Conventional coordinated control strategies for DC bus voltage signal(DBS)in islanded DC microgrids(IDCMGs)struggle with coordinating multiple distributed generators(DGs)and cannot effectively incorporate state of cha...Conventional coordinated control strategies for DC bus voltage signal(DBS)in islanded DC microgrids(IDCMGs)struggle with coordinating multiple distributed generators(DGs)and cannot effectively incorporate state of charge(SOC)information of the energy storage system,thereby reducing the system flexibility.In this study,we propose an adaptive coordinated control strategy that employs a two-layer fuzzy neural network controller(FNNC)to adapt to varying operating conditions in an IDCMG with multiple PV and battery energy storage system(BESS)units.The first-layer FNNC generates optimal operating mode commands for each DG,thereby avoiding the requirement for complex operating modes based on SOC segmentation.An optimal switching sequence logic prioritizes the most appropriate units during mode transitions.The second-layer FNNC dynamically adjusts the droop power to overcome power distribution challenges among DG groups.This helps in preventing the PV power from exceeding the limits and mitigating the risk of BESS overcharging or over-discharging.The simulation results indicate that the proposed strategy enhances the coordinated operation of multi-DG IDCMGs,thereby ensuring the efficient and safe utilization of PV and BESS.展开更多
The use of different energy carriers together,known as an energy hub,has been a hot topic of research in recent years amongst scientists and researchers.The term energy hub refers to the simultaneous operation of vari...The use of different energy carriers together,known as an energy hub,has been a hot topic of research in recent years amongst scientists and researchers.The term energy hub refers to the simultaneous operation of various infrastructures for energy generation and transfer,which has gained momentum in the form of microgrids(MGs).This paper introduces a new strategy for the optimal performance of an MG consisting of different energy carriers for each day.In a smart distribution network(DN),MGs can reduce their own costs in the previous-day market by bidding on sales and purchases.The sales and purchases bidding problem is challenging due to different uncertainties,however.This paper proposes a two-stage strategy for making an optimal bid on electricity sales and purchases with electricity and gas price dependency in the previous-day and real-time markets for an energy hub.In this model,the MG behavior regarding the electricity and gas energy sales/purchase,the simultaneous effects of electricity and gas prices,as well as the energy carriers’dependence on one another are all examined.Due to the inherent uncertainty in the sources of clean energy production,the probabilistic model and the production and reduction scenario have been used in the paper to cover this issue.In the proposed grid,energy sales/purchases are presented in a multi-carrier MG in a two-stage model.This model is solved by using the harmony search algorithm in MATLAB.Numeric results demonstrate the benefits of this model in reducing energy hub costs of operation.展开更多
Peer-to-peer(P2P)energy trading provides a promising solution for integrating distributed microgrids(MGs).However,most existing research works on P2P energy trading among MGs ignore the influence of the dynamic networ...Peer-to-peer(P2P)energy trading provides a promising solution for integrating distributed microgrids(MGs).However,most existing research works on P2P energy trading among MGs ignore the influence of the dynamic network usage fees imposed by the distribution system operator(DSO).Therefore,a method of P2P energy trading among MGs based on the optimal dynamic network usage fees is proposed in this paper to balance the benefits of DSO.The interaction between DSO and MG is formulated as a Stackelberg game,in which the existence and uniqueness of optimal dynamic network usage fees are proven.Additionally,the optimal dynamic network usage fees are obtained by transforming the bi-level problem into single-level mixed-integer quadratic programming using Karush-Kuhn-Tucker conditions.Furthermore,the underlying relationship among optimal dynamic network usage fees,electrical distance,and power flow is revealed,and the mechanism of the optimal dynamic network usage fee can further enhance P2P energy trading among MGs.Finally,simulation results on an enhanced IEEE 33-bus system demonstrate that the proposed mechanism achieves a 17.08%reduction in operation costs for MG while increasing DSO revenue by 15.36%.展开更多
This paper presents a risk-based competitive bi-level framework for optimal decision-making in energy sales by a distribution company(DISCO)in an active distribution network(ADN).At the upper level of this framework,t...This paper presents a risk-based competitive bi-level framework for optimal decision-making in energy sales by a distribution company(DISCO)in an active distribution network(ADN).At the upper level of this framework,the DISCO and a rival retailer compete for selling energy.The DISCO intends to maximize its profit in the competitive market.Therefore,it is very important for the DISCO to make a decision and offer an optimal price for attracting customers and winning the competition.Networked microgrids(MGs)at the lower level,as the costumers,intend to purchase energy from less expensive sources in order to minimize costs.There is a bi-level framework with two different targets.The genetic algorithm is used to solve this problem.The DISCO needs to be cautious,so it uses the conditional value at risk(CVaR)to reduce the risk and increase the probability of making the desired profit.The effect of this index on the trade between the two levels is studied.The simulation results show that the proposed method can reduce the cost of MGs as the costumers,and can enable the DISCO as the seller to win the competition with its rivals.展开更多
Taking the consumption rate of renewable energy and the operation cost of hybrid AC/DC microgrid as the optimization objectives,the adjustment of load demand curves is carried out considering the demand side response(...Taking the consumption rate of renewable energy and the operation cost of hybrid AC/DC microgrid as the optimization objectives,the adjustment of load demand curves is carried out considering the demand side response(DSR)on the load side.The complementary utilization of renewable energy between AC area and DC area is achieved to meet the load demand on the source side.In the network side,the hybrid AC/DC microgrids purchase electricity from the power grid at the time-of-use(TOU)price and sell the surplus power of renewable energy to the power grid for profits.The improved memetic algorithm(IMA)is introduced and applied to solve the established mathematical model.The promotion effect of the proposed source-network-load coordination strategies on the optimal operation of hybrid AC/DC microgrid is verified.展开更多
基金supported by National Key R&D Program of ChinaunderGrant,(2021YFB2601403).
文摘Conventional coordinated control strategies for DC bus voltage signal(DBS)in islanded DC microgrids(IDCMGs)struggle with coordinating multiple distributed generators(DGs)and cannot effectively incorporate state of charge(SOC)information of the energy storage system,thereby reducing the system flexibility.In this study,we propose an adaptive coordinated control strategy that employs a two-layer fuzzy neural network controller(FNNC)to adapt to varying operating conditions in an IDCMG with multiple PV and battery energy storage system(BESS)units.The first-layer FNNC generates optimal operating mode commands for each DG,thereby avoiding the requirement for complex operating modes based on SOC segmentation.An optimal switching sequence logic prioritizes the most appropriate units during mode transitions.The second-layer FNNC dynamically adjusts the droop power to overcome power distribution challenges among DG groups.This helps in preventing the PV power from exceeding the limits and mitigating the risk of BESS overcharging or over-discharging.The simulation results indicate that the proposed strategy enhances the coordinated operation of multi-DG IDCMGs,thereby ensuring the efficient and safe utilization of PV and BESS.
基金supported as a Major Project of the Beijing Social Science Foundation“Research on Financial Support System Adapting to the Coordinated Development of Strategic Emerging Industries in Beijing-Tianjin-Hebei”,No.20ZDA11.
文摘The use of different energy carriers together,known as an energy hub,has been a hot topic of research in recent years amongst scientists and researchers.The term energy hub refers to the simultaneous operation of various infrastructures for energy generation and transfer,which has gained momentum in the form of microgrids(MGs).This paper introduces a new strategy for the optimal performance of an MG consisting of different energy carriers for each day.In a smart distribution network(DN),MGs can reduce their own costs in the previous-day market by bidding on sales and purchases.The sales and purchases bidding problem is challenging due to different uncertainties,however.This paper proposes a two-stage strategy for making an optimal bid on electricity sales and purchases with electricity and gas price dependency in the previous-day and real-time markets for an energy hub.In this model,the MG behavior regarding the electricity and gas energy sales/purchase,the simultaneous effects of electricity and gas prices,as well as the energy carriers’dependence on one another are all examined.Due to the inherent uncertainty in the sources of clean energy production,the probabilistic model and the production and reduction scenario have been used in the paper to cover this issue.In the proposed grid,energy sales/purchases are presented in a multi-carrier MG in a two-stage model.This model is solved by using the harmony search algorithm in MATLAB.Numeric results demonstrate the benefits of this model in reducing energy hub costs of operation.
基金supported by the National Natural Science Foundation of China(No.52107199)the International Corporation Project of Shanghai Science and Technology Commission(No.21190780300).
文摘Peer-to-peer(P2P)energy trading provides a promising solution for integrating distributed microgrids(MGs).However,most existing research works on P2P energy trading among MGs ignore the influence of the dynamic network usage fees imposed by the distribution system operator(DSO).Therefore,a method of P2P energy trading among MGs based on the optimal dynamic network usage fees is proposed in this paper to balance the benefits of DSO.The interaction between DSO and MG is formulated as a Stackelberg game,in which the existence and uniqueness of optimal dynamic network usage fees are proven.Additionally,the optimal dynamic network usage fees are obtained by transforming the bi-level problem into single-level mixed-integer quadratic programming using Karush-Kuhn-Tucker conditions.Furthermore,the underlying relationship among optimal dynamic network usage fees,electrical distance,and power flow is revealed,and the mechanism of the optimal dynamic network usage fee can further enhance P2P energy trading among MGs.Finally,simulation results on an enhanced IEEE 33-bus system demonstrate that the proposed mechanism achieves a 17.08%reduction in operation costs for MG while increasing DSO revenue by 15.36%.
文摘This paper presents a risk-based competitive bi-level framework for optimal decision-making in energy sales by a distribution company(DISCO)in an active distribution network(ADN).At the upper level of this framework,the DISCO and a rival retailer compete for selling energy.The DISCO intends to maximize its profit in the competitive market.Therefore,it is very important for the DISCO to make a decision and offer an optimal price for attracting customers and winning the competition.Networked microgrids(MGs)at the lower level,as the costumers,intend to purchase energy from less expensive sources in order to minimize costs.There is a bi-level framework with two different targets.The genetic algorithm is used to solve this problem.The DISCO needs to be cautious,so it uses the conditional value at risk(CVaR)to reduce the risk and increase the probability of making the desired profit.The effect of this index on the trade between the two levels is studied.The simulation results show that the proposed method can reduce the cost of MGs as the costumers,and can enable the DISCO as the seller to win the competition with its rivals.
基金supported by the National Natural Science Foundation of China(No.51577068)the National High Technology Research and Development Program of China(863 Program)(No.2015AA050104).
文摘Taking the consumption rate of renewable energy and the operation cost of hybrid AC/DC microgrid as the optimization objectives,the adjustment of load demand curves is carried out considering the demand side response(DSR)on the load side.The complementary utilization of renewable energy between AC area and DC area is achieved to meet the load demand on the source side.In the network side,the hybrid AC/DC microgrids purchase electricity from the power grid at the time-of-use(TOU)price and sell the surplus power of renewable energy to the power grid for profits.The improved memetic algorithm(IMA)is introduced and applied to solve the established mathematical model.The promotion effect of the proposed source-network-load coordination strategies on the optimal operation of hybrid AC/DC microgrid is verified.
