Power Generation Curve Research Articles

Feature Extraction Approach for Distributed Wind Power Generation Based on Power System Flexibility Planning Analysis

This study addresses the integral role of typical wind power generation curves in the analysis of power system flexibility planning. A novel method is introduced for extracting these curves, integrating an enhanced K-means clustering algorithm with advanced optimization techniques. The process commences with thorough data cleaning, filtering, and smoothing. Subsequently, the refined K-means algorithm, augmented by the Pearson correlation coefficient and a greedy algorithm, clusters the wind power curves. The optimal number of clusters is ascertained through the silhouette coefficient. The final stage employs particle swarm and whale optimization algorithms for the extraction of quintessential wind power output curves, essential for flexibility planning in power systems. This methodology is validated through a case study involving wind power output data from a new energy-rich provincial power grid in North China, spanning from 1 January 2019, to 31 December 2022. The resultant curves proficiently mirror wind power fluctuations, thereby laying a foundational framework for power system flexibility planning analysis.

Research on Operation and Maintenance Strategy of New Energy Photovoltaic Power Generation System Considering Extreme Weather Conditions

Abstract This paper employs the gray correlation analysis method to compute the correlation between irradiance, ambient temperature, wind speed, and photovoltaic power generation efficiency. This method relies on screened data for data normalization, and after processing, it determines the degree of similarity between the factors. We propose a dynamic combined maintenance strategy for PV power generation systems that considers reliability, overhaul resources, and overhaul time constraints. We propose a two-layer optimization strategy based on extreme weather factors and failure-related characteristics, and we use empirical analysis to investigate the maintenance strategy’s effectiveness. The analysis reveals a positive correlation between irradiance and PV power generation efficiency, with a multiplicative increase in the range of 1-3. In the correlation analysis of ambient temperature and wind speed variables, the power generation curve is not consistent with its trend, which is related to the influence of extreme temperature and extreme wind speed, and the meteorological factors affecting the PV power generation efficiency change at different moments of the day, where the correlation coefficient at 6:45 is 0.912. When the failure correlation strength increases to a value of 0.1, the number of repairs rises to 8. When the weather accessibility ratio is 0.2, the reduction in system availability is up to 62%. The two-layer optimization strategy, which considers extreme weather factors and failure-related characteristics, is both economical and universal.

DESIGN METHODOLOGY OF A HYBRID RENEWABLE ENERGY MICROGRID FOR AN ISOLATED RURAL COMMUNITY. APPLICATION CASE IN ZAMBIA

This article proposes a methodology for the design of off-grid electricity systems based on renewable energies in rural of developing countries communities that never consumed electricity. The state of the art is reviewed and the methodology aggregates proposals from multiple previous works. It is divided into phases: Data Collection, Assessment, Planning/Modelling and Execution. The main actors and their responsibilities are explained. Rarely considered stages such as co-design with the beneficiary community or the creation of a renewable energy community are included. Their necessity for the implementation and success of these projects with social, economic and environmental objectives is discussed. The application of the methodology is illustrated with first results from a case study in Mumbeji, Zambia, Africa. The project stakeholders, the results of demand curve estimation, power generation curves, available resources, engineering costs and the modelling of the microgrid design are presented. Keywords: Hybrid renewable energy systems; Rural electrification; Off-grid rural communities, Energy simulation.

Reliability Analysis of Power System Considering Flexibility Resources

Flexible resources are a critical resource in ensuring the security and steady running of a large percentage of electricity systems that use new sources of energy. The power system’s flexible resources participate in power regulation, improving the power system capability to absorb new energy and also the electricity system’s reliability. This paper calculates the dependability index of electricity system using the basic calculation method of the equivalent energy function. By flexibly handling the new power generation curve’s adjustable capacity and flexible resources, new energy generation and flexible resources count toward equivalent energy calculation, and the dependability index of the electricity system is calculated. The efficacy of this approach is illustrated by an example.

Mathematical modeling of a submerged piezoelectric wave energy converter device installed over an undulated seabed

A piezoelectric wave energy converter (PWEC) device integrated with an impermeable breakwater placed over an undulated seabed is considered. The PWEC device is composed of a single submerged flexible plate with piezoelectric layers attached to both faces of the flexible plate. Due to the piezoelectric effect, this piezoelectric plate generates electricity when excited by the incident waves. A detailed analysis is done to investigate the effect of PWEC plate submergence depth, plate length, plate edge conditions, incident wave period, bottom ripple amplitude, and ripple number on the power generation by the PWEC device. It is seen that the PWEC device edge conditions, submergence depth, and plate length play a significant role in the resonating pattern associated with the wave power generation curve. The results demonstrate that the PWEC device having moderate plate length and with free and moored type front edges generates a higher amount of wave power for a wider range of incident wave frequencies.

WindGMMN: Scenario Forecasting for Wind Power Using Generative Moment Matching Networks

With the increasing penetration of wind power generation, the fluctuating and intermittent behavior of wind power poses huge challenges to the operation and planning of distribution networks. A popular way to mitigate these challenges is to provide a group of possible wind power forecasting scenarios instead of depending on deterministic point forecasting values, so that system operators can consider the uncertainties. This letter proposes a novel WindGMMN method for wind power scenario forecasting, in which necessary modifications are made on the generative moment matching network (GMMN), and an optimization strategy is designed to find a series of wind power scenarios with similar shapes, probability distributions, and temporal correlations as potential scenarios. Simulations and analyses were performed on a public dataset with 2190 wind power generation curves and their corresponding meteorological features. The results show that the proposed WindGMMN outperforms popular baselines (e.g., variational auto-encoders and generative adversarial networks) for scenario forecasting of wind power, without any restrictions on the time horizon (e.g., times ranging from 10 min to 24 h).

Sizing and Sitting of Battery Energy Storage Systems in Distribution Networks with Transient Stability Consideration

In this study, the capacity and location of battery energy storage systems (BESSs) in a distribution network were evaluated to increase the stability and reliability of power systems by applying the proposed transient stability indicators. The search capability of particle swarm optimization (PSO) combined with Pareto optimality in MATLAB R2019b was used to solve multi-objective problems in steady-state conditions, including active power loss, voltage deviation, and total operating cost. The BESS capacity and location were then derived using the Manhattan distance method. Subsequently, the BESS control model was constructed using the commercial software DIgSILENT PowerFactory 2021 to evaluate generator power smoothing and short-term voltage stability index during the transient response and determine the final capacity and location of BESS in distribution networks. Finally, the accuracy of the proposed method was verified by considering the daily load and solar power generation curves using the IEEE 33-bus radial power distribution network.

Short-term power prediction for renewable energy using hybrid graph convolutional network and long short-term memory approach

Accurate short-term solar and wind power predictions play an important role in the planning and operation of power systems. However, the short-term power prediction of renewable energy has always been considered a complex regression problem, owing to the fluctuation and intermittence of output powers and the law of dynamic change with time due to local weather conditions, i.e. spatio-temporal correlation. To capture the spatio-temporal features simultaneously, this paper proposes a new graph neural network-based short-term power forecasting approach, which combines the graph convolutional network (GCN) and long short-term memory (LSTM). Specifically, the GCN is employed to learn complex spatial correlations between adjacent renewable energies, and the LSTM is used to learn dynamic changes of power generation curves. The simulation results show that the proposed hybrid approach can model the spatio-temporal correlation of renewable energies, and its performance outperforms popular baselines on real-world datasets.

Research on Optimum Ratio of New Energy Installed in Shandong Power Grid

Abstract In recent years, we have witnessed the rapid growth of new energy sources in China, with the implementation of “carbon peak, carbon neutrality” and new power system, there is no doubt that the development of new energy will maintain a rapid growth rate. At the same time, in order to ensure the high-quality development of new energy, the planning of wind power and photovoltaic should be taken in advance. Taking Shandong Power Grid as an example, this paper analysed the optimal installed ratio of wind power and photovoltaic power in Shandong power grid from the perspective of electrical power balance, aiming at the highest fit between wind and photovoltaic power generation and load curve. The calculation results show that when the installed ratio of wind power and photovoltaic is 1.45:0.8 (about 1.8:1), the wind and photovoltaic power generation can fit the load curve most closely. Based on this, some suggestions are put forward to provide technical support for energy and electric power planning.

Scenario Generations for Renewable Energy Sources and Loads Based on Implicit Maximum Likelihood Estimations

Scenario generations for renewable energy sources and loads play an important role in the stable operation and risk assessment of integrated energy systems. This paper proposes a deep generative network based method to model time-series curves, e.g., power generation curves and load curves, of renewable energy sources and loads based on implicit maximum likelihood estimations (IMLEs), which can generate realistic scenarios with similar patterns as real ones. After training the model, any number of new scenarios can be obtained by simply inputting Gaussian noises into the data generator of IMLEs. The proposed approach does not require any model assumptions or prior knowledge of the form in the likelihood function being made during the training process, which leads to stronger applicability than explicit density model based methods. The extensive experiments show that the IMLEs accurately capture the complex shapes, frequency-domain characteristics, probability distributions, and correlations of renewable energy sources and loads. Moreover, the proposed approach can be easily generalized to scenario generation tasks of various renewable energy sources and loads by fine-tuning parameters and structures.

Probabilistic Assessment of PV Hosting Capacity Under Coordinated Voltage Regulation in Unbalanced Active Distribution Networks

The increasing penetration of photovoltaic (PV) generators has led to a shift of the operational policy of the distribution system operator (DSO) from passive to active intervention in distribution networks (DNs), where a centralized controller governs the operation of voltage regulation devices. However, the PV output uncertainty hinders the verification of the impact of PVs on DNs. Therefore, the hosting capacity (HC) analysis framework for PV penetration should reflect both the operational benefit of the DSO and the PV output uncertainty. Thus, in this study, a two-stage optimization-based framework is proposed to analyze the probabilistic HC for PV under active network management (ANM) of DNs. In the first stage, the optimal PV base capacity (PVBC) to maximize the sum of the HC for PVs is determined based on a heuristic optimization method; in the second stage, using the predefined load and PV output profile, the maximum available power generation curve for PVBC is calculated, and the maximum HC for PV is derived by the calibration of PVBC through a comparison with the actual PV generation profile curve. The proposed method considers the time-series-based load flow results, reflecting the time-scheduling strategy by the DSO. Moreover, the uncertain characteristics of PV output are stochastically considered using a Monte Carlo simulation-based repetitive calculation approach. Case studies were implemented using the modified IEEE-123 test system, and the simulation results provided a quantitative comparison of the effect of the probabilistic HC improvement on the utilization of controllable resources and the centralized ANM by the DSO.

Analysis of energy storage operation and configuration of high proportion wind power system

Driven by the goal of "carbon neutrality", the future power system will be a high proportion of renewable energy power system. This paper takes a high proportion of wind power system as an example to explore the influence of "supply side" low-carbon transition on the economy and reliability of power system operation.In this paper, a nonlinear model can be established based on the need of investment cost and operation and maintenance cost to the daily total output value of total load power and energy storage cost, so as to obtain the minimum daily output data of three units, and draw the daily power generation curve of the unit according to the relationship between the three units and the lowest cost.Secondly, the balance of the system power is analyzed and the cost of wind power is calculated.

Optimal Investments in PV Sources for Grid-Connected Distribution Networks: An Application of the Discrete–Continuous Genetic Algorithm

The problem of the optimal siting and sizing of photovoltaic (PV) sources in grid connected distribution networks is addressed in this study with a master–slave optimization approach. In the master optimization stage, a discrete–continuous version of the Chu and Beasley genetic algorithm (DCCBGA) is employed, which defines the optimal locations and sizes for the PV sources. In the slave stage, the successive approximation method is used to evaluate the fitness function value for each individual provided by the master stage. The objective function simultaneously minimizes the energy purchasing costs in the substation bus, and the investment and operating costs for PV sources for a planning period of 20 years. The numerical results of the IEEE 33-bus and 69-bus systems demonstrate that with the proposed optimization methodology, it is possible to eliminate about 27% of the annual operation costs in both systems with optimal locations for the three PV sources. After 100 consecutive evaluations of the DCCBGA, it was observed that 44% of the solutions found by the IEEE 33-bus system were better than those found by the BONMIN solver in the General Algebraic Modeling System (GAMS optimization package). In the case of the IEEE 69-bus system, the DCCBGA ensured, with 55% probability, that solutions with better objective function values than the mean solution value of the GAMS were found. Power generation curves for the slack source confirmed that the optimal siting and sizing of PV sources create the duck curve for the power required to the main grid; in addition, the voltage profile curves for both systems show that voltage regulation was always maintained between ±10% in all the time periods under analysis. All the numerical validations were carried out in the MATLAB programming environment with the GAMS optimization package.

Power Generation Curve Clearing Algorithm based on High-Low Match Auction Mechanism

In the era of power production with coal and gas as the main fuel, the output value of the power system is easy to control and adjust. Therefore, the traditional power trading only focuses on the research of transaction method of electricity quantity. After the addition of clean energy, the output value of power system fluctuates greatly and is difficult to control. At this time, the trading mechanism which only takes electricity as the main factor is no longer applicable. It is necessary to consider adding the fluctuation factors of electricity into the trading process, and use the power of the market to change the purchase decision and electricity consumption habits of users, so that the fluctuation of electricity use curve is consistent with that of power generation curve. Based on the high-low match auction mechanism, this paper designs a clearing rule considering the fluctuation of generation curve, and gives an example to verify it.

Analysis and application of the relationship between wind power curve and power generation based on operating data

Wind power curve has a certain promotion effect on increasing the power generation. Firstly, this paper analyzes various factors that affect the power curve of a wind turbine, and establishes a mathematical model of the power curve. Secondly, the relationship between wind power generation and power curve is analyzed, and the calculation formula of annual power generation is proposed. Finally, based on the data of an actual operating wind farm, a statistical analysis of annual power generation is carried out, and the power curves of two typical units is compared to analyze the causes. Through the analysis of examples, the effectiveness of the proposed method is verified, and it can provide a reference for improving the operation level of wind turbines.

Research on optimal allocation strategy of multiple energy storage in regional integrated energy system based on operation benefit increment

Wind power generation curve generally has the reverse characteristic with the peak regulation of electricity load curve, which will lead to serious wind abandonment and energy waste. The development of integrated energy systems and energy storage maybe change this predicament. Based on the infrastructure and operation model of regional integrated energy system, this paper studied the allocation priority of cooling, heating, electricity storage and electricity to gas (P2G) system, considering multiple energy complementary and collaborative operation of combined cooling heating and power (CCHP) units and other equipment. Firstly, the operation benefit increment index was proposed to quantify the operation cost variation, also named promotion effect in this paper, between the scenarios of integrated energy system without/with certain form energy storage added. Then, an configuration optimization model of multiple energy storage devices based on operation benefit increment was put forward. By employing an industrial district integrated energy system in northern China as the study case, the operation scheduling scheme and the priority of energy storage configuration were solved by the optimization strategy proposed in this paper. And the promotion effects of different energy storage configurations were analyzed from the viewpoint of operation economy of the integrated energy system. The results show that the abandoned wind power and the cost of environmental pollution control are reduced in the integrated energy system with four kinds of energy storage. And the economic efficiency is greatly improved.

Numerical Research and Open Sea Tests of a 100 kW Modified Edinburgh Duck Wave Energy Convertor

This paper presents a modified wave energy device. The wave energy device retains the primitive capture shape of the famous Edinburgh Duck, but uses a new type of underwater-stabilized substrate. The hydrodynamic calculation with frequency-domain models was performed to find out the optimum wave period for the wave energy converter. The results to be presented were obtained by using the Boundary Element Method (BEM), based on linear wave theory. The characteristic function expands of velocity potential in the cylindrical coordinate system was applied to limit computational domain and improve computational speed. The hydrodynamic performance of the device was assessed. The influence of hydraulic damping coefficient was evaluated. Focus was given to the capture width ratio, motion of the device and optimal hydraulic damping coefficient. After two years of construction in the shipyard, the device was constructed and an open sea test had been finished near WanShan Island, South China Sea. During the test, the daily power generation and power curve of the device were measured. In this test, amounts of test data, experience and lessons had been achieved and will be summed up and presented in the paper.

Opportunities for power-to-Gas and Power-to-liquid in CO2-reduced energy scenarios: The Italian case

Integration of renewable energy in the electricity market poses significant challenges on power grid management due to the volatility of these sources. In fact, the mismatch between renewable power generation and load curves, along with the need for grid stability, may lead to substantial curtailments when potential electricity supply exceeds demand. In this respect, the surplus from renewable energies can be conveniently exploited to produce hydrogen via electrolysis. This concept can be referred to as “Power-to-Gas” and “Power-to-Liquid” when synthetic grid gas and liquid fuels are respectively produced via syngas hydrogenation processes and is rapidly emerging as a promising measure in support of renewable energy penetration, leading to the decarbonisation of energy generation without affecting grid reliability. This study evaluates the impact of Power-to-Gas and Power-to-Liquid systems on future CO2-reduced scenarios, characterised by increasing shares of renewable energies and electric vehicles under a holistic Smart Energy System perspective. Results show potential synergies among crucial energy sectors in terms of CO2 emissions, curtailments and costs. Among the proposed options, synthetic grid gas produced by biomass gasification, and subsequent hydrogenation, leads to the best techno-economic scenario with a reduction of CO2 emission of 30% with negligible change in yearly total costs.

Feasibility study on a vehicular thermoelectric generator for both waste heat recovery and engine oil warm-up

The thermoelectric modules have the bidirectional characteristic, which can work either in power generation mode based on Seebeck effect or in heating-cooling mode based on Peltier effect. In this paper, a feasibility study is performed for a bifunctional thermoelectric generator applied in vehicle engines, which can use Seebeck effect for waste heat recovery and Peltier effect for engine warm-up. To predict the bidirectional behaviours of the thermoelectric module, a dual-mode thermoelectric module model is developed, and the simulation results for the two modes (power generation mode and heating-cooling mode) are both verified with experiments. The four-quadrant operation diagram of thermoelectric module, which clearly presents the cooling, heating and power generation curves, is produced based on the validated model.A case study of applying a bifunctional thermoelectric generator to a 2 l-diesel engine passenger car is then carried out based on a further extended bifunctional TEG model and an engine oil and coolant circuit model. The simulation results shows that the optimal engine oil temperature (100 °C) cannot be reached in the new European driving cycle for the baseline engine without thermoelectric generator, but it can be reached by both applying thermoelectric generator operating in waste heat recovery mode and engine warm-up mode. Compared with the thermoelectric generator only operating in waste heat recovery mode, a 3-min faster warm-up effect of engine oil can be obtained when the bifunctional thermoelectric generator works in engine warm-up mode with electrical current applied. It also finds out that the electric power generated in waste heat recovery mode at vehicle starting and low vehicle speed phrase is limited. However, a faster warm-up effect (engine oil temperature rising 7 °C in the first 100 s) can be obtained by operating thermoelectric generator in engine warm-up mode at vehicle staring. The electric energy used in engine warm-up mode can be regenerated in the later waste heat recovery mode. To compensate all the used electric energy in engine warm-up mode, longer time of thermoelectric generator operating in waste heat recovery mode is needed.

Optimized Operation of Hybrid System Integrated With MHP, PV and PHS Considering Generation/Load Similarity

Renewable energy is widely used as a clean energy in the world, yet the intermittent of the power generation from renewable energy power plants, resulting in poor quality of power supply. However, the hybrid energy system and some energy storage devices can be installed to mitigate the power fluctuation, to achieve power smoothing and maximize the profit, a double-objective optimization model of hybrid system integrated with micro-hydro power (MHP), photovoltaic (PV), and pumped hydro storage (PHS) is established in this study. The day-ahead optimized operation strategy of the PHS based-on hour-level data of seven days is obtained, which aims to maximize the similarity value (SIM) between the power generation curve and load profile, and the economic revenue (ER) of the PHS. Optimized schedule is obtained by means of chance-constrained programming (CCP) and modified multi-objective particle swarm optimization (MOPSO). The performance of the model is evaluated by using the meteorological, the historical MHP power generation and the load data obtained in Xiaojin County, Sichuan province, China. The simulation results reveal that the best trade-off value between SIM and ER can be obtained on the Pareto front with comprehensive consideration of the system operation strategy and target. Furthermore, the generation/load similarity can be increased 7.89% under the participating of the PHS.