Large-scale Renewable Energy Integration Research Articles

A Deep Learning Approach Based on Novel Multi-Feature Fusion for Power Load Prediction

Adequate power load data are the basis for establishing an efficient and accurate forecasting model, which plays a crucial role in ensuring the reliable operation and effective management of a power system. However, the large-scale integration of renewable energy into the power grid has led to instabilities in power systems, and the load characteristics tend to be complex and diversified. Aiming at this problem, this paper proposes a short-term power load transfer forecasting method. To fully exploit the complex features present in the data, an online feature-extraction-based deep learning model is developed. This approach aims to extract the frequency-division features of the original power load on different time scales while reducing the feature redundancy. To solve the prediction challenges caused by insufficient historical power load data, the source domain model parameters are transferred to the target domain model utilizing Kendall’s correlation coefficient and the Bayesian optimization algorithm. To verify the prediction performance of the model, experiments are conducted on multiple datasets with different features. The simulation results show that the proposed model is robust and effective in load forecasting with limited data. Furthermore, if real-time data of new energy power systems can be acquired and utilized to update and correct the model in future research, this will help to adapt and integrate new energy sources and optimize energy management.

Topological Structure and Control Strategy of E-UPFC

This article proposes a unified power flow controller with energy (E-UPFC) installed at the renewable energy grid connection node of the transmission network. Compared with other unified power flow controllers (UPFCs), the proposed E-UPFC can not only regulate the power flow on its connected transmission lines, but also suppress the power fluctuations of grid-connected nodes injected with large-scale renewable energy. At the same time, with the installation position of E-UPFC transferred from the transmission line to the node, the original stiff node can be transformed into a flexible node that can regulate the injected power flexibly. First, the PV grid-connected system with E-UPFC is introduced, and its principle of power flow regulation is detailed. In addition, the topology, mathematical modeling, and control strategies of E-UPFC are discussed. Finally, the E-UPFC is applied to the IEEE 3-generator 9-bus system with large-scale renewable energy integration in Matlab/Simulink in order to verify the correctness and feasibility of E-UPFC and its control strategies.

Research on Coordinated Optimization of Source-Load-Storage Considering Renewable Energy and Load Similarity

Currently, the global energy revolution in the direction of green and low-carbon technologies is flourishing. The large-scale integration of renewable energy into the grid has led to significant fluctuations in the net load of the power system. To meet the energy balance requirements of the power system, the pressure on conventional power generation units to adjust and regulate has increased. The efficient utilization of the regulation capability of controllable industrial loads and energy storage can achieve the similarity between renewable energy curves and load curves, thereby reducing the peak-to-valley difference and volatility of the net load. This approach also decreases the adjustment pressure on conventional generating units. Therefore, this paper proposes a two-stage optimization scheduling strategy considering the similarity between renewable energy and load, including energy storage and industrial load participation. The combination of the Euclidean distance, which measures the similarity between the magnitude of renewable energy–load curves, and the load tracking coefficient, which measures the similarity in curve shape, is used to measure the similarity between renewable energy and load profiles. This measurement method is introduced into the source-load-storage optimal scheduling to establish a two-stage optimization model. In the first stage, the model is set up to maximize the similarity between renewable energy and the load profile and minimize the cost of energy storage and industrial load regulation to obtain the desired load curve and new energy output curve. In the second stage, the model is set up to minimize the overall operation cost by considering the costs associated with abandoning the new energy sources and shedding loads to optimize the output of conventional generator sets. Through a case analysis, it is verified that the proposed scheduling strategy can achieve the tracking of the load curve to the new energy curve, reducing the peak-to-valley difference of the net load curve by 48.52% and the fluctuation by 67.54% compared to the original curve. These improvements effectively enhance the net load curve and reduce the difficulty in regulating conventional power generation units. Furthermore, the strategy achieves the full discard of renewable energy and reduces the system operating costs by 4.19%, effectively promoting the discard of renewable energy and reducing the system operating costs.

Static-Voltage-Stability Analysis of Renewable Energy-Integrated Distribution Power System Based on Impedance Model Index

Static-voltage stability has become one of the most significant risks faced by large-scale renewable energy integration. However, traditional methods for static-voltage-stability analysis are often overly complex. This paper constructs an equivalent impedance model for renewable energy-integrated distribution power systems, proposing a static-voltage analysis method for renewable energy-integrated distribution power systems based on an impedance model index. This method has been verified to be applicable not only to a renewable energy single-infeed system but also to a multi-infeed system. Furthermore, an analysis is conducted on the influence of the integration capacity, location of renewable energy, and the topology of networks on the impedance model index, indicating that a higher impedance model index corresponds to greater static-voltage-stability margins in the system. Hence, during the planning of renewable energy integration, the plan with the highest impedance model index should be selected. Finally, the accuracy of the analysis method and conclusions in this paper was validated based on the IEEE 14-node system.

Assessment of the synthetic inertial response of an actual solar PV power plant

As the large-scale integration of renewable energy grows, inertia in power systems is reduced, which causes the rate of change of frequency to increase and worsens the frequency nadir during disturbances. There is, therefore, an urgent need to accelerate the development of international standards and regulations that require solar PV systems and other renewable installations to also contribute to stability in the network by emulating inertia. Spain is a pioneer in this field: it is currently the only country that includes the synthetic inertia technical requirement in its grid code. In this context, the present paper analyzes the synthetic inertial response of a PV system model that forms part of an actual PV power plant in Spain under two scenarios, according to its grid code: i) when measurements are conducted at the power conversion PV system level and hence only its control algorithms are executed; and ii) when measurements are conducted at the power plant level and the power plant controller also influences the response of the entire installation. Among its aims, this paper seeks to thoroughly assess the response of an actual PV power conversion system that has the capability of actively contributing to frequency regulation in a network and to critically analyze the suitability of having a power plant controller when the PV facility contributes to power-frequency control by means of an inertial response. The results reveal, at the power conversion PV system level, that the inertia emulation module significantly reduces the response times of the installation to changes in frequency -by more than 68% in the worst case under frequency increases, and by 73% under frequency decreases, thus contributing to more rapid power-frequency regulation in the network. However, at the power plant level, the power plant controller disturbs the PV power conversion system’s behavior and causes the opposite effect: response times increase -up to more than 7% in the most unfavorable case when the inertia module is enabled.

Optimal scheduling for wind-solar-hydro hybrid generation system with cascade hydropower considering regulation energy storage requirements

Large-scale integration of renewable energy into the grid can lead to significant changes in the net load, peak-to-valley difference, peak and valley occurrence time of the power system. As a result, the power of hydropower plants must take a rapid adjustment response. Aiming at the coordinated operation of multiple energy sources, such as wind power, solar power, cascade hydropower station and energy storage pumping station, a coordinated scheduling model is proposed which can fully improve the consumption capacity of wind and solar power by aiming at the maximum power generation, minimum net load fluctuation and minimum wind and solar abandonment. Through the configuration of three different pumping station capacities, the influence of energy storage pumping station capacity on the complementary power generation system is analyzed. When the pumping station capacity is large enough, the output of the wind and solar can be completely consumed. The studies show that the cascade power station and pump energy storage regulation have a strong net load filling valley effect, which can effectively reduce the impact of wind and solar access on system operation, maintain the efficient and stable operation of the unit, and ensure the absorption rate of renewable energy.

Network for the Large-Scale Integration of Renewable Energies in Electrical Systems (RIBIERSE-CYTED, 723RT0150): Results for 2023

The RIBIERSE-CYTED network, i.e., the network for the large-scale integration of renewable energies in electrical systems (723RT0150) (2023-2026) is a hub for researchers and technologists belonging to Ibero-American universities, companies, and local administrations. This network promotes cross-training, mobility between centers, and the dissemination and implementation of technical and training activities aimed at analyzing and developing opportunities for the maximal integration of renewable resources, seeking a more sustainable energy model while allowing for the increased use of renewable resources and electric mobility (e-mobility).

Dynamic characteristics and economic analysis of a coal-fired power plant integrated with molten salt thermal energy storage for improving peaking capacity

Improving the peaking capacity of coal-fired units is imperative to ensure the stability of the power grid, thus facilitating the grid integration and popularization of large-scale renewable energy. To address this issue, this paper introduces a new concept that combines molten salt energy storage with coal-fired power plants. The proposed design consists of extracting a portion of steam from the turbine side and adjusting the extracted steam mass flow rate by adjusting the valve opening to improve the dynamic characteristics of a coal-fired power plant in terms of both increasing the peaking depth and peaking speed. A coal-fired boiler with integrated thermal energy storage was dynamically modeled using Dymola and its accuracy was verified. The results show that the highest equivalent round-trip efficiency is achieved by extracting from the main steam and discharging into the feedwater during charging process and from reheater1 and discharging into the feedwater during discharging process; The peaking potential during charging and discharging is 12.83 % Pe and 6.86 % Pe respectively, with maximum peaking rates of 9.27 % Pe/min and 5.11 % Pe/min respectively, and the optimal peaking method is also proposed according to the different peaking situations. The total cost of equipment and materials to retrofit the conventional coal-fired units was 19,948,193 USD and the levelized cost of delivery was 151.29 USD/MWh.

Multi-Objective Short-Term Optimal Dispatching of Cascade Hydro–Wind–Solar–Thermal Hybrid Generation System with Pumped Storage Hydropower

Aiming to mitigate the impact of power fluctuation caused by large-scale renewable energy integration, coupled with a high rate of wind and solar power abandonment, the multi-objective optimal dispatching of a cascade hydro–wind–solar–thermal hybrid generation system with pumped storage hydropower (PSH) is proposed in this paper. Based on the proposed system, the scheduling operation strategy takes into account the complex restrictions of cascade hydropower as well as the flexibility of the PSH. According to various scenarios, the NSGA-II approach is adopted to address the optimization problem, minimizing the system’s residual load variation and operation cost. The Pareto solution sets are contrasted and evaluated, applying the TOPSIS with CRITIC weighting. Additionally, the scheduling output of thermal power, cascade hydropower, and PSH is given in terms of different scenarios. The results demonstrate that the allocation of PSH to a hybrid energy system can significantly reduce the operation cost and the fluctuation in the residual load.

A new optimization approach considering demand response management and multistage energy storage: A novel perspective for Fujian Province

Due to the inherent uncertainty and the mismatch between renewable energy output and load demand, renewable energy-based system optimization is increasingly important in China. Therefore, electricity demand response (EDR) is critical to the stability and efficiency of an integrated renewable energy system (IRES). The customers' demand response under the time-of-use (TOU) mechanism is the key to the entire process. However, how to optimize integrated renewable energy system based on accurately identifying the electricity demand response participation of customers is one of the main issues at this stage. To solve the problem, this paper presents a novel approach for integrated renewable energy system optimization considering electricity demand response management and multistage energy storage systems from the perspective of Fujian Province, China. To enable large-scale renewable energy integration, the original output is processed using the Hampel identifier (HI)-Savitzky-Golay filter (SGF) technology. A machine learning (ML)-based identification model is developed for EDR to determine whether the customers are participating or not. Driven by the time-of-use mechanism, customers can make dynamic adjustments based on the Logistics function. After that, an integrated renewable energy system model optimization strategy is proposed based on satisfying customers’ electricity demand response. Throughout the optimization process, the multistage energy storage system plays a vital role in the residual fluctuation absorption for renewable energy filtering, the dynamic adjustment process of the demand response, and the integrated renewable energy system optimization. Through extensive case studies and discussions, it is demonstrated that random forest (RF) and support vector machine (SVM) reach 80 % identification accuracy with a 20 % testing ratio. According to the optimization approach proposed in this paper, the minimum operation cost can be calculated to be 9.336*106 CNY. The methodology presented in this paper establishes a special relationship between electricity demand response identification and dynamic adjustments, and provides an optimization model for Fujian Province, China, which is a reliable reference for future work.

Large-scale integration of renewable energies by 2050 through demand prediction with ANFIS, Ecuador case study

The growing reliance on hydroelectric power and the risk of future droughts pose significant challenges for power systems, especially in developing countries. To address these challenges, comprehensive long-term energy planning is essential. This paper proposes an optimized electrical system for 2050, using Ecuador as a case study. For forecasting electricity demand, a Neuro-Fuzzy Adaptive Inference System is employed, utilizing real historical data. Subsequently, the EnergyPlan software constructs a long-term energy consumption model, exploring three scenarios based on Ecuador's energy potential. The first scenario represents a 'business as usual’ approach, mirroring the current trend in the Ecuadorian electricity system. In contrast to the second scenario, it encompasses a broader range of renewable sources, including offshore wind, pumped storage, biomass, and geothermal energy. The third scenario extends the second one by incorporating demand response systems, such as vehicle-to-grid and hydrogen-to-grid technologies. In terms of novelty, this study highlights the innovative use of the Neuro-Fuzzy Adaptive Inference System for demand forecasting, along with a comprehensive exploration of multiple scenarios to optimize the electrical system. Research findings indicate that the integration of these new renewable energy sources not only reduces electricity import costs but also ensures surplus electricity production. Consequently, it is anticipated that the 2050 electricity system will reduce its dependence on hydroelectric energy while adopting photovoltaic and wind energy with penetration rates of 65 %, 11.2 %, and 9 %, respectively. This transition will be facilitated by a pumped storage system with a 28 % penetration rate and enhanced connectivity with neighboring countries, enabling the seamless integration of electric and hydrogen vehicles.

Risk-averse energy management of hydro/thermal/pumped storage complementarily operating with wind/solar: Balancing risk, cost and carbon emission

Large-scale grid integration of variable renewable energy is crucial for achieving decarbonized development. However, this integration requires frequent regulation of flexible power sources for complementary operation, which can lead to wear-and-tear and fatigue damage to key components. This poses potential risks to flexible power sources. Existing studies have primarily focused on limiting unit startups, while have neglected the risk of frequent power regulation. Thus, this work proposes a risk-averse short-term scheduling method for a Wind-Solar-Cascade hydro-Thermal-Pumped storage hybrid energy system to balance frequent regulation risk, cost, and carbon emission: (1) a risk-averse short-term scheduling model is proposed, considering multilayer constraints; (2) a multi-objective hybrid African vulture optimization algorithm is proposed to effectively solve the scheduling problem including continuous and discrete variables. A case study in the Songhua River basin, China shows that: (1) compared with traditional models, the proposed model reduces the risk by 31.4% and enhances the comprehensive performance in balancing the three objectives by 22.4%; (2) the proposed algorithm performs robustness and search capability advantages, with improvements of 33.01% and 21.44% respectively, in solving the problem of challenging constraints and mixed decision variables. Overall, this work contributes to enhancing the management of large hybrid energy systems.

Power investment and transmission network expansion in China and its neighbouring countries towards carbon neutrality

Transnational power cooperation contributes to promoting the large-scale use of renewable energies in Asia, but how to develop the structure of power transmission network deserves careful investigation. This paper thoroughly investigates the structure of power investment and transmission network in China and neighbouring countries, aiming to promote the large-scale use of renewable energies around Asia. Various power-generation and power-transmission technologies are considered in detail, and transnational power interconnection risk cost and carbon emission cost are embedded into a bottom-up optimization model. The future power-generation structures and networks installed under various policy scenarios are analysed in detail, and the corresponding economic, carbon emission reduction and renewable energy consumption benefits are evaluated. The results show that power transmission between China's internal regions and neighbouring countries can save $56.7 billion for the entire power system through the large-scale grid integration of renewable energy. The installed capacities of wind power, solar power, and hydropower renewable energy power generation could be increased by 141 GW, 163 GW, and 58 GW, respectively. The construction of transnational transmission lines from Central Asia to Northwest China, Mongolia to North China, the Russian Far East to Northeast China, and Northeast China to South Korea could effectively promote the utilization of renewable energy in the Russian Far East, Mongolia and Central Asia and further reduce carbon emissions in China, Japan and South Korea.

Geothermal energy exploitation in an island-based 100% renewables strategy. Case study of Tenerife (Spain)

The large-scale integration of non-dispatchable renewable energy must be carefully planned, especially in poorly robust electrical energy systems. Tenerife is the most populated of the islands in the Canary Archipelago (Spain) and has an isolated electrical system. It is envisaged for the horizon of 2040 that the island will have a high contribution of non-dispatchable renewable energies (fundamentally wind and solar photovoltaic). Tenerife also has exploitable high enthalpy geothermal resources. An energy planning model is implemented in this paper based on which different scenarios with high non-dispatchable renewable energy penetration in the island's electrical system are simulated. Geothermal energy is also incorporated in the simulations as a dispatchable renewable energy source, with the power required sized according to the hypotheses of each scenario. The results are compared according to different technical and economic parameters. An evaluation is also made of the influence that the capacity of the electrical system to take on higher non-dispatchable renewable power and the presence of energy accumulation systems might have on the minimization of losses in the non-dispatchable renewable energy capacity factor. The results of the simulated scenarios show that geothermal energy could meet up to 28.8% of the island's electrical energy demand, increasing the stability, flexibility and supply guarantee of system. A 100% renewable system could be possible through the incorporation of this energy source and the planning of energy accumulation systems. In the case study, the economic saving for Spain's electrical system could exceed 400 €million per year.

Comprehensive benefit evaluations for integrating off-river pumped hydro storage and floating photovoltaic

Integrating pumped hydro storage with wind-solar power is an effective method for large-scale integration of renewable energy. The integration of floating photovoltaics with pumped hydro storage solves the issues of unstable output from photovoltaic generation and limited land resources. However, traditional pumped hydro storage has limitations in terms of siting and structure, resulting in environmental issues and opposition when integrated with floating photovoltaics. To overcome these limitations, a novel integrated generation system that combines off-river pumped hydro storage and floating photovoltaic is proposed. Additionally, an optimized scheduling model is developed for the hybrid energy power system with the objective of minimizing operating costs. Finally, a case study of a large clean energy project in Qinghai, China is conducted to quantitatively evaluate the comprehensive benefits of this integrated system in hybrid renewable energy power generation from economic, technical, and environmental perspectives. The results indicate that the combination of off-river pumped hydro storage and floating photovoltaics provides improved technological, economic, and environmental benefits. Specifically, under the premise of meeting the power supply rate and ensuring power transmission reliability, the integration of off-river pumped hydro storage and floating photovoltaic generation system surpasses the traditional integration of pumped hydro storage and photovoltaics in multiple aspects. The integrated system can increase power transmission by 26%, decrease investment costs by 15.04%, lower operating costs by 31.75%, save land use by 4.78 km2, and evaporation reduction efficiency by 44.6%. The OFPHS + FPV operation scheme can provide a reference for the development of pumped hydro storage and photovoltaic industries in resource-constrained areas and provide technical guidance to improve the flexibility of regional power systems.

A decision-making method for the operation flexibility enhancement of hybrid cascaded MTDC

To enable the integration of large-scale renewable energy, hybrid HVDC technology, which combines the technical advantages of LCC-HVDC and VSC-HVDC, is being gradually deployed in the power grid nowadays. The operation of the Wu-dong-de Hybrid DC Project and the Jian-su Hybrid cascaded MTDC Project has proved its advantages. However, for the simultaneous application of different converter station technologies in the system, the control strategies become complex. Issuing appropriate control instructions to ensure system stability according to operational requirements is an issue that cannot be ignored in decision-making. Even under abnormal conditions, when the topology changes due to various failure scenarios, reasonable decision-making and precise control instruction definitions are required. To achieve flexible planning of the MTDC system, this paper presents a decision-making method for control strategies of a hybrid cascaded MTDC system, which analyzes the control strategy combinations selected for normal and abnormal conditions of the MTDC system. In addition, a control instruction calculating method and decision-making process for precise control in normal and abnormal control conditions is proposed. Simulation results based on a five-terminal hybrid cascaded MTDC in PSCAD/EMTDC have verified the effectiveness of the proposed method.

A combined wind-PV power prediction method based on clustering prescreening

Due to the volatility caused by the high uncertainty of renewable energy, the large-scale integration of renewable energy into the electric power grid brings huge challenges to the scheduling and operation of the electric power system. Effective power prediction of power source can not only relieve the pressure of power system peaking and frequency regulation, but also improve the accuracy of decision-making. So as to make the most of the information between wind and photovoltaic power source, this paper puts forward a wind-PV joint power forecasting model. First, the raw power data is pre-screened by clustering to select the closer connection between wind and PV power; next, the screened data are passed through the upper CNN structure respectively after pre-processed. Then, the outputs from CNN modules are fed together into the prediction layer, which include LSTM layers and fully connected layers. Finally, the proposed wind-PV joint prediction model is verified by simulation experiments.

Evaluation of Renewable Energy Accommodation Capacity Considering Multiple Flexibility Resources of Source-Load-Storage

The utilization of multiple flexibility resources (MFR), such as source-load-storage (SLS-sides), plays a vital role in promoting the integration of large-scale grid-connected renewable energy (RE). Analyzing the renewable energy accommodation capacity (REAC) and assessing the impact of flexibility resources on enhancing REAC is vital for the coordinated development of SLS sides. This study focuses on the technical mechanisms of thermal power (TP) deep peak regulation, load demand response (DR), and energy storage (ES). Considering the operational constraints of these MFR, an evaluation model for REAC is established. The model is a multi-objective optimization framework that encompasses the maximized installed capacity of RE while minimizing the comprehensive expenses of the power system. A hierarchical decoupling solution method is used to solve the evaluation model, obtaining the Pareto optimal solution set for RE installed capacity and power system operating cost. Subsequently, a fuzzy membership function is applied to select a compromise solution, quantitatively characterizing the REAC. The case study results demonstrate that by effectively coordinating the “SLS-sides” MFR, the REAC of the IEEE-RTS96 system can be improved from 1, 758 MW to 2, 731 MW, indicating a remarkable enhancement of 55.35%.

A Joint Clearing Model of Energy-Frequency Modulation Based on Flexible Block Order

The large-scale integration of renewable energy into the grid has led to a gradual diversification of power generation trading units on the power-side, with varying operational characteristics, costs, and trading needs among diverse power generation trading units. Traditional power system clearing models face challenges. Block order is a bidding type that allows for multiple volume–price combinations. At the same time, the randomness and volatility of renewable energy poses challenges to the safe and stable operation of the system. Building a power system clearing model that meets the diverse and flexible needs of the power system has become an important consideration. Therefore, this paper considers the design of three flexible energy blocks: a sustaining block, flux block, and adjustment block to meet the differential needs of diverse trading units and establishes a flexible block order clearing model. Secondly, it establishes a joint clearing model of electrical energy and frequency modulation (FM) to ensure the stable and reliable operation of the system, and solves the model based on relevant constraints to determine the winning electrical energy price and FM price. The CPLEX and Yalmip algorithms are used to solve the model. Finally, a case study was conducted based on an improved IEEE 14-bus system. The results showed that the model proposed in this paper can satisfy the differential needs of diverse trading units, effectively improve the renewable energy consumption capacity, and reduce the prices of the energy market and frequency modulation market. Also, the standard deviation of the net load of the system is relatively low, which improves the reliability of the power system’s operation.

Adaptive H∞ event-triggered load frequency control in islanded microgirds with limited spinning reserve constraints

Using an islanded microgrid (MG) with large-scale integration of renewable energy is the most popular way of solving the reliable power supply problem for remote areas and critical electrical users. However, compared with traditional power systems, the limited spinning reserves and network communication bandwidth may cause weak frequency stability in the presence of stochastic renewable active outputs and load demand fluctuations. In this paper, an adaptive event-triggered control (ETC) strategy for a load frequency control (LFC) system in an islanded MG is proposed. First, a bounded adaptive event-triggered communication scheme is designed. This not only saves on network resources, but also ensures that the control center has a sensitive monitoring ability for the MG operating status when the frequency deviations have been effectively damped. Secondly, by fully considering the spinning reserve constraints and uncertain communication delays, the LFC system is described as a nonlinear model with saturation terms. Design criteria for ETC parameters are strictly deduced based on Lyapunov stability theory. Finally, an ETC parameter optimization algorithm based on random direction search is developed to reconcile the bandwidth occupancy and control performance. The effectiveness of the proposed method is verified in an MG test system.