approach is validated by comparing the results with those obtained by popular multi-objective PSO (MOPSO) and non-dominated sorting genetic algorithm (NSGA-II) under similar objectives. Also, the results of the proposed methodology are compared with those achieved from one of the most recently introduced hosting capacity enhancement approaches in literature. Eventually, the impacts of different background voltage distortion (BVD) levels and load-side’s nonlinearity levels (NLLs) on filter performance, particularly filter cost as well as enhanced hosting capacity, are analysed and various conclusions are drawn.

On the dispatch of minigrids with large penetration levels of variable renewable energy

The continuous use of fossil fuels for decades in electricity generation has led to dire environmental consequences. This has fostered the incorporation of variable renewable energy resources (RES) to improve the environmental outlook and minimize emissions. This paper presents an approach for the dispatch of a minigrid considering variable solar photovoltaic (PV) generation. Due to the variability of the solar irradiance received from the sun during daytime only, solar irradiance is modeled as a stochastic random variable that is fitted into a Beta probability density function (PDF). The minigrid dispatch problem, modeled using stochastic optimization, is then approximated into a linear equivalent to become a mixed integer linear programming (MILP) problem that can be solved efficiently. The proposed approach is implemented on the modified IEEE 14-bus test system to verify its capability in solving the minigrid dispatch under various test case scenarios. <em> </em>

Power system planning with high renewable energy penetration considering demand response

Electric system planning with high variable renewable energy (VRE) penetration levels has attracted great attention world-wide. Electricity production of VRE highly depends on the weather conditions and thus involves large variability, uncertainty, and low-capacity credit. This gives rise to significant challenges for power system planning. Currently, many solutions are proposed to address the issue of operational flexibility inadequacy, including flexibility retrofit of thermal units, inter-regional transmission, electricity energy storage, and demand response (DR). Evidently, the performance and the cost of various solutions are different. It is relevant to explore the optimal portfolio to satisfy the flexibility requirement for a renewable dominated system and the role of each flexibility source. In this study, the value of diverse DR flexibilities was examined and a stochastic investment planning model considering DR is proposed. Two types of DRs, namely interrupted DR and transferred DR, were modeled. Chronological load and renewable generation curves with 8760 hours within a whole year were reduced to 4 weekly scenarios to accelerate the optimization. Clustered unit commitment constraints for accommodating variability of renewables were incorporated. Case studies based on IEEE RTS-96 system are reported to demonstrate the effectiveness of the proposed method and the DR potential to avoid energy storage investment.

Design and Energy Requirements of a Photovoltaic-Thermal Powered Water Desalination Plant for the Middle East

Seawater or brackish water desalination is largely powered by fossil fuels, raising concerns about greenhouse gas emissions, particularly in the arid Middle East region. Many steps have been taken to implement solar resources to this issue; however, all attempts for all processing were concentrated on solar to electric conversion. To address these challenges, a small-scale reverse-osmosis (RO) desalination system that is in part powered by hybrid photovoltaic/thermal (PVT) solar collectors appropriate for a remote community in the Kingdom of Saudi Arabia (KSA) was designed and its power requirements calculated. This system provides both electricity to the pumps and low-temperature thermal energy to pre-heat the feedwater to reduce its viscosity, and thus to reduce the required pumping energy for the RO process and for transporting the feedwater. Results show that both thermal and electrical energy storage, along with conventional backup power, is necessary to operate the RO continuously and utilize all of the renewable energy collected by the PVT. A cost-optimal sizing of the PVT system is developed. It displays for a specific case that the hybrid PVT RO system employs 70% renewable energy while delivering desalinized water for a cost that is 18% less than the annual cost for driving the plant with 100% conventional electricity and no pre-heating of the feedwater. The design allows for the sizing of the components to achieve minimum cost at any desired level of renewable energy penetration.

A Lyapunov-Function Based Controller for 3-Phase Shunt Active Power Filter and Performance Assessment Considering Different System Scenarios

Shunt active power filter (SAPF) belongs to the class of custom power devices (CPDs) and offers compensation to harmonics originated owing to customer side nonlinear loads, reactive power and unbalance in the distribution power networks functioning in current control mode (CCM). The performance of a SAPF as a harmonic compensator entirely relies on the control technique i.e. the precise detection of the harmonic current components of load that are necessary to be compensated. In the present work, a 3-phase SAPF, inspired by a Lyapunov function based control approach, has been designed for compensation of harmonics resulted in the feeder current owing to the customer side nonlinearity. A control law is determined in the proposed strategy which makes the derivative of the Lyapunov function consistently a negative one for an entire set of stable states. The DC-link capacitor voltage is regulated at constant reference through the proportional-integral (PI) controller. In this method rating of the shunt active power filter is considerably reduced than the other two broadly employed conventional methods. Furthermore, the harmonic compensation efficacy of the proposed Lyapunov function based SAPF is compared with the one based on other two conventional approaches under four different system scenarios namely a simple nonlinear load with and without utility side voltage distortion, a modified IEEE 13 bus test distribution system loaded with a 3-phase chopper fed direct current (DC) motor drive at a single bus and last especially for increasing the harmonic-constrained penetration level of renewable energy. Results obtained through simulation performed in MATLAB/Simulink shows that total harmonic distortion (THD) of source current and dynamic, as well as steady-state performance with Lyapunov function based controller, is significantly improved than the other two conventional methods. Also, the robust compensation performance of the SAPF empowers it to deal with the high penetration of renewable energy.

D-STATCOM d-q Axis Current Reference Control Applying DDPG Algorithm in the Distribution System

The high penetration level of renewable energy in large-scale power systems could adversely affect power quality, such as voltage stability and harmonic pollution. This paper assesses the impacts of Distribution Static Compensator (D-STATCOM), one of the Flexible AC Transmission System (FACTS) devices, on power quality of 4.16kV-level distribution systems via transient and steady-state analysis. Carrier-based Pulse Width Modulation (PWM) control in D-STATCOM generates d-q axis current reference via the PID (Proportional-Integral-Differential) controller to control d-q axis current and voltage. A new control method, via the Deep Deterministic Policy Gradient (DDPG) algorithm-based reinforcement learning (RL), is studied to create a new d-q axis current reference applying to the voltage control, which can improve voltage stability and transient response and derive fast convergence of current and voltage at the D-STATCOM bus. The real-time simulations on an IEEE 13-bus system show that the proposed approach can better control the D-STATCOM than the conventional control methods for enhancing voltage stability and transient performance.

An Optimal Over-frequency Generator Tripping Strategy for Regional Power Grid with High Penetration Level of Renewable Energy

This paper proposes an optimal over-frequency generator tripping strategy aiming at implementing the least amount of generator tripping for the regional power grid with high penetration level of wind/photovoltaic (PV), to handle the over-frequency problem in the sending-end power grid under large disturbances. A steady-state frequency abnormal index is defined to measure the degrees of generator over-tripping and under-tripping, and a transient frequency abnormal index is presented to assess the system abnormal frequency effect during the transient process, which reflects the frequency security margin during the generator tripping process. The scenario-based analysis method combined with the non-parametric kernel density estimation method is applied to model the uncertainty of the outgoing power caused by the stochastic fluctuations of wind/PV power and loads. Furthermore, an improved fireworks algorithm is utilized for the solution of the proposed optimization model. Finally, the simulations are performed on a real-sized regional power grid in Southern China to verify the effectiveness and adaptability of the proposed model and method.

A Fast Penalty-Based Gauss-Seidel Method for Stochastic Unit Commitment With Uncertain Load and Wind Generation

Stochastic network-constrained unit commitment (S-NCUC) can be used to manage the uncertainty of an increasing penetration level of renewable energy effectively. However, the drawbacks of the progressive hedging algorithm (PHA) based solutions are that they are not provably convergent due to the non-convexity of S-NCUC. Additionally, the solution obtained is usually fractional-valued (non-binary) and therefore not readily implementable. In this paper, we apply a novel Penalty-Based Gauss-Seidel (PBGS) algorithm in solving S-NCUC using an exact augmented Lagrangian representation with proven convergence. To improve the computational efficiency of the PBGS, we further propose an accelerating technique with rigorous proof to skip solving scenarios when certain conditions are met during iterations. We numerically validate the proposed algorithms on the IEEE 118-bus system and a practically-sized ERCOT-like system, both with variable wind generation. Numerical results demonstrate the efficacy of the proposed algorithms in yielding high-quality S-NCUC solutions. The merits of the proposed algorithms are also revealed in comparison with PHA and extensive-form (EF) based mixed-integer programming solutions.

Multi-Level Energy Management Systems Toward a Smarter Grid: A Review

Home Energy Management Systems (HEMSs) may not be able to solve network issues, especially in the presence of high penetration level of Electric Vehicles (EVs) and decentral renewable energy. To solve the problem, Grid Energy Management Systems (GEMSs) were introduced. However, because of the contradictory nature of the main objectives of HEMS which are economical oriented on end-users, e.g., cost minimization, and GEMS which are technical oriented on system operators, e.g., maximization of system stability and power quality cannot be satisfied simultaneously. Hence, a multi-level energy management system seems to be necessary to improve the techno-economic performance of the distribution system while satisfying end-users, electricity retailers, and the system operator. Because of the significance of the subject, this paper presents the state-of-the-art regarding different energy management systems at home, aggregator, and network levels. The advantages and disadvantages of each system are discussed and compared, considering their main elements such as objective functions, constraints, optimization algorithms, communication protocols, and impact of EVs. The challenges and limitations in hierarchical energy management are explained. Finally, some future research directions are suggested to improve the multi-level energy management system.

Demand Responsive Market Decision-Makings and Electricity Pricing Scheme Design in Low-Carbon Energy System Environment

The two-way interaction between smart grid and customers will continuously play an important role in enhancing the overall efficiency of the green and low-carbon electric power industry and properly accommodating intermittent renewable energy resources. Thus far, the existing electricity pricing mechanisms hardly match the technical properties of smart grid; neither can they facilitate increasing end users participating in the electricity market. In this paper, several relevant models and novel methods are proposed for pricing scheme design as well as to achieve optimal decision-makings for market participants, in which the mechanisms behind are compatible with demand response operation of end users in the smart grid. The electric vehicles and prosumers are jointly considered by complying with the technical constraints and intrinsic economic interests. Based on the demand response of controllable loads, the real-time pricing, rewarding pricing and insurance pricing methods are proposed for the retailers and their bidding decisions for the wholesale market are also presented to increase the penetration level of renewable energy. The proposed demand response oriented electricity pricing scheme can provide some useful operational references on the cooperative operation of controllable loads and renewable energy through the feasible retail and wholesale market pricing methods, and thereby enhancing the development of the low-carbon energy system.

Toward a More Renewable Energy-Based LFC Under Random Packet Transmissions and Delays With Stochastic Generation and Demand

The load frequency control (LFC) aims to keep the frequency fluctuation of the power grids within certain specified limits, under various load disturbances. However, with the increased usage of renewable energy sources (RESs) in smart grids, it is essential to regulate the conventional power plants, based on renewable energy penetration levels. Moreover, with the decentralized nature of the control operation in smart grids, the communication network between the control center and actuator faces the challenge of random communication delays and packet drops in the form of cyberattacks. In this article, the conventional thermal power plant operations within an LFC have been modified using energy storage elements with an emphasis on maximizing the RES utilization while tackling the problems associated with cyber-physical systems, such as packet drops and random time delays. A filtered proportional&#x2013;integral&#x2013;derivative (PID) controller is tuned in the LFC using the particle swarm optimization (PSO) algorithm, including random time delays and cyberattacks modeled as random packet drops. The tuned PID control performance in the LFC scheme is tested with synthetic stochastic as well as real profiles of RES and load demands. The numerical analysis has been conducted on two-area LFC model with Monte Carlo simulations of stochastic demand and generation profiles. <i>Note to Practitioners</i>&#x2014;We are moving toward more renewable energy-based cyber-physical power grids, and it is becoming increasingly important to understand the limits of the balance between more renewable energy integration versus changing the prime-mover in thermal power generation units to meet uncertain load demands. This article proposes a new load frequency control (LFC) scheme employing a nonlinear dead-zone element between the control signals and the actuators (prime movers), which allows the utilization of the available renewable energy sources (RESs) to meet the load and then send a set-point change command in the thermal power generators if the RES does not meet the load. The robustness of the smart grid stability is also verified with the denial-of-service (DoS)-type cyberattack, which is represented in the form of high probability of packet drops and stochastic time delays in the communication channels between the control center and the generation units. It is also essential to understand how different stochastic profiles may affect the stability and performance of the tuned LFC loops, under random time delays and packet dropouts. These are quantified using the uncertainty bounds of grid frequency, its rate of change, control inputs, and power exchange between the two areas that are analyzed using Monte Carlo simulations with different types of nonstationary load and RES profiles and also using real data to show the effectiveness of the LFC scheme with communication constraints and the resulting imperfections.

Components design and performance analysis of a novel compressed carbon dioxide energy storage system: A pathway towards realizability

To improve the penetration level of renewable energies can give strong support to the development of low-carbon society. Energy storage technologies with excellent performance are benefit to handle the associated problems arise from the fluctuation of renewables. Carbon dioxide is now recognized as a favorite working medium in compressed gas energy storage system. In order to approach the realization of a preferable carbon dioxide based energy storage system, the components design and performance analysis of a novel compressed carbon dioxide energy storage system is proposed in this paper. Firstly, the carbon dioxide compressor and all heat exchangers are designed by considering the real physical properties of carbon dioxide. Then, the system performances under design and off-design conditions are investigated according to the designed heat exchangers, compressor predicted performance maps and the models of other components. Results show that the charge time is 2.02 h, and the discharge time in constant pressure mode and variable pressure mode are 3.64 h and 2.88 h, respectively. The roundtrip efficiency in constant pressure mode is 64.96% with a stable output power, whereas the roundtrip efficiency in variable pressure mode can reach 67.37%. In constant pressure mode, the increased load level results in a raised energy density and a parabolic trend with optimal value at 65.16% in roundtrip efficiency. In variable pressure mode, the turbine inlet temperature has a positive effect on system energy density, whereas the roundtrip efficiency has a parabolic trend.

Research on Comprehensive Value of Electrical Energy Storage in CCHP Microgrid with Renewable Energy Based on Robust Optimization

The combined cooling, heating, and power (CCHP) microgrid system is good for energy gradient utility. At the same time, it can promote the renewable energy (RE) consumption and abate environmental pollution. In a CCHP microgrid system, the electrical energy storage (EES), which can storage and release electrical energy, plays an indispensable role. A robust optimization model of the CCHP microgrid participating in power market transaction is constructed to calculate the CCHP microgrid operation cost in 4 cases. The results show that the EES can significantly reduce the cost of the CCHP microgrid by 13.21%, compared with 8.36% in Group 1 without renewable energy. The EES can reduce the reserved capacity of micro gas turbine units to deal with the precariousness of RE generation and then reduce the CCHP microgrid operation cost by reducing the purchase of energy from the power grid and arbitrage. Finally, the calculation method of comprehensive value of the EES is constructed. The comprehensive value of the EES is higher in Group 2 with renewable energy compared with Group 1 without renewable energy. Through net present value (NPV) calculation and sensitivity analysis, it is found that the RE penetration level and EES cost have the greatest impact on the economic performance of EES. This shows that with the continuous rising of the RE penetration level and the gradual decrease of EES cost, great potential still waits to be tapped in the comprehensive value of EES in the future.

Tidal Supplementary Control Schemes-Based Load Frequency Regulation of a Fully Sustainable Marine Microgrid

The world is targeting fully renewable power generation by the middle of the century. Distributed generation is the way to increase the penetration level of renewable energies. This paper presents load frequency control of a hybrid tidal, wind, and wave microgrid to feed an isolated island. This research is a step towards 100% renewable energy communities in remote seas/oceans islands. The wave and tidal generation systems model are presented. The study presents load frequency control through three supplementary control strategies: conventional integrators, fractional order integrator, and non-linear fractional order integrator. All the controllers of the microgrid are designed by using a novel black widow optimization technique. The applied technique is compared to other existing state-of-the-art algorithms. The results show that the black widow non-linear fractional integrator has a better performance over other strategies. Coordination between the unloaded tidal system and blade pitch control of both wind and tidal systems are adopted in the microgrid to utilize the available reserve power for the frequency support. Simulation and optimization studies are performed using the MATLAB/SIMULINK 2017a software application.

A Survey of Computational Intelligence Techniques for Wind Power Uncertainty Quantification in Smart Grids.

The high penetration level of renewable energy is thought to be one of the basic characteristics of future smart grids. Wind power, as one of the most increasing renewable energy, has brought a large number of uncertainties into the power systems. These uncertainties would require system operators to change their traditional ways of decision-making. This article provides a comprehensive survey of computational intelligence techniques for wind power uncertainty quantification in smart grids. First, prediction intervals (PIs) are introduced as a means to quantify the uncertainties in wind power forecasts. Various PI evaluation indices, including the latest trends in comprehensive evaluation techniques, are compared. Furthermore, computational intelligence-based PI construction methods are summarized and classified into traditional methods (parametric) and direct PI construction methods (nonparametric). In the second part of this article, methods of incorporating wind power forecast uncertainties into power system decision-making processes are investigated. Three techniques, namely, stochastic models, fuzzy logic models, and robust optimization, and different power system applications using these techniques are reviewed. Finally, future research directions, such as spatiotemporal and hierarchical forecasting, deep learning-based methods, and integration of predictive uncertainty estimates into the decision-making process, are discussed. This survey can benefit the readers by providing a complete technical summary of wind power uncertainty quantification and decision-making in smart grids.

Frequency regulation of a weakly connected microgrid using the fuzzy-PID controller

Abstract Microgrid system stability is a major source of concern due to the rapid increase in load demand and a high level of renewable energy penetration. During the grid- connected mode, the microgrid power quality is mainly affected by the level of connection strength with the host grid. Thus, this paper aims to investigate the impact of the implemented control strategy in the microgrid, on frequency response when its level of connection strength is weak, that is, the frequency and voltage are not dominantly controlled by the grid. A control scheme based on a fuzzy logic-based self-tuning PID controller (fuzzy-PID) is used to maintain the frequency within the acceptable range. Simulations illustrate that the frequency dynamic response of the microgrid guarantees a good performance using the fuzzy-PID controller despite a large drop in grid inertia and the presence of disturbances whereas the classical PID controller cannot maintain the frequency within the acceptable range.