Energy storage systems(ESSs)operate as independent market participants and collaborate with photovoltaic(PV)generation units to enhance the flexible power supply capabilities of PV units.However,the dynamic variations...Energy storage systems(ESSs)operate as independent market participants and collaborate with photovoltaic(PV)generation units to enhance the flexible power supply capabilities of PV units.However,the dynamic variations in the profitability of ESSs in the electricity market are yet to be fully understood.This study introduces a dual-timescale dynamics model that integrates a spot market clearing(SMC)model into a system dynamics(SD)model to investigate the profit-aware capacity growth of ESSs and compares the profitability of independent energy storage systems(IESSs)with that of an ESS integrated within a PV(PV-ESS).Furthermore,this study aims to ascertain the optimal allocation of the PV-ESS.First,SD and SMC models were set up.Second,the SMC model simulated on an hourly timescale was incorporated into the SD model as a subsystem,a dual-timescale model was constructed.Finally,a development simulation and profitability analysis was conducted from 2022 to 2040 to reveal the dynamic optimal range of PV-ESS allocation.Additionally,negative electricity prices were considered during clearing processes.The simulation results revealed differences in profitability and capacity growth between IESS and PV-ESS,helping grid investors and policymakers to determine the boundaries of ESSs and dynamic optimal allocation of PV-ESSs.展开更多
Demand response(DR) is gaining more and more importance in the architecture of power systems in a context of flexible loads and high share of intermittent generation. Changes in electricity markets regulation in sever...Demand response(DR) is gaining more and more importance in the architecture of power systems in a context of flexible loads and high share of intermittent generation. Changes in electricity markets regulation in several countries have recently enabled an effective integration of DR mechanisms in power systems. Through its flexible components(pumps, tanks), drinking water systems are suitable candidates for energy-efficient DR mechanisms. However, these systems are often managed independently of power system operation for both economic and operational reasons. Indeed, a sufficient level of economic viability and water demands risk management are necessary for water utilities to integrate their flexibilities to power system operation. In this paper,we proposed a mathematical model for optimizing pump schedules in water systems while trading DR blocs in a spot power market during peak times. Uncertainties about water demands were considered in the mathematical model allowing to propose power reductions covering the potential risk of real-time water demand forecasting inaccuracy.Numerical results were discussed on a real water system in France, demonstrating both economic and ecological benefits.展开更多
The coordination of enrgy transition,fixed cost recovery,and sufficient generation supply leads to a new challenge for a traditional capacity market mechanism.Moreover,in order to better match network expansion at the...The coordination of enrgy transition,fixed cost recovery,and sufficient generation supply leads to a new challenge for a traditional capacity market mechanism.Moreover,in order to better match network expansion at the same time,it is crucial to redesign the capacity market mechanism considering system topology.In this paper,a novel capacity market mechanism is proposed considering spot market operations,network expansion,and energy transition,which can minimize the total cost of capacity investment,network expansion,and generation operations,while satisfying the energy transition constraints and topology circumstances.Specifically,the capacity market mechanism co-ordinated with spot market operations is illustrated,in which the energy transition and network constraints are embedded.Then,a bi-level optimization model is established where the trade organizers minimize the total cost of both investment and operations,subject to the spot power market simultaneously minimizing the local dispatching costs.The numerical results of a test system show that more economical capacity portfolios can be obtained by constructing reasonable transmission lines,thereby obtaining a more optimal market cost.A detailed multi-scenario simulation is further analyzed to verify the effectiveness of the proposed market mechanism.展开更多
Estimating the price of a financial asset or any tradable product is a complex task that depends on the availability of a reasonable amount of data samples. In the Brazilian electricity market environment, where spot ...Estimating the price of a financial asset or any tradable product is a complex task that depends on the availability of a reasonable amount of data samples. In the Brazilian electricity market environment, where spot prices are centrally calculated by computational models, the projection of hourly energy prices at the spot market is essential for decision-making, and with the particularities of this sector, this task becomes even more complex due to the stochastic behavior of some variables, such as the inflow to hydroelectric power plants and the correlation between variables that affect electricity generation, traditional statistical techniques of time series forecasting present an additional complexity when one tries to project scenarios of spot prices on different time horizons. To address these complexities of traditional forecasting methods, this study presents a new approach based on Machine Learning methodology applied to the electricity spot prices forecasting process. The model’s Learning Base is obtained from public information provided by the Brazilian official computational models: NEWAVE, DECOMP, and DESSEM. The application of the methodology to real cases, using back-testing with actual information from the Brazilian electricity sector demonstrates that the research is promising, as the adherence of the projections with the realized values is significant.展开更多
基金supported by National Natural Science Foundation of China(U2066209)。
文摘Energy storage systems(ESSs)operate as independent market participants and collaborate with photovoltaic(PV)generation units to enhance the flexible power supply capabilities of PV units.However,the dynamic variations in the profitability of ESSs in the electricity market are yet to be fully understood.This study introduces a dual-timescale dynamics model that integrates a spot market clearing(SMC)model into a system dynamics(SD)model to investigate the profit-aware capacity growth of ESSs and compares the profitability of independent energy storage systems(IESSs)with that of an ESS integrated within a PV(PV-ESS).Furthermore,this study aims to ascertain the optimal allocation of the PV-ESS.First,SD and SMC models were set up.Second,the SMC model simulated on an hourly timescale was incorporated into the SD model as a subsystem,a dual-timescale model was constructed.Finally,a development simulation and profitability analysis was conducted from 2022 to 2040 to reveal the dynamic optimal range of PV-ESS allocation.Additionally,negative electricity prices were considered during clearing processes.The simulation results revealed differences in profitability and capacity growth between IESS and PV-ESS,helping grid investors and policymakers to determine the boundaries of ESSs and dynamic optimal allocation of PV-ESSs.
文摘Demand response(DR) is gaining more and more importance in the architecture of power systems in a context of flexible loads and high share of intermittent generation. Changes in electricity markets regulation in several countries have recently enabled an effective integration of DR mechanisms in power systems. Through its flexible components(pumps, tanks), drinking water systems are suitable candidates for energy-efficient DR mechanisms. However, these systems are often managed independently of power system operation for both economic and operational reasons. Indeed, a sufficient level of economic viability and water demands risk management are necessary for water utilities to integrate their flexibilities to power system operation. In this paper,we proposed a mathematical model for optimizing pump schedules in water systems while trading DR blocs in a spot power market during peak times. Uncertainties about water demands were considered in the mathematical model allowing to propose power reductions covering the potential risk of real-time water demand forecasting inaccuracy.Numerical results were discussed on a real water system in France, demonstrating both economic and ecological benefits.
文摘The coordination of enrgy transition,fixed cost recovery,and sufficient generation supply leads to a new challenge for a traditional capacity market mechanism.Moreover,in order to better match network expansion at the same time,it is crucial to redesign the capacity market mechanism considering system topology.In this paper,a novel capacity market mechanism is proposed considering spot market operations,network expansion,and energy transition,which can minimize the total cost of capacity investment,network expansion,and generation operations,while satisfying the energy transition constraints and topology circumstances.Specifically,the capacity market mechanism co-ordinated with spot market operations is illustrated,in which the energy transition and network constraints are embedded.Then,a bi-level optimization model is established where the trade organizers minimize the total cost of both investment and operations,subject to the spot power market simultaneously minimizing the local dispatching costs.The numerical results of a test system show that more economical capacity portfolios can be obtained by constructing reasonable transmission lines,thereby obtaining a more optimal market cost.A detailed multi-scenario simulation is further analyzed to verify the effectiveness of the proposed market mechanism.
文摘Estimating the price of a financial asset or any tradable product is a complex task that depends on the availability of a reasonable amount of data samples. In the Brazilian electricity market environment, where spot prices are centrally calculated by computational models, the projection of hourly energy prices at the spot market is essential for decision-making, and with the particularities of this sector, this task becomes even more complex due to the stochastic behavior of some variables, such as the inflow to hydroelectric power plants and the correlation between variables that affect electricity generation, traditional statistical techniques of time series forecasting present an additional complexity when one tries to project scenarios of spot prices on different time horizons. To address these complexities of traditional forecasting methods, this study presents a new approach based on Machine Learning methodology applied to the electricity spot prices forecasting process. The model’s Learning Base is obtained from public information provided by the Brazilian official computational models: NEWAVE, DECOMP, and DESSEM. The application of the methodology to real cases, using back-testing with actual information from the Brazilian electricity sector demonstrates that the research is promising, as the adherence of the projections with the realized values is significant.
