Peak Shaving Strategy of Concentrating Solar Power Generation Based on Multi-Time-Scale and Considering Demand Response

According to the multi-time-scale characteristics of power generation and demand-side response(DR)resources,as well as the improvement of prediction accuracy along with the approaching operating point,a rolling peak shaving optimization model consisting of three different time scales has been proposed.The proposed peak shaving optimization model considers not only the generation resources of two different response speeds but also the two different DR resources and determines each unit combination,generation power,and demand response strategy on different time scales so as to participate in the peaking of the power system by taking full advantage of the fast response characteristics of the concentrating solar power(CSP).At the same time,in order to improve the accuracy of the scheduling results,the combination of the day-ahead peak shaving phase with scenario-based stochastic programming can further reduce the influence of wind power prediction errors on scheduling results.The testing results have shown that by optimizing the allocation of scheduling resources in each phase,it can effectively reduce the number of starts and stops of thermal power units and improve the economic efficiency of system operation.The spinning reserve capacity is reduced,and the effectiveness of the peak shaving strategy is verified.

Combined hybrid energy storage system and transmission grid model for peak shaving based on time series operation simulation

This study proposes a combined hybrid energy storage system(HESS) and transmission grid(TG) model, and a corresponding time series operation simulation(TSOS) model is established to relieve the peak-shaving pressure of power systems under the integration of renewable energy. First, a linear model for the optimal operation of the HESS is established, which considers the different power-efficiency characteristics of the pumped storage system, electrochemical storage system, and a new type of liquid compressed air energy storage. Second, a TSOS simulation model for peak shaving is built to maximize the power entering the grid from the wind farms and HESS. Based on the proposed model, this study considers the transmission capacity of a TG. By adding the power-flow constraints of the TG, a TSOS-based HESS and TG combination model for peak shaving is established. Finally, the improved IEEE-39 and IEEE-118 bus systems were considered as examples to verify the effectiveness and feasibility of the proposed model.

Peak shaving operation optimization of high proportion new energypower generation considering wind-solar complementationand source-load coupling

To optimize peaking operation when high proportion new energy accesses to power grid,evaluation indexes are proposed which simultaneously consider wind-solar complementation and source-load coupling.A typical wind-solar power output scene model based on peaking demand is established which has anti-peaking characteristic.This model uses balancing scenes and key scenes with probability distribution based on improved Latin hypercube sampling(LHS)algorithm and scene reduction technology to illustrate the influence of wind-solar on peaking demand.Based on this,a peak shaving operation optimization model of high proportion new energy power generation is established.The various operating indexes after optimization in multi-scene peaking are calculated,and the ability of power grid peaking operation is compared whth that considering wind-solar complementation and source-load coupling.Finally,a case of high proportion new energy verifies the feasibility and validity of the proposed operation strategy.

Collaborative optimization model of renewable energy development considering peak shaving costs of various flexibility resources

China has set carbon emission goals for 2030 and 2060.Renewable energy sources,primarily wind and photovoltaic power,are being considered as the future of power generation.The major limitation to the development of new energies is the limited flexibility of regulations on power system resources,resulting in insufficient consumption capacity.Thus,the flexible resource costs for peak shaving as well as the reasonable coordinated development and operation optimization of regional renewable energy need to be considered.In this study,a renewable energy development layout configuration analysis method was established by considering the composite cost of a power system,comprehensively analyzing the potential of various flexibility regulation resources for the power system and its composite peak shaving cost,and combining renewable energy output characteristics,load forecasting,grid development,and other factors.For the optimization of various flexible resource utilization methods,a peak shaving cost estimation method from the perspective of the entire power system was established by combining the on-grid electricity prices and operating costs of different power sources.A collaborative optimization model of power system operation that aims at the lowest peak shaving cost and satisfies the constraints of operation,safety,and environmental protection was proposed.Finally,a certain area of Gansu Province was used as an example to perform detailed analysis and calculation,which demonstrated that the model has an optimal effect.This model can provide an analysis method for regional renewable energy development layout configurations and system optimization operations.

Tube-Wall Temperature Prediction ofa 660-MW Ultra-Supercritical Double-Reheat Boiler under Flexible Peak Shaving

Ultra-supercritical double-reheat boilers employ multiple temperature-regulating methods during flexible peak shaving.This exposes the boiler to significant tube-wall temperature deviations and overtemperatures.In this study,a compartment model was developed to subdivide the overlapping high-temperature heating surfaces of a 660-MW double-reheat boiler into smaller compartments.The model was verified using field experimental data and was applied to calculate the enthalpy and tube-wall temperature distributions under different loads.In addition,the effects of three temperature-regulating methods(swinging burners,regulating dampers,and flue gas recirculation)on the variation tendency and deviations of the tube-wall temperature were analysed.The results indicated that overtemperatures occurred in the high-temperature superheater under a low load.The regulating damper method caused the high-temperature secondary reheater to exceed the permissible temperature by 21.4℃.The tube-wall temperature exhibited a bimodal distribution along the width.At 0%flue gas recirculation,the maximum tube-wall temperature deviation attained 13.39℃,exceeding a reasonable value by 3.39℃.The swinging burner method was the least likely to cause tube-wall temperature deviations and overtemperatures.The reasonable regulation ranges for swinging burners,regulating dampers,and flue gas recirculation were-20°to 7°,45%to 60%,and 8%to 24%,respectively.Based on different flexible peak shaving constraints,an appropriate temperature-regulating method was selected,and the regulation rangewas specified.

Optimization of Urban Multi-energy Flow Systems Considering Seasonal Peak Shaving of Natural Gas

In order to alleviate the shortage of natural gas supply in winter,relevant policies have been issued to promote the construction of gas peak-shaving and storage facilities.Largescale gas storage can transfer the supply-demand relationship of natural gas in time sequence,which has great potential in improving the economy and reliabillity of urban multi-energy flow systems.Addressing this issue,this paper proposes a mid-and long-term energy optimization method for urban multi-energy flow system that considers seasonal peak shaving of natural gas.First,the energy supply and demand features of different energy subsystems are analyzed.Then,a network model of the electricity-gas-heat multi-energy flow system is established.Considering the time-of-use electricity price mechanism and the seasonal fluctuations of the natural gas price,a mid-and long-term energy optimization model maximizing the annual economic revenue is established.The alternative direction multiplier method with Gaussian back substitution(ADMM-GBS)algorithm is used to solve the optimal dispatch model.Finally,the proposed method is verified by employing the actual data of the demonstration zone in Yangzhong City,China.The simulation results show that the proposed method is effective.

Peak Electricity Demand Management and Energy Efficiency among Large Steel Manufacturing Firms in Nairobi Region, Kenya

To reduce peak electricity demand and hence reduce capacity costs due to added investment of generating additional power to meet short intervals of peak demand, can enhance energy efficiency. Where it is possible to adjust timing and the quantity of electricity consumption and at the same time achieve the same useful effect, the value of the energy service itself remains unchanged. Peak demand management is viewed as the balance between demand and generation of energy hence an important requirement for stabilized operation of power system. Therefore, the purpose of this study was to establish the correlation between peak electricity demand management strategies and energy efficiency among large steel manufacturing firms in Nairobi, Kenya. The strategies investigated were demand scheduling, Peak shrinking and Peak shaving. Demand scheduling involves shifting predetermined loads to low peak periods thereby flattening the demand curve. Peak shrinking on the other hand involves installation of energy efficient equipment thereby shifting the overall demand curve downwards. Peak shaving is the deployment of secondary generation on site to temporarily power some loads during peak hours thereby reducing demand during the peak periods of the plant. The specific objectives were to test the relationship between demand scheduling and energy efficiency among large steel manufacturing firms in Nairobi Region;to test the correlation between peak shrinking and energy efficiency among large steel manufacturing firms in Nairobi Region;and to test the association between peak shaving and energy efficiency among large steel manufacturing firms in Nairobi Region. The study adopted a descriptive research design to determine the relationship between each independent variable namely demand scheduling, peak shrinking, peak shaving and the dependent variable, the energy efficiency. The target population was large steel manufacturing firms in Nairobi Region, Kenya. The study used both primary and secondary data. The primary data was from structured questionnaires while secondary data was from historical electricity consumption data for the firms under study. The results revealed that both peak shrinking and peak shaving were statistically significant in influencing energy efficiency among the steel manufacturing firms in Nairobi Region, each with Pearson correlation coefficient of 0.903, thus a strong linear relationship between the investigated strategy and the dependent variable, energy efficiency. The obtained results are significant at probability value of 0.005 (p 0.05). The conclusion is that peak shrinking and peak shaving have an impact on energy efficiency in the population under study, and if properly implemented, may lead to efficient utilization of the available energy. The study further recommended that peak demand management practices need to be implemented efficiently as a way of improving the overall plant load factor and energy efficiency.

Numerical Simulation of Combustion in 660MWTangentially Fired Pulverized Coal Boiler on Ultra-Low Load Operation

In this paper,the combustion conditions in the boiler furnace of a 660 MWtangential fired pulverized coal boiler are numerically simulated at 15%and 20%rated loads,to study the flexibility of coal-fired power units on ultra-low load operation.The numerical results show that the boiler can operate safely at 15%and 20%ultra-low loads,and the combustion condition in the furnace is better at 20%load,and the tangent circles formed by each characteristic section in the furnace are better,and when the boiler load is decreased to 15%,the tangent circles in the furnace begin to deteriorate.The average flue gas temperature of different areas of the furnace shows that when the boiler furnace operates under ultra-low load conditions,the average smoke temperature of the cold ash hopper at 20%load is basically the same as the average smoke temperature at 15%load;in the burner area,the average smoke temperature of the cold ash hopper at 20%load is about 50 K higher than that at 15%load;in the burned out area,the average smoke temperature of the cold ash hopper at 20%load is slightly higher than that at 15%load.The average temperature of flue gas in the furnace showed a tendency to increase rapidly with the height of the furnace,then slow down and fluctuate the temperature in the burner area,and finally increase slightly in the burnout area due to the further combustion of combustible components to release heat,and then began to decrease.

“Extreme utilization” theory and practice in gas storages with complex geological conditions

Based on more than 20-year operation of gas storages with complex geological conditions and a series of research findings, the pressure-bearing dynamics mechanism of geological body is revealed. With the discovery of gas-water flowing law of multi-cycle relative permeability hysteresis and differential utilization in zones, the extreme utilization theory targeting at the maximum amount of stored gas, maximum injection-production capacity and maximum efficiency in space utilization is proposed to support the three-in-one evaluation method of the maximum pressure-bearing capacity of geological body, maximum well production capacity and maximum peak shaving capacity of storage space. This study realizes the full potential of gas storage(storage capacity) at maximum pressure, maximum formation-wellbore coordinate production, optimum well spacing density match with finite-time unsteady flow, and peaking shaving capacity at minimum pressure, achieving perfect balance between security and capacity. Operation in gas storages, such as Hutubi in Xinjiang, Xiangguosi in Xinan, and Shuang6 in Liaohe, proves that extreme utilization theory has promoted high quality development of gas storages in China.

Combined peak reduction and self-consumption using proximal policy optimisation

Residential demand response programs aim to activate demand flexibility at the household level.In recent years,reinforcement learning(RL)has gained significant attention for these type of applications.A major challenge of RL algorithms is data efficiency.New RL algorithms,such as proximal policy optimisation(PPO),have tried to increase data efficiency.Addi tionally,combining RL with transfer learning has been proposed in an effort to mitigate this challenge.In this work,we further improve upon state-of-the-art transfer learning performance by incorporating demand response domain knowledge into the learning pipeline.We evaluate our approach on a demand response use case where peak shaving and self-consumption is incentivised by means of a capacity tariff.We show our adapted version of PPO,combined with transfer learming,reduces cost by 14.51%compared to a regular hysteresis controller and by 6.68%compared to traditional PPO.

Optimization dispatching strategy for an energy storage system considering its unused capacity sharing

In renewable energy systems,energy storage systems can reduce the power fluctuation of renewable energy sources and compensate for the prediction deviation.However,if the renewable energy prediction deviation is small,the energy storage system may work in an underutilized state.To efficiently utilize a renewable-energy-sided energy storage system(RES),this study proposed an optimization dispatching strategy for an energy storage system considering its unused capacity sharing.First,this study proposed an unused capacity-sharing strategy for the RES to fully utilize the storage’s unused capacity and elevate the storage’s service efficiency.Second,RES was divided into“deviation-compensating energy storage(DES)”and“sharing energy storage(SES)”to clarify the function of RES in the operation process.Third,this study established an optimized dispatching model to achieve the lowest system operating cost wherein the unused capacity-sharing strategy could be integrated.Finally,a case study was investigated,and the results indicated that the proposed model and algorithm effectively improved the utilization of renewable-energy-side energy storage systems,thereby reducing the total operation cost and pressure on peak shaving.

Blockchain-based prosumer incentivization for peak mitigation through temporal aggregation and contextual clustering

Peak mitigation is of interest to power companies as peak periods may require the operator to over provision supply in order to meet the peak demand.Flattening the usage curve can result in cost savings,both for the power companies and the end users.Integration of renewable energy into the energy infrastructure presents an opportunity to use excess renewable generation to supplement supply and alleviate peaks.In addition,demand side management can shift the usage from peak to off-peak times and reduce the magnitude of peaks.In this work,we present a data driven approach for incentive-based peak mitigation.Understanding user energy profiles is an essential step in this process.We begin by analysing a popular energy research dataset published by the Ausgrid corporation.Extracting aggregated user energy behavior in temporal contexts and semantic linking and contextual clustering give us insight into consumption and rooftop solar generation patterns.We implement,and performance test a blockchain-based prosumer incentivization system.The smart contract logic is based on our analysis of the Ausgrid dataset.Our implementation is capable of supporting 792,540 customers with a reasonably low infrastructure footprint.

Electric vehicle optimal scheduling method considering charging piles matching based on edge intelligence

To adress the problems of insufficient consideration of charging pile resource limitations,discrete-time scheduling methods that do not meet the actual demand and insufficient descriptions of peak-shaving response capability in current electric vehicle(EV)opti-mization scheduling,edge intelligence-oriented electric vehicle optimization scheduling and charging station peak-shaving response capability assessment methods are proposed on the basis of the consideration of electric vehicle and charging pile matching.First,an edge-intelligence-oriented electric vehicle regulation frame for charging stations is proposed.Second,continuous time variables are used to represent the available charging periods,establish the charging station controllable EV load model and the future available charging pile mathematical model,and establish the EV and charging pile matching matrix and constraints.Then,with the goal of maximizing the user charging demand and reducing the charging cost,the charging station EV optimal scheduling model is established,and the EV peak response capacity assessment model is further established by considering the EV load shifting constraints under different peak response capacities.Finally,a typical scenario of a real charging station is taken as an example for the analysis of optimal EV scheduling and peak shaving response capacity,and the proposed method is compared with the traditional method to verify the effectiveness and practicality of the proposed method.

Flexible Operation Mode of Coal-fired Power Unit Coupling with Heat Storage of Extracted Reheat Steam

In order to provide more grid space for the renewable energy power,the traditional coal-fired power unit should be operated flexibility,especially achieved the deep peak shaving capacity.In this paper,a new scheme using the reheat steam extraction is proposed to further reduce the load far below 50%rated power.Two flexible operation modes of increasing power output mode and reducing fuel mode are proposed in heat discharging process.A 600 MW coal-fired power unit with 50%rated power is chosen as the research model.The results show that the power output is decreased from 300.03 MW to 210.07 MW when the extracted reheat steam flow rate is 270.70 t·h^(-1),which increases the deep peak shaving capacity by 15%rated power.The deep peak shaving time and the thermal efficiency are 7.63 h·d^(-1)and 36.91%respectively for the increasing power output mode,and they are 7.24 h·d^(-1)and 36.58%respectively for the reducing fuel mode.The increasing power output mode has the advantages of higher deep peak shaving time and the thermal efficiency,which is recommended as the preferred scheme for the flexible operation of the coal-fired power unit.

Off-Design Performance of 9F Gas Turbine Based on gPROMs and BP Neural Network Model

Gas turbines are increasingly and widely used,whose research and production reflect a country’s industrial capacity and level.Due to the changeable working environment,gas turbines usually work under the condition of simultaneous changes of ambient temperature,load and fuel.However,the current researches mainly focus on the change in single condition,and do not fully consider the simultaneous change in different conditions.On the basis of single condition,this paper further studies the dual off-design performance of gas turbines under three conditions:temperature-load,fuel-load and fuel-temperature.Firstly,the whole machine model of a gas turbine is established,in which the compressor model has the greatest impact on the performance of gas turbines.Therefore,this paper obtains a more accurate compressor model by combining the engineering modeling advantages of gPROMs and the powerful mathematical calculation ability of MATLAB neural network.Then,according to the established gas turbine model,the dual off-design performance is studied,which is mainly based on the parameter of output and efficiency.The result shows that the efficiency and power output of gas turbines will decrease with the increase of ambient temperature.With the decrease of fuel calorific value,power output and efficiency will increase.As the load decreases,the efficiency of the gas turbines will decrease,and these changes are consistent with the single off-design performance.However,when the fuel and temperature change simultaneously,only adjusting the IGV angle cannot avoid the surge when the temperature is above 30°C.At this time,it is necessary to adjust the extraction rate in order to ensure the safe and stable operation of gas turbines.Therefore,the research on dual off-design performance of gas turbines has an important significance for the peak shaving operation of gas turbines.

The operating schedule for battery energy storage companies in electricity market

This paper presents a series of operating schedules for Battery Energy Storage Companies(BESC)to provide peak shaving and spinning reserve services in the electricity markets under increasing wind penetration.As individual market participants,BESC can bid in ancillary services markets in an Independent System Operator(ISO)and contribute towards frequency and voltage support in the grid.Recent development in batteries technologies and availability of the day-ahead spot market prices would make BESC economically feasible.Profit maximization of BESC is achieved by determining the optimum capacity of Energy Storage Systems(ESS)required for meeting spinning reserve requirements as well as peak shaving.Historic spot market prices and frequency deviations from Australia Energy Market Operator(AEMO)are used for numerical simulations and the economic benefits of BESC is considered reflecting various aspects in Australia’s National Electricity Markets(NEM).

Modeling and Energy Efficiency Analysis of Thermal Power Plant with High Temperature Thermal Energy Storage(HTTES)

This paper presents the recent research on the study of the strategies for the flexible operation of the thermal power plant to meet the requirement of load balance. The study aimed to investigate the feasibility of bringing the High Temperature Thermal Energy Storage(HTTES) to the thermal power plant steam-water cycle, to identify the suitable HTTES in the cold(hot) section of the reheating pipeline and to test the efficiency of the HTTES integration to increase the flexibility of peak shaving and energy efficiency via thermal power plant with HTTTES modelling and simulation. Thermoflex was adopted to perform the simulation and a 300 MW subcritical coal-fired power plant model was implemented onto the software platform. The simulation results show that it is feasible to extract steam from the steam turbine to charge the HTTES, and to discharge the stored thermal energy back to the power generation process, and to analyse the improved capability of the plant flexible operation with HTTES. Then the study was extended to analyse the effect of thermal energy temperature, the opening of the regulating valve, and the pipeline pressure loss aspects on thermal efficiency of the whole plant. The study is beneficial to achieve more economic operation of the thermal power plant with HTTES integration. It is concluded that the introduction of the HTTES can improve the consumption of wind power, and these ideas and methods for solving the energy consumption of the renewable energy and reducing the peak energy consumption are provided.

Inverse Proportion Technique Based Scheduling Strategy for Energy Storage System Considering Load Demand Differences

The energy storage system(ESS)as a demand-side management(DSM)resource can effectively smooth the load power fluctuation of a power system.However,designing a more reasonable ESS operational strategy will be a prerequisite before incorporating the energy storage device into DSM.As different load levels have different demands for the real-time chargedischarge power of an ESS,this paper proposes a heuristic ESS operation scheduling strategy which can take into account the electrical load demand differences.In this paper,firstly,two demand degree concepts for charging power and discharging power are defined to describe the differentiated ESS demand under the condition of different electrical load levels.Secondly,an inverse proportion technique based ESS scheduling strategy,with the consideration of the load demand difference,is proposed in this paper.Thirdly,some evaluating indices are defined in this paper for describing the influence of the proposed strategy on the smoothing degree of the daily load curve.Finally,several case studies are designed to verify the validity and correctness of the proposed technique,and the results show that the proposed technique can effectively smooth the load curve and improve the ability of peak shaving and valley filling.

Highly efficient conversion of surplus electricity to hydrogen energy via polysulfides redox

Decoupled electrolysis of water is a promising strategy for peak load regulation of electricity.The key to developing this technology is to construct decoupled devices containing stable redox mediators and corresponding efficient catalysts,which remains a considerable challenge.Herein,we designed a high-performance device,using polysulfides as mediators and graphene-encapsulated CoNi as catalysts.It produced H2 with a low potential of 0.82 V at 100 mA/cm^(2),saving 60.2%more energy than direct water electrolysis.The capacity of H2 production reached 2.53105 mAh/cm^(2),which is the highest capacity reported so far.This device exhibited excellent cyclability in 15-day recycle tests,without any decay of performance.The calculation results revealed that the electronic structure of the graphene shell was modulated by the electron transfer from N-dopant and metal core,which significantly facilitated recycle of polysulfides on graphene surfaces.This study provides a promising method for constructing a smart grid by developing efficient decoupled devices.

Auxiliary Service Dynamic Compensation Mechanism Design for Incentivizing Market Participants to Provide Flexibility in China

Because of the rapid growth of new energy and the accompanying considerable uncertainty in the power market,the demand for flexibility in a power sys-tem has risen sharply.In the meantime,the market structure of auxiliary services has changed,resulting in market participants(MPs)benefiting less than expected from providing flexible services.To encourage MPs to provide flexibility,this study proposes a dynamic design framework for an auxiliary service compensation mech-anism.To evaluate the proposed framework,a case study is conducted,examining a peak-shaving service in Liao-ning province in northeast China.First,the operational status and limitations of the typical product,the peak-shaving service,in China’s flexibility auxiliary ser-vices market are analyzed.Then,taking into consideration the time value of the flexible products provided by the MPs,a dynamic mechanism for hierarchical compensation of flexibility auxiliary service costs is proposed,and a mathematical model aimed at optimizing the MPs’comprehensive income is constructed.The results show that,compared with the existing traditional mechanism,the proposed method can effectively guarantee fair remu-neration for the flexibility provider,while easing the tense supply-demand relationship in the flexibility market.