Net Load Curve Research Articles

Revealing the Role of Redox Reaction Selectivity and Mass Transfer in Current–Voltage Predictions for Ensembles of Photocatalysts

Photocatalysts are conceptually simple reaction units where nanoscale semiconductors integrated with catalysts drive a pair of redox reactions on illumination. However, the proximity of reaction sites performing cathodic and anodic reactions poses dire challenges to realize large light-to-fuel conversion efficiencies. In this study, a powerful, yet straightforward, equivalent-circuit detail-balance modeling framework is developed and applied to evaluate the performance of photocatalytic systems featuring multiple light absorbers. Specifically, low bandgap iridium-doped strontium titanate is modeled a Z-scheme photocatalyst to effect desired hydrogen evolution and iron-based redox shuttle oxidation reactions. Our model has unique capabilities to simulate competing redox reactions and address mass-transfer limitations. In a significant departure from state-of-the-art circuit models, our study develops tools to perform load-line analyses by incorporating a net electrochemical load curve that includes both desired and competing redox reactions. Consequently, reaction selectivity is predicted from equivalent circuit models for photocatalytic and photoelectrochemical systems. Our investigation into ensembles comprised of multiple, semi-transparent light absorbers reveals their potential to outperform a single, optically thick light absorber, particularly when operated under mass-transfer-limited conditions. However, this outcome hinges on minimizing mass-transfer rates of select redox species to prevent undesired reactions of hydrogen oxidation and/or redox shuttle reduction. Our findings demonstrate that reaction selectivity can be achieved by tuning asymmetry in redox species mass-transfer even with perfectly symmetric electrocatalytic charge-transfer coefficients. The influences of various kinetic, mass-transfer, and thermodynamic parameters are explored to offer crucial insights to inform the next-generation of photocatalysts, selective coatings, and reactor designs.

Performance analysis and optimization of multi-energy complementary combined heat and power system considering the influence of time-of-use price

Abstract The multi-energy complementary system can meet the increasingly abundant and diversified energy consumption needs of users and, at the same time, help to improve energy utilization efficiency, reduce environmental pollution, and optimize the balance of energy supply and demand. In this paper, a multi-energy complementary cogeneration system with a gas turbine distributed photovoltaic, gas boiler, and heat storage tank is proposed, and the operation scheduling optimization model of the system is constructed. Aiming to minimize the comprehensive cost of economy and carbon emission, the model adopts a linear programming method to optimize the operation scheduling of multi-energy complementary systems. Taking a multi-energy complementary cogeneration system as an example, the key equipment of the system is optimized under three different scenarios with different time-of-use prices, and the influence of time division of time-of-use electricity price on the comprehensive cost of the system is analyzed. The results show that the time division method, which is more in line with the system net load curve, can effectively reduce the comprehensive cost of the complementary system and increase the consumption space of new energy.

Real-time V2G simulator for grid system frequency and voltage regulation with fault ride-through capabilities

Electric vehicles (EVs) have become one of the promising power grid ancillary services. They can help reduce the intermittent of renewable energy sources by supporting active power and frequency control services. Additionally, bi-directional chargers included into EV batteries can supply/consume reactive power for voltage regulation. To improve voltage quality, a unique control approach is devised to offer fault ride-through. In this study, real-time charging optimization schemes based on particle swarm optimization are developed to coordinate the EVs charging/discharging power with the grid power dispatches depending on the charging priority and preferential electricity prices. Five different car user profiles including different initial state of charge and plug in state have been depicted. Different optimum issues are carried out with comprehensive simulations employing hardware in the loop via real-time simulation for a modified IEEE 14-bus test system. Where different frequency and voltage controls, management strategies, variant fault scenarios, benefits, and challenges for power system regulation with EVs are illustrated. According to the simulation results, the proposed optimized charging strategies reduce the total expenses of EV parking station by 40 % compared to other cases, hence maximizing EV owner satisfaction. The net load curve is smoothed and reduced by 11 %, particularly during peak period.

Power system flexibility analysis using net-load forecasting based on deep learning considering distributed energy sources and electric vehicles

Today, wind and solar energy sources have opened their place in the power system due to their environmental appeal. With the presence of these renewable energy sources (RESs), forecasting net load (NL) and its ramp are of huge importance owing their fundamental role in evaluating and improving the flexibility of the power grid. However, it is challenging to analysis flexibility and NL ramps using NL forecasts due to the uncertainty of RESs. Furthermore, the surge in the number of electric vehicles (EVs) as notable electricity consumers, and how their charging strategy can affect the NL curve are matters of concern. Most NL forecasting and flexibility analyses overlook the concurrent integration of wind and solar sources. Moreover, the impact of controlled EVs charging on grid flexibility remains largely unexplored. Existing studies also fail to assess the proper combination of wind and solar energy sources for future flexibility needs. This paper presents a framework for forecasting NL and detecting ramps with the simultaneous presence of EVs and RESs to investigate the impact of controlled charging of EVs on flexibility. Investigating charging of EVs as a solution is delved deeply in this paper to reduce NL ramps and improve flexibility. The numerical results show that controlled EVs charging may lead to 3% ramp reduction and 52% increment in the system flexibility. This paper also proposes to forecast and find the proper combination of RESs for the next years, leading to a 75% increment in the system flexibility compared to the business as usual RESs penetration.

Day-Ahead and Intraday Two-Stage Optimal Dispatch Considering Joint Peak Shaving of Carbon Capture Power Plants and Virtual Energy Storage

The anti-peaking characteristics of a high proportion of new energy sources intensify the peak shaving pressure on systems. Carbon capture power plants, as low-carbon and flexible resources, could be beneficial in peak shaving applications. This paper explores the role of carbon capture devices in terms of peak shaving, valley filling, and adjustment flexibility and constructs a virtual energy storage model utilizing various flexible loads on the demand side. Also, it proposes a joint peak shaving strategy involving carbon capture devices and virtual energy storage. Considering the predictive error characteristics of wind power and load across different time scales, this study establishes a day-ahead and intraday two-stage rolling regulation peaking model based on carbon capture power plants and virtual energy storage to fully leverage the diverse response speeds and peak shaving capabilities of various types of flexible loads. Initially, the model calculates the net load curve after implementing a demand response system, aiming to minimize the load peak–valley difference. Subsequently, within the intraday period, it seeks to minimize system operating costs by precisely allocating peak shaving resources as per demand, thus aiming for economic efficiency while ensuring the system’s peak shaving capability.

Environmental and economic dispatching strategy for power system with the complementary combination of wind-solar-hydro-thermal-storage multiple sources

The linkage, coordination, and complementary cooperation of energy supply can improve the efficiency of transportation and utilization. At present, the level of new energy consumption needs to be improved, the coordination of the source network load storage link is insufficient, and the insufficient complementarity of various types of power sources in the power system. This article fully explores the differences and complementarities of various types of wind-solar-hydro-thermal-storage power sources, a hierarchical environmental and economic dispatch model for the power system has been established. Among them, the upper level model takes the flexible consumption of new energy as the optimization goal, the middle level model uses a combination of hydropower station and energy storage to minimizes the fluctuation variance and peak-to-valley difference of the net load curve, the lower level model aims to achieve optimal environmental and economic benefits of the power system, comprehensively considers the coal consumption cost, startup and shutdown cost, energy storage operation cost, and pollutant emissions of thermal power units, determines the startup and shutdown mode and output power of thermal power units. Finally, an improved IEEE 6-machine 30-node system is used as an example for simulation analysis, the results show that after applying the proposed hierarchical environmental and economic dispatch strategy of the power system, the fluctuation variance and the peak-to-valley difference of the net load curve have been reduced by 46.3% and 31.5%, respectively, and the environmental and economic benefits of the system is improved by 5.1% compared with the traditional economic dispatch strategy. It can meet the requirements of energy system cleaning and decarbonization while improving the operation economy, which verifies the effectiveness of the proposed environmental economic dispatch model.

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.

Distributed control of energy storages for multi-time-step peak load shaving in a microgrid

Distributed energy storages (ESs) will be widely used in the future smart microgrids due to their continuous technology improvement and cost reduction. Meanwhile, the distributed control method also gains increasing attention for controlling distributed devices to protect user privacy and reduce computation burden compared with the centralized scheme. In this paper, a distributed control method of ESs is proposed for multi-time-step peak load shaving in a microgrid. Considering the ES efficiency is related to its power, an optimization is constructed to minimize the power loss during ES operations when performing peak load shaving function. By analyzing the net load curve and the ES available capacity, the multi-time-step peak load shaving problem is transformed into single-time-step ES operations at each time step. The Combine-Then-Adapt diffusion strategy is united with the consensus + innovation strategy to realize the peak load shaving in a fully distributed way. Case studies verify the efficiency of the proposed method.

Analysis on flexibility resources of Western Inner Mongolia power grid

This paper defines the concept of flexibility in the power system as the ability of the individual components or the system itself to meet the net load changes and respond to power regulation demands. It then reviews the existing methods for evaluating flexibility and their classification, identifies the advantages and disadvantages of these methods, and provides a mathematical model for the flexible operation of the Western Inner Mongolia power grid. Based on the data provided by production units, the paper summarizes the output characteristics of various flexibility resources in the Western Inner Mongolia power grid, including the output curves of typical photovoltaic power stations, typical wind power plants, the overall load curve, typical regional load curves, and power transfer curves of three interconnecting busbars. It also studies the potential regulation capabilities of new energy sources, loads, and interconnecting lines. Finally, the paper evaluates the flexibility level of the Western Inner Mongolia power grid by drawing the net load curve, points out the shortcomings in flexibility in the construction of the new power system in the Western Inner Mongolia region, and provides corresponding suggestions.

Multi-scale flexibility assessment of electrical, thermal and gas multi-energy systems based on morphological decomposition

The rapid development of renewable energy has also had an impact on the flexibility of multi energy systems such as electricity, heat, and gas. To analyze the flexible characteristics of multi energy systems at multiple time scales, a multi-scale flexibility evaluation method based on morphological decomposition is proposed. The net load curve is decomposed using mathematical morphology methods, and a multi-scale energy storage configuration method based on the flexibility of electric heating systems is proposed. The analysis data shows that the probability of insufficient upward flexibility, margin expectation, and insufficient expectation of the scale weighted flexibility index are 1.12%, 3.98%, and 1.16%, respectively, while the probability of insufficient downward flexibility, margin expectation, and insufficient expectation are 0.73%, 4.54%, and 0.56%, respectively. The introduction of energy storage and controllable load simultaneously results in an overall downward flexibility index of 0.92% for the system. The results indicate that controllable load can improve the economy of system peak shaving, providing more options for energy storage and configuration in multi energy systems.

Risk-limiting dispatching strategy considering demand response in multi-energy microgrids

Multi-energy microgrids deploy distributed power sources with flexible generation characteristics to meet the changing needs of users. Because they comprise renewable as well as traditional energy sources, they can play an important role in the transition to clean and efficient low-carbon power generation. However, both the complexity of coordinating different power sources and random fluctuations of renewable-energy generation capacity pose significant challenges. To solve these problems, a risk-limiting dispatching strategy for multi-energy microgrids is proposed that considers net load-demand response. First, a bi-directional demand-response mechanism is introduced, and a flexible load is used as a dispatchable resource to optimize the net load curve of the system. Second, entropy-based reliability modeling is conducted to address the multiple uncertainties in system operation. Third, a risk-limiting dispatching model of the multi-energy microgrid is established that considers three optimization objectives: economy, environmental protection, and reliability. Finally, the NSGA-III algorithm is used to obtain the set of optimal solutions for the model, and a compromise solution that meets the system operation requirements is selected based on the technique for order preference by similarity to an ideal solution (TOPSIS) with a criteria importance though intercriteria correlation (CRITIC) and analytic hierarchy process (AHP, CRITIC–AHP) hybrid assignment. The simulation results show that the proposed optimal scheduling strategy improves the operational reliability index while ensuring system economy and environmental protection. This study provides a new approach for controlling system risk and ensuring continuous energy-supply capability under uncertain conditions.

Multi‐time scale adequacy evaluation of the power system with high penetration of renewable energy sources based on empirical mode decomposition

AbstractAdequacy, especially in the power system with high penetration of renewable energy sources, is a crucial consideration in power system planning and operational dispatching. A multi‐time scale adequacy evaluation method based on empirical mode decomposition (EMD) is proposed, taking into account the multi‐time scale characteristics of renewable energy sources and the response characteristics of flexible regulation resources. In this method, a net load curve is decomposed into several component curves by the EMD algorithm. Then, the adequacy demand on each time scale is obtained by waveform recognition. Furthermore, the available adequacy resources on different time scales are quantified according to the regulation models of flexible resources. By analysing the adequacy demand and available adequacy resources of the power system on the same time scale, the adequacy evaluation indices on each time scale can be calculated and weighted to form the comprehensive indices. Finally, the adequacy evaluation indices on each time scale with different capacities of renewable energy sources and energy storage systems are compared and analysed using a practical power system as a case. Simulation results show the validity of the multi‐time scale adequacy evaluation method.

A representative day selection method based on forward–backward sweep in generation expansion planning

Due to computational limitations, generation expansion planning often selects representative days to ensure computational efficiency. The optimality of the generation expansion planning solution greatly depends on the accuracy of the selected representative days. At present, the selection of representative days mostly adopts historical data. This method is essentially to separate the selection of representative days from the planning. We get the planning results according to the selected representative days, and then operate the simulation under the representative days. This method is feasible without considering the renewable energy expansion planning, because the net load curve does not change before and after the planning. However, in the context of the increasing penetration of renewable energy, this method of selecting representative days cannot represent the actual operating situation of the system. The net load data before and after the planning changes, and the representative days before the planning cannot reflect the actual running situation after the planning. Based on the above questions, this paper has proposed a representative day selection method based on forward–backward sweep that provides three advantages: the target level of net-load curve is captured, the representative days can reflect the future situation in reality, and a linear relaxation method to enhance computational effectiveness is designed. This method can effectively reduce the error of traditional methods and the effectiveness has been demonstrated in XJTU-ROTS test system.

Flexibility Demand Calculation Model Based on Net Load Decomposition with CEEMDAN Algorithm

The high proportion of renewable energy connected to the grid will propose higher flexibility requirements for the power system. Since flexible resources have different adjustment rates, it is urgent to establish a method for calculating flexibility demands at different time scales. Therefore, this paper proposes a flexibility demand calculation model based on the net load decomposition using the CEEMDAN algorithm. First, the CEEMDAN algorithm and IIR filter are used to decompose the net load curve to obtain the fluctuating components of different frequencies, and then the flexibility demands at different time scales are calculated according to these fluctuating components.

Curve and double-layer economic dispatching considering reasonable wind abandonment under different time granularities

In view of the large output of wind power during the load trough time, the peak regulation cost may increase sharply, and the traditional hourly dispatch may not be able to accurately track the load fluctuation due to the fluctuation of renewable energy. In this paper, based on different time granularities, an adaptive segmented double-layer economic scheduling model of the net load curve considering reasonable wind abandonment is constructed. The model can better cope with net load changes while reducing the load peak-to-valley difference. First, a reasonable wind abandonment model is established under different time granularities of 5 min, 15 min, 30 min, and 1 h; Then, based on the static thinking of the net load curve by time period, the load change in the hourly scale is fully considered without changing the total number of dispatching periods so that each dispatching period can adaptively select the duration according to the change of net load gradient, and a self-adaptive subsection model of the net load curve is established to minimize the total running cost. Finally, taking IEEE-30 nodes as the example system, the NSGA-II algorithm and CPLEX solver are used to solve the model. The results verify the economy and feasibility of the proposed model.

Highly accurate peak and valley prediction short-term net load forecasting approach based on decomposition for power systems with high PV penetration

The increasing penetration of photovoltaic has been reshaping the electricity net load curve, which has a significant impact on power system operation and short-term dispatch scheduling. Accurate short-term net load forecasting is essential to ensure reliable and economical operations of a power system. Nonetheless, most of the existing net load forecasting approaches are mostly focused on net load forecasting at household, distribution or microgrid level, but not at grid system-wide level. They also suffer from low accuracy due to the presence of uncertainties on high-frequency fluctuations in the net load. This paper proposes a new improved two-stage net load forecasting method at grid system-wide level. Firstly, it contributes to eliminate the high-frequency components with insignificant amount of energy from the original net load by using the “Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-ICEEMDAN” technique. Then, net load decomposition outcomes form the inputs of a computationally efficient and accurate “Long Short-Term Memory-LSTM” network algorithm to produce an accurate day-ahead forecasting, which lays out the foundation of day-ahead power dispatch scheduling. The superiority of the suggested algorithm was confirmed by comparing the obtained results against five other algorithms that use different empirical based decomposition techniques along with Back Propagation (BP) or LSTM. Statistical metrics, Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were computed to show the model accuracy. The validity of the proposed method is verified by the net load data of “South West Interconnected System” power network in Western Australia, which refers to the total demand on the conventional generators made up of the consumers’ actual demand plus system losses, minus the solar power harnessed by the rooftop PV panels installed within the grid system. Achieving a very high day-ahead net load forecasting accuracy of 96.67% confirms our hypothesis on ICEEMDAN’s capability to decompose the net load carefully into different meaningful components.

A demand side response scheme for enhancing power system security in the presence of wind power

One of the major concerns associated with the wind power integration is its anti-peaking characteristic which increases the required ramping of the conventional generators and potentially threatens power system security. Recently, demand side response has become one of the likely successful solutions that can be employed in overcoming the negative impact of the anti-peaking characteristic of the daily net load curve. This paper presents a demand side response framework in which the load shifting strategy is effectively applied to improve the daily load profile of the system in the presence of wind power. The size of the load to be shifted is determined such that the system security in terms of voltage magnitudes and voltage stability margins is preserved for the normal state and prospective emergency states. To stimulate the demand side response candidates to shift their load and support the utilities to realize a particular daily load shape, an incentive reimbursement pattern based on the participants’ submitted sizes has been proposed. The mathematical formulation of the problem is stated as a nonlinear mixed integer programming problem. The problem is solved using Benders decomposition technique where the intact problem is split into a master problem and several smaller nonlinear problems solved individually by the conventional methods. The performance of proposed approach has been evaluated by its application on the IEEE 57- and 118-bus systems.

Collaborative optimal dispatch of microgrid and electric vehicles based on the Stackelberg game

The development of both microgrids and electric vehicles has become an important part of the current energy scenario. Useful complementary advantages can be formed between electric vehicles and microgrids, the consumers of which can utilize renewable energy and narrow the peak–valley differences of the net load curve while ensuring their own pecuniary interests. Based on the idea of the Stackelberg game, an optimal dispatch model of a microgrid with electric vehicles is proposed herein, where the benefits of the state of charge are taken into account. In the upper layer of the model, the charging and discharging behaviors of electric vehicles are guided by the goal of minimizing the operating cost of the microgrid. In the lower layer of the model, electric vehicle users adjust the charging and discharging strategies with the goal of maximizing their individual interests. The study results demonstrate that the proposed model not only reflects the benefits of both the master and slave but also reduces the peak–valley differences of the microgrid load. Further, the charging and discharging times of electric vehicles are reduced, and their state of charge is maintained at a high level.

Research on the Internal Flow Characteristics of Pump Turbines for Smoothing the Output Fluctuation of the Wind–Photovoltaic Complementary System

Wind and photovoltaic (PV) power generation and other distributed energy sources are developing rapidly. But due to the influence of the environment and climate, the output is very unstable, which affects the power quality and power system stability. Pumped hydroelectric energy storage (PHES) systems are suitable as peaking power sources for wind and photovoltaic (Wind–PV) complementary systems because of their fast start–stop and long life. The mathematical models and operational characteristics of the three subsystems in the wind–PV–PHES complementary system are analyzed to improve the generation efficiency and access capacity of wind and PV power. The peaking characteristics of the PHES system are used to balance the maximum benefit and minimum output fluctuation of the wind–PV complementary system. The stable operation of the pump turbine is an important guarantee for the smooth output of the wind–PV complementary system. Three operating points are selected from the net load curve and converted to the pump turbine model parameters. The internal flow characteristics and laws of the pump turbine under different guide vane opening conditions are summarized through the analysis of the computational fluid dynamics (CFD) numerical simulation post-processing results. The study shows that the output of wind and PV power generation varies with the changes in wind speed and solar radiation, respectively. The output of the wind–PV complementary system still has large fluctuations, and the PHES system can effectively suppress the power fluctuation of the wind–PV complementary system and reduce the abandoned wind and light rate. CFD technology can accurately and efficiently characterize the internal flow characteristics of the pump turbine, which provides a basis for the design, optimization, and transformation of the pump turbine.

Detection and estimation of behind-the-meter photovoltaic generation based on smart meter data analytics

With the economic development and the implementation of national policies, the penetration rate of distributed photovoltaic (PV) panels, especially those behind the meter, has been greatly increased at the customers' side, which has been changing the operational landscape of the distribution network. In that regard, a critical challenge is that the PV generation behind the meter cannot be detected and measured directly by the utility company. There are even cases where customers install PV without authorization. To ensure the secure and reliable operation, the utility company has to perceive the total PV generation from smart meter data while making operational decisions to strike a balance of power supply and demand. This paper presents a novel two-stage PV detection and capacity estimation method purely based on smart meter data, which is essentially a data-driven model based on machine learning. First, given the net load curves of customers, the PV capacity features are extracted and the support vector machine (SVM) model is used to classify the customers with or without realistic PV installations. Second, based on the spatial correlation of original power demand, the total PV capacity curve is estimated by using the difference between daytime and nighttime data based on the aggregated load information. Finally, the capacity curve, along with the net load curve, is fed into Long-Short Term Memory (LSTM) model as input to estimate the real-time PV generation of individual customers.