High Penetration Of Renewable Energy Research Articles

Studying the Optimal Frequency Control Condition for Electric Vehicle Fast Charging Stations as a Dynamic Load Using Reinforcement Learning Algorithms in Different Photovoltaic Penetration Levels

This study investigates the impact of renewable energy penetration on system stability and validates the performance of the (Proportional-Integral-Derivative) PID-(reinforcement learning) RL control technique. Three scenarios were examined: no photovoltaic (PV), 25% PV, and 50% PV, to evaluate the impact of PV penetration on system stability. The results demonstrate that while the absence of renewable energy yields a more stable frequency response, a higher PV penetration (50%) enhances stability in tie-line active power flow between interconnected systems. This shows that an increased PV penetration improves frequency balance and active power flow stability. Additionally, the study evaluates three control scenarios: no control input, PID-(Particle Swarm Optimization) PSO, and PID-RL, to validate the performance of the PID-RL control technique. The findings show that the EV system with PID-RL outperforms the other scenarios in terms of frequency response, tie-line active power response, and frequency difference response. The PID-RL controller significantly enhances the damping of the dominant oscillation mode and restores the stability within the first 4 s—after the disturbance in first second. This leads to an improved stability compared to the EV system with PID-PSO (within 21 s) and without any control input (oscillating more than 30 s). Overall, this research provides the improvement in terms of frequency response, tie-line active power response, and frequency difference response with high renewable energy penetration levels and the research validates the effectiveness of the PID-RL control technique in stabilizing the EV system. These findings can contribute to the development of strategies for integrating renewable energy sources and optimizing control systems, ensuring a more stable and sustainable power grid.

Incorporate robust optimization and demand defense for optimal planning of shared rental energy storage in multi-user industrial park

The increasing uncertainty and volatility of net load caused by the high penetration of renewable energy leads to higher demand tariffs for industrial park and potentially impacts their economic benefits. To tackle these issues, this paper develops a novel business mode to enable rental energy storage sharing among multiple users within an industrial park, and propose a robust optimization and demand defense-based iterative bi-layer planning framework. The upper layer focuses on the maximization of the investment profitability of shared rental energy storage by developing a robust information gap decision theory optimization. Meanwhile, the lower layer is dedicated to enhancing the demand defense ability of shared rental energy storage in real-time operation through the formulation of a distributed model predictive control. After that, the synchronous alternating direction multiplier method with consistency theory is derived for solving the distributed optimization. Numerical results demonstrate that the proposed shared rental energy storage is 6.391% and 7.714% more economical than shared and self-built energy storage, respectively. Moreover, the iterative bi-layer planning enables flexible energy storage capacity configuration, reduces the impact of net load uncertainty, improves the ability of demand defense, and enhances the system’s overall economy.

A Demand–Supply Cooperative Responding Strategy in Power System With High Renewable Energy Penetration

A Demand–Supply Cooperative Responding Strategy in Power System With High Renewable Energy Penetration

Sub-synchronous oscillation suppression strategy for virtual synchronous generators based on dual-loop sliding mode control

The virtual synchronous generator (VSG) has synchronous voltage source characteristics that can effectively improve the stability of high-penetration renewable energy power systems. However, the output impedance of VSG using traditional voltage-current inner-loop control has lower damping in the sub-synchronous frequency range. Therefore, when the VSG is connected to a grid with series compensation for long-distance transmission of renewable energy, in the sub-synchronous frequency range, there is a potential interaction between the inductive output impedance of VSG and the capacitive impedance of the line, leading to the occurrence of sub-synchronous resonance (SSR). To address this challenge, the stability of a series-compensated grid-connected system employing voltage-current inner-loop control was analyzed using VSG sequence impedance modeling. A dual-loop sliding mode control strategy (SMC-DL) was proposed to suppress SSR occurrence. This control strategy can significantly enhance the damping of VSG’s output impedance in the sub-synchronous frequency range, and effectively suppress SSR. Compared to traditional control strategies, this control strategy does not require the addition of virtual impedance, avoids voltage offset issues, adapts to grids with different series compensation levels. Finally, the effectiveness of this approach was verified through simulations and experiments.

Improved Mayfly Algorithm for Optimizing Power Flow with Integrated Solar and Wind Energy

Across the globe, the transition towards sustainable energy systems necessitates seamless implementation of Renewable Energy Sources (RES) into traditional power grids. Such RESs include solar and wind power. The current research work intends to overcome the challenges associated with Optimal Power Flow (OPF) problem in power systems in which the traditional operation parameters ought to be optimized for effective and trustworthy integration of the RESs. The current study proposes an innovative nature-inspired approach by enhancing the Mayfly algorithm on the basis of mating behaviour of mayflies. The aim of this approach is to tackle the complexities introduced by dynamic and discontinuous nature of solar and wind power. The improved Mayfly algorithm aims at minimizing power losses, emission, optimize voltage profiles, and ensure reliable integration of solar and wind power. The current study outcomes provide knowledgeable insights towards power flow optimization in power systems with high penetration of renewable energy. The application results reveal that the improved mayfly algorithm achieved better efficacy compared to the classical mayfly algorithm and the rest of the optimization algorithms.

Geospatial optimisation of wind and solar energy generation: A framework for optimised resource allocation

Wind and solar power generation profiles exhibit a high degree of power output variability and uncertainty compared to conventional fossil fuel generation plants. However, it is generally accepted that this variability and uncertainty can be mitigated by geographic siting and dispersion of the renewable energy plants, particularly with the view to leverage aggregation effects. In this context, this paper proposes a framework for the geospatial optimisation of wind and solar PV generation for scenarios with a high penetration of wind and solar renewable energy. The proposed optimisation framework represents a probabilistic risk-based approach that seeks to minimise the number of events where high residual load values require ancillary service interventions to maintain power balance. In this approach, the wind and solar resources are categorised in terms of the statistical properties of the associated spatiotemporal wind and solar power profiles for a given set of daily and seasonal Time of Use periods. It is thereby recognised that the resource characteristics and grid impact of wind and solar generation profiles can be interpreted with reference to the daily and seasonal cycles exhibited by the demand profiles, wherein some Time of Use periods are more important than others. The proposed framework is implemented for a number of baseline case studies and optimisation case studies, using wind and solar resource data sets covering South Africa. Overall, it is concluded that this approach can leverage geospatial site optimisation to substantially reduce the risks associated with high residual load events. Furthermore, the proposed framework is highly flexible in that the formulation of the minimum and maximum allocation constraints allows application for real-world scenarios whereby stringent capacity allocation constraints can be applied.

Coupling energy management of power systems with energy hubs through TSO-DSO coordination: a review

Abstract The transition towards a sustainable energy system requires the integration of high-penetration renewable energy sources (RESs) and the use of new energy management frameworks to handle RES variability and uncertainty. The evolution of novel energy management technologies has made it possible to design and operate integrated energy systems (IESs) that couple various energy carriers such as electricity, heat, and gas. The emergence of energy hubs (EHs) offers the potential for greater efficiency, reliability, and flexibility in energy supply and demand. However, integrating EHs into the power system operation remains a significant challenge due to the complex interaction between the transmission system operator (TSO), distribution system operator (DSO), and EHs. Enhanced coordination between TSOs and DSOs is of the utmost importance within this context. This paper first aims to review and highlight the most practical features of EHs including the EH concept, topology, elements, classifications, flexibility resources, and optimization approaches. Subsequently, it describes TSO-DSO roles and all concepts that are in line with the aims of this subject, such as conceptual framework, TSO-DSO coordination schemes, formulation, and solution techniques. Meanwhile, it will identify any remaining research gaps and prospective trends in this respect.

Development pathway and influencing factors of hydrogen energy storage accommodating renewable energy growth

Hydrogen energy is considered an important energy storage mode with medium- and long-term cross-seasonal storage capabilities in scenarios with high penetration of renewable energy (RE). However, there is a lack of research regarding the appropriate scale of hydrogen energy storage (HES) considering different RE power generation scenarios. In response to this gap, the present study aims to establish a power system simulation model to analyze the development pathway of HES while accommodating RE growth with a minimum cost constraint for a power system operation. The impacts of carbon trading prices, advances in HES technology, and RE resource endowment on HES development are also investigated. The findings indicate that the total available solar energy resources in China are 46.94TW, and the total available wind energy resources are 9.52TW. When the share of RE generation reaches 44.33–54.78 % of total power generation, the utilization of HES for balancing power system is advantageous in reducing the overall system costs. Looking ahead to 2060, the year when China is projected to achieve its carbon-neutrality goal, RE is estimated to reach 63.12–91.1 % of total power generation. Consequently, the HES in each province is estimated to contribute to 3.58–18.02 % of total electricity consumption. Provinces with more abundant wind resources relative to solar resources are expected to have a 36.43 % higher demand for HES.

Primary frequency control optimization of coal-fired power plants with high-low position shafts: Employing a revised strategy with power-decoupling and feedforward-compensating

Coal-fired units play a critical role in peak-shaving and frequency regulation with high penetration of renewable energy. The coal-fired unit arranged by high-low position shafts applied in power stations will participate in these regulation processes. In this study, the dynamic model of the 1100 MW supercritical coal-fired unit is built and verified, dynamic characteristics of the primary frequency regulation process are analyzed and a poor control effect of the original control strategy is illustrated. Then, the dynamic coupling relationship between high and low-level units is obtained, and there is a 14-s time delay between the two power outputs, and a revised regulation strategy that employs load distribution factor and flow-rate feedforward-compensating is proposed. Compared with the original control strategy, the revised control strategy can effectively lower fluctuations of the steam parameters by approximately 50%, and the corrective regulation time decreases from 25.1 s and 15.8 s to 6.9 s and 12.1 s under positive and negative load disturbance, respectively. Moreover, it can improve the control effect of low-level unit, whose regulation time under positive and negative load disturbance decreases from 45.7 s and 27.7 s to 9.4 and 20.6 s, respectively. This research can provide technical guidance for the operation of coal-fired units.

Thermal performance of phase change material based heat exchanger filled with inorganic salt/expanded graphite

Solid-liquid latent heat storage, featured by the high-density energy storage capacity, is promising in facilitating the high-penetration renewable energy. However, the design of the heat exchanger is challenging due to the conflicting objectives between the heat transfer efficiency and energy storage capacity. To this end, this paper introduces a comprehensive evaluation index, based on which an orthogonal based design optimization method is proposed for heat exchangers filled with inorganic salt/expanded graphite composite phase change material (CPCM). A thermophysical properties based numerical model is built to describe and analyze the solidification and melting of CPCM. The comprehensive evaluation index is utilized to quantify the overall change in heat storage/release performance relative to the mass change of the baseline phase change heat exchanger. The orthogonal experimental method is employed to investigate the impact of various heat exchanger structural factors on the comprehensive performance, followed by optimization design. Simulation results indicated that the longitudinally finned heat exchanger outperforms the other two heat exchangers in terms of the comprehensive evaluation index. Moreover, the inlet fluid temperature exerts a more significant impact on the comprehensive evaluation index compared to the inlet fluid velocity. Additionally, the effects of pipe spacing, fin number, fin height, and tube material on the thermal performance of the longitudinally finned heat exchanger were examined and analyzed. Among these factors, the fin height was identified as the most substantial contribution to the heat transfer performance, while the tube material has a minor effect.

Multi-objective configuration and evaluation of dynamic virtual inertia from DFIG based wind farm for frequency regulation

Modern power systems are gradually evolving into low-inertia systems due to the integration of high penetration of renewable energy and power electronic equipment. For the natural inertia on the rotor, doubly-fed induction generator (DFIG) based wind farms are expected to provide inertia support for systems. The paper proposed an analytical model of DFIG with df/dt inertia control for studying frequency dynamics, and investigates the optimal design of virtual inertia from wind farms, as well as its equivalent inertia contribution to system. The inertia response differences between DFIG and synchronous generator (SG) are discussed and analyzed for the first time, and then to accurately describe the frequency response with low cost, an analytical model of DFIG with inertia control is developed considering wind turbine aerodynamic characteristics. The explicit expression of virtual inertia provided by DFIG is derived according to the kinetic energy equilibrium, and the key factors that dominate the magnitude of virtual inertia are illustrated. Based on the system frequency response model including wind farms, taking the system frequency nadir (SFN) and the time of reaching nadir (ToN) as targets, a multi-objective optimal configuration model of virtual inertia is constructed and solved through MOPSO. The time domain simulations of a generic two machine four bus system and modified IEEE 9-bus test system are both performed in MATLAB/Simulink, the accuracy and efficiency of our proposed DFIG model on system frequency dynamics research are demonstrated, the pareto frontiers of virtual inertia configuration under different working conditions are optimized and analyzed as well as the impacts of different parameters on inertia evaluation.

Demand response model: A cooperative-competitive multi-agent reinforcement learning approach

This study introduces a novel Demand Response (DR) Model based on Multi-agent Reinforcement Learning (MARL-DR), comprised of a pricing and incentives scheme aimed at improving the accuracy of existing demand response strategies. Furthermore, this new approach represents a flexibility solution to prevent sharp price variations caused by the high penetration of unconventional renewable energy, which are directly passed on to end-users. The model employs a cooperative-competitive MARL-DR technique based on Q-learning, with the goal of determining optimal prices and incentives that maximize benefits for both customers and electric Service Provider (SP). In this regard, the model has the capability to offer customers pricing options in both real-time and time-of-use, to actively adjust each user's consumption. Additionally, through demand characterization factors, such as the coincidence factor (CF), the of typical user behavior is improved, and the influence of individual user demand on system peak demand is more accurately detected. It is also demonstrated that the cooperative-competitive approach offers better performance compared to other approaches. Finally, a sensitivity analysis is presented at various stages of the model to verify its accuracy and efficiency in pricing and incentives formulation.

Multi-scenario flexibility assessment of power systems considering renewable energy output uncertainty

The widespread adoption of renewable energy sources presents a significant challenge to the flexibility of power system. To assess the flexibility of the power system in scenarios with uncertain renewable energy output, it is crucial to quantify it quantitatively. This evaluation plays a vital role in planning flexible regulatory resources and dispatching resources for both the energy source and load. This study introduces a novel flexibility assessment model tailored for power grids with high renewable energy penetration, specifically addressing uncertainty associated with wind and PV. By analyzing the impact of wind and PV uncertainty on system flexibility, the paper proposes an improved cohesive hierarchical cluster analysis method, incorporating reliability considerations based on the Davies-Bouldin classification reliability index. Additionally, the study develops models for flexibility resources and demands within high renewable energy power systems, along with quantitative assessment indicators across three key aspects. Through a structured flexibility assessment process accounting for wind and PV uncertainty, the effectiveness of the proposed approach is validated using real-world data from a renewable energy power grid in Shandong province. A set of typical renewable energy output scenarios with uncertainty is constructed using the improved hierarchical cluster analysis method. The study then analyses the impact of different wind and PV penetration rates and the proportion of energy storage units on system flexibility by the flexibility assessment model to validate the proposed method's effectiveness.

Available Transfer Capability Assessment of Multiarea Power Systems with Conditional Generative Adversarial Network

Available transfer capability (ATC) is an important measurement index to evaluate the security margin of interconnected power grids and serve as a reference for the transmission right transaction. In modern power systems, ATC is affected by the transmission network topology, renewable power output uncertainty, and load demand uncertainty. Traditional works usually model the power source-load uncertainty by using robust optimization, interval optimization, or chance-constraint optimization, which cannot fully reflect the probabilistic distribution of the daily source-load uncertainty. This paper proposes an ATC assessment methodology based on the typical stochastic scenarios of renewable output and load demand of multiarea power systems. Furthermore, the conditional generative adversarial network (CGAN) algorithm is adopted to generate and select representative scenario sets based on historical raw data, which can fully reflect the usual operating condition of a system with high renewable energy penetration. The scenario set that is fed into the ATC assessment model can fully characterize the impact of source-load uncertainty on daily ATC. Finally, the proposed method is verified by a modified three-area IEEE 9-bus system and a real-world provincial power system.

Design and implementation of controlled load damping factor controller in series connection for power systems with high penetration of renewable energy

As sustainable natural sources such as wind and solar are increasingly integrated into power grids, conventional fossil fuel-based generating units will be gradually replaced, resulting in enormous pressure on frequency regulation of the novel power systems. This paper proposes a method for implementing a cascaded load-damping factor controller based on the controlled load-damping factor controller. Series-type voltage regulators, namely, solid-state transformers or dynamic voltage restorers, can regulate the voltage of the 10 kV feeder within the allowed range. Therefore real-time control of composite load power is realized, which significantly improves the primary frequency regulation capability of novel power systems with renewable energy generation(REG). The effect of actuator regulation time constant is considered, The impact of dynamic loads on the primary frequency regulation of the controller, designed based on a static load model, is analyzed, The effectiveness of the controller on the primary frequency regulation under different renewable energy penetration rates is discussed, The effectiveness of the controller in smoothing short-term power fluctuations of REG is examined. Finally, the effectiveness of the series-connected controlled load-damping factor controller in primary frequency regulation is verified on the RTDS simulation experimental platform. The proposed control system is characterized by a simple structure, easy implementation, and convenient maintenance, which can realize the coordination and unification of reactive-voltage and active-frequency control of the novel power system in the 110 kV customer substation and is beneficial to practical application in engineering.

Demand flexibility and cost-saving potentials via smart building energy management: Opportunities in residential space heating across the US

Leveraging demand-side flexibility resources (e.g., buildings) is a crucial and cost-effective strategy for addressing the operational and infrastructure-related challenges in power grids pursuing deep decarbonization with high renewable energy penetration. However, the demand flexibility opportunities and financial benefits for residential space heating, which are sizeable demand-side flexibility resources, through emerging building energy management solutions (i.e., smart control and phased change material (PCM) thermal storage) across the US are not fully understood. In this paper, we systematically assess the demand flexibility and cost-saving/revenue potentials in residential space heating through detailed building-level simulations for five consecutive years at a 5-min temporal resolution in 20 metro areas across the high-heating-demand regions of the US. The results show a high degree of synergy between PCM thermal storage and smart control, which enables substantial demand flexibility potential, reaching 98.5% of peak load shifting, and electricity cost-saving/revenue potential, reaching 338.3% of electricity cost reductions, for residential space heating in the US. By achieving such performance, adopting smart control and PCM thermal storage is financially viable in 50% of the tested metro areas. The results reveal that the demand flexibility and cost-saving/revenue potentials of residential space heating in the US are further enhanced by higher volatilities in electricity prices. Active PCM thermal storage has lower energy efficiency but much higher energy flexibility than passive PCM thermal storage. Based on the findings, recommendations for integrating PCM thermal storage and smart control systems within residential space heating are provided.

Voltage Optimization in Active Distribution Networks—Utilizing Analytical and Computational Approaches in High Renewable Energy Penetration Environments

This review paper synthesizes the recent advancements in voltage regulation techniques for active distribution networks (ADNs), particularly in contexts with high renewable energy source (RES) penetration, using photovoltaics (PVs) as a highlighted example. It covers a comprehensive analysis of various innovative strategies and optimization algorithms aimed at mitigating voltage fluctuations, optimizing network performance, and integrating smart technologies like smart inverters and energy storage systems (ESSs). The review highlights key developments in decentralized control algorithms, multi-objective optimization techniques, and the integration of advanced technologies such as soft open points (SOPs) to enhance grid stability and efficiency. The paper categorizes these strategies into two main types: analytical methods and computational methods. In conclusion, this review underscores the critical need for advanced analytical and computational methods in the voltage regulation of ADNs with high renewable energy penetration levels, highlighting the potential for significant improvements in grid stability and efficiency.

Performance evaluation and operation optimization of a combined heat and power plant integrated with molten salt heat storage system

Combined heat and power is an effective way to enhance the energy efficiency of thermal power plants. The solar and wind power are intermittent, and dispatchable power sources are critical to balance the supply and demand sides. To facilitate the high penetration of renewable energy, combined heat and power (CHP) plants should provide more and more peak shaving services for the power grid. The thermal energy can be stored in and released from the molten salt heat storage system (MSHSS). The integration of MSHSS can enlarge the adjustable range of the power load of CHP plants, so it is a potential way to enhance the operational flexibility of CHP plants supplying industrial steam and power. In this study, the MSHSS integrated within CHP plants are proposed, which store a part of sensible heat energy carried by extraction steam before supplying to industrial users. The heat storage capacity dominates the flexibility enhancement, and indicators to evaluate energy performances of the proposed system are defined. Moreover, models for the operation optimization of CHP plants integrated with the MSHSS are developed, and a case study of a 350 MW CHP unit is conducted. When the reheat steam is extracted to supply to industrial steam, the maximum and minimum adjustable power loads can be increased and decreased by 17.13 and 12.01 MW, respectively with the integration of the MSHSS. Moreover, during the peak, flat, valley time of reference day, the heat supply coal consumption rates are 73.94, 74.19 and 81.03 kg t−1,respectively. With the operation scheduling of CHP plant integrated with MSHSS, the MSHSS with reheat steam as heat source show the lowest coal consumption rate as 1553.79 ton/day.

A load-side resource aggregation method based on Minkowski Sum

In the context of high penetration of renewable energy in new power systems, load-side resources have good regulation potential to improve system flexibility. In order to fully exploit the regulation potential of load-side resources, the power regulation range and regulation cost of load-side resources need to be aggregated first. Based on the establishment of a unified model of the scheduling feasible region of the load-side resources, the computation of the Minkowski sum of the feasible region of the load-side resources is realized through the linear superposition of the maximally inner-approximated feasible region. The decomposition scheme of the instructions of the aggregator and the computation method of the equivalent cost function of the aggregator are proposed. The effectiveness of the proposed aggregation method is verified by arithmetic simulation.

Low Inertia Power System Planning Considering Frequency Quality Under High Penetration of Renewable Energy

Low Inertia Power System Planning Considering Frequency Quality Under High Penetration of Renewable Energy