Autonomous Demand Side Management of Electric Vehicles

There is an error in the table of content, where publication A and B have swiched places.Demand-side management approaches that exploit the temporal flexibility of electric vehicles have attracted much attention in recent years due to the increasing market penetration. These demand-side management measures contribute to alleviating the burden on the power system, especially in distribution grids where bottlenecks are more prevalent. Electric vehicles can be defined as an attractive asset for distribution system operators, which have the potential to provide grid services if properly managed. In this thesis, first, a systematic investigation is conducted for two typically employed demand-side management methods reported in the literature: A voltage droop control-based approach and a market-driven approach. Then a control scheme of decentralized autonomous demand side management for electric vehicle charging scheduling which relies on a unidirectionally communicated grid-induced signal is proposed. In all the topics considered, the implications on the distribution grid operation are evaluated using a set of time series load flow simulations performed for representative Austrian distribution grids. Droop control mechanisms are discussed for electric vehicle charging control which requires no communication. The method provides an economically viable solution at all penetrations if electric vehicles charge at low nominal power rates. However, with the current market trends in residential charging equipment especially in the European context where most of the charging equipment is designed for 11 kW charging, the technical feasibility of the method, in the long run, is debatable. As electricity demand strongly correlates with energy prices, a linear optimization algorithm is proposed to minimize charging costs, which uses next-day market prices as the grid-induced incentive function under the assumption of perfect user predictions. The constraints on the state of charge guarantee the energy required for driving is delivered without failure. An average energy cost saving of 30% is realized at all penetrations. Nevertheless, the avalanche effect due to simultaneous charging during low price periods introduces new power peaks exceeding those of uncontrolled charging. This obstructs the grid-friendly integration of electric vehicles.publishedVersio

IEC 61851 compliant demand side management algorithm for electric vehicle charging : a MILP based decentralized approach

Author's accepted manuscript.© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Charging scheduling algorithms play a vital role in diminishing the negative consequences on electricity networks from the widespread adaptation of electro-mobility. Therefore, there is a growing interest in a pragmatic solution that requires only modest resources. To reach this goal, we propose a decentralized, IEC charging standard compliant, two-layer charging scheduling algorithm, which only requires unidirectional communication and reduced computing capabilities. The objective of the algorithm proposed is to achieve valley filling by exploiting the flexibility of electric vehicles through optimal tracking of a target signal. The IEC standard compliant, semi-continuous charging characteristic is attained with a mixed-integer linear formulation. Different formulations of the problem by forming vehicle groups and randomization in charging events are examined. The results show that the IEC 61851-compliant formulation with a semi-continuous charging characteristic for the proposed method fails to perform as good as the variable charging rate formulation, which has a 2.8 and 3.9-fold deviation in the variance of the total demand relative to the variable charging rate at 50% and 100% penetration rates, respectively. Nevertheless, the inclusion of randomization and grouping improves the performance of the IEC standard-compliant formulation. Considering four groups, the variance in demand of semi-continuous charging formulation at 50% penetration is nearly equal to that of the variable charging rate proofing the viable potential of the technically feasible solution proposed.acceptedVersio

Home energy management system : a home energy management system under different electricity pricing mechanisms

Masteroppgave i fornybar energi ENE 500 Universitetet i Agder 2014Peak demand is a severe problem in the electricity grid and it was solved by supply side management in the past. But nowadays the demand side management sources have drawn attention due to the economic and environmental constraints. Demand side management in the domestic sector can play an important role in reducing the peak demand on the power system network. It can help in reducing stress and overloading on the transmission and distribution lines. In many countries there are various demand response programs implemented for industrial and commercial loads. In these programs load control is primarily achieved by various types of pricing mechanisms. There are very few demand response programs in use for energy management in residential sector. Direct curtailment of the loads is the most popular method used to reduce the peak demand in the domestic sector. But by direct load control, customer comfort may be compromised. In contrast peak load reduction through load shifting can benefit both consumers and utilities. In order to analyze demand response in the domestic sector, it is important to understand physical based power intensive load models with an emphasis on water heater units, air conditioner units, clothes dryers and electric vehicles. In this work, these load models are developed considering thermodynamic principles of buildings as well as their built in technical parameters. With the development of smart grid systems specially in the distribution network and possibility of load modeling, there is a requirement of a domestic intelligent energy management algorithm. In this work, power intensive non-critical loads are managed through developed energy management system algorithm and these loads are water heater, air conditioning unit, clothes dryer and electric vehicle. With the introduction of electric vehicles, demand responses can be performed within home for avoiding any overloading problems in the distribution network as well as on power generation. Additionally, the electricity bill saving which can be gained through proposed energy management system is analyzed by considering different electricity pricing mechanisms. The highlight of the presented energy management system algorithm for home energy management is its capability to control the non-critical loads below specified peak demand limits by considering consumer behavior and priorities, giving consumers more flexibility in their operational time. Moreover, the results show that the electricity saving which can be gained through the proposed energy management system lies in a noticeably high range. It is expected that the research findings of this work can be beneficial to utilities in providing information of limits and scope of domestic demand responses. And also it is anticipated that the cost analysis carried out can be used to motivate the consumers towards demand response through the developed energy management system. Key words: Domestic demand response, Home energy management system (EMS), demand limits, non-critical loads, load priority, Time of Use pricing, Real Time Pricin

Voltage-based droop control of electric vehicles in distribution grids under different charging power levels

publishedVersio

Optimal power tracking for autonomous demand side management of electric vehicles

Increasing electric vehicle penetration leads to undesirable peaks in power if no proper coordination in charging is implemented. We tested the feasibility of electric vehicles acting as flexible demands responding to power signals to minimize the system peaks. The proposed hierarchical autonomous demand side management algorithm is formulated as an optimal power tracking problem. The distribution grid operator determines a power signal for filling the valleys in the non-electric vehicle load profile using the electric vehicle demand flexibility and sends it to all electric vehicle controllers. After receiving the control signal, each electric vehicle controller re-scales it to the expected individual electric vehicle energy demand and determines the optimal charging schedule to track the re-scaled signal. No information concerning the electric vehicles are reported back to the utility, hence the approach can be implemented using unidirectional communication with reduced infrastructural requirements. The achieved results show that the optimal power tracking approach has the potential to eliminate additional peak demands induced by electric vehicle charging and performs comparably to its central implementation. The reduced complexity and computational overhead permits also convenient deployment in practice.publishedVersio

Autonomous demand side management of electric vehicles

Demand-side management approaches that exploit the temporal flexibility of electric vehicles have attracted much attention in recent years due to the increasing market penetration. These demand-side management measures contribute to alleviating the burden on the power system, especially in distribution grids where bottlenecks are more prevalent. Electric vehicles can be defined as an attractive asset for distribution system operators, which have the potential to provide grid services if properly managed. In this thesis, first, a systematic investigation is conducted for two typically employed demand-side management methods reported in the literature: A voltage droop control-based approach and a market-driven approach. Then a control scheme of decentralized autonomous demand side management for electric vehicle charging scheduling which relies on a unidirectionally communicated grid-induced signal is proposed. In all the topics considered, the implications on the distribution grid operation are evaluated using a set of time series load flow simulations performed for representative Austrian distribution grids. Droop control mechanisms are discussed for electric vehicle charging control which requires no communication. The method provides an economically viable solution at all penetrations if electric vehicles charge at low nominal power rates. However, with the current market trends in residential charging equipment especially in the European context where most of the charging equipment is designed for 11 kW charging, the technical feasibility of the method, in the long run, is debatable. As electricity demand strongly correlates with energy prices, a linear optimization algorithm is proposed to minimize charging costs, which uses next-day market prices as the grid-induced incentive function under the assumption of perfect user predictions. The constraints on the state of charge guarantee the energy required for driving is delivered without failure. An average energy cost saving of 30% is realized at all penetrations. Nevertheless, the avalanche effect due to simultaneous charging during low price periods introduces new power peaks exceeding those of uncontrolled charging. This obstructs the grid-friendly integration of electric vehicles

Home energy management system : a home energy management system under different electricity pricing mechanisms

Masteroppgave i fornybar energi ENE 500 Universitetet i Agder 2014Peak demand is a severe problem in the electricity grid and it was solved by supply side management in the past. But nowadays the demand side management sources have drawn attention due to the economic and environmental constraints. Demand side management in the domestic sector can play an important role in reducing the peak demand on the power system network. It can help in reducing stress and overloading on the transmission and distribution lines. In many countries there are various demand response programs implemented for industrial and commercial loads. In these programs load control is primarily achieved by various types of pricing mechanisms. There are very few demand response programs in use for energy management in residential sector. Direct curtailment of the loads is the most popular method used to reduce the peak demand in the domestic sector. But by direct load control, customer comfort may be compromised. In contrast peak load reduction through load shifting can benefit both consumers and utilities. In order to analyze demand response in the domestic sector, it is important to understand physical based power intensive load models with an emphasis on water heater units, air conditioner units, clothes dryers and electric vehicles. In this work, these load models are developed considering thermodynamic principles of buildings as well as their built in technical parameters. With the development of smart grid systems specially in the distribution network and possibility of load modeling, there is a requirement of a domestic intelligent energy management algorithm. In this work, power intensive non-critical loads are managed through developed energy management system algorithm and these loads are water heater, air conditioning unit, clothes dryer and electric vehicle. With the introduction of electric vehicles, demand responses can be performed within home for avoiding any overloading problems in the distribution network as well as on power generation. Additionally, the electricity bill saving which can be gained through proposed energy management system is analyzed by considering different electricity pricing mechanisms. The highlight of the presented energy management system algorithm for home energy management is its capability to control the non-critical loads below specified peak demand limits by considering consumer behavior and priorities, giving consumers more flexibility in their operational time. Moreover, the results show that the electricity saving which can be gained through the proposed energy management system lies in a noticeably high range. It is expected that the research findings of this work can be beneficial to utilities in providing information of limits and scope of domestic demand responses. And also it is anticipated that the cost analysis carried out can be used to motivate the consumers towards demand response through the developed energy management system. Key words: Domestic demand response, Home energy management system (EMS), demand limits, non-critical loads, load priority, Time of Use pricing, Real Time Pricin

Autonomous demand side management of electric vehicles in a distribution grid

The electricity demand due to the increasing number of EVs presents new challenges for the operation of the electricity network, especially for the distribution grids. The existing grid infrastructure may not be sufficient to meet the new demands imposed by the integration of EVs. Thus, EV charging may possibly lead to reliability and stability issues, especially during the peak demand periods. Demand side management (DSM) is a potential and promising approach for mitigation of the resulting impacts. In this work, we developed an autonomous DSM strategy for optimal charging of EVs to minimize the charging cost and we conducted a simulation study to evaluate the impacts to the grid operation. The proposed approach only requires a one way communicated incentive. Real profiles from an Austrian study on mobility behavior are used to simulate the usage of the EVs. Furthermore, real smart meter data are used to simulate the household base load profiles and a real low voltage grid topology is considered in the load flow simulation. Day-ahead electricity stock market prices are used as the incentive to drive the optimization. The results for the optimum charging strategy is determined and compared to uncontrolled EV charging. The results for the optimum charging strategy show a potential cost saving of about 30.8% compared to uncontrolled EV charging. Although autonomous DSM of EVs achieves a shift of load as pursued, distribution grid operation may be substantially affected by it. We show that in the case of real time price driven operation, voltage drops and elevated peak to average powers result from the coincident charging of vehicles during favourable time slots

Voltage-based droop control of electric vehicles in distribution grids under different charging power levels

If left uncontrolled, electric vehicle charging poses severe challenges to distribution grid operation. Resulting issues are expected to be mitigated by charging control. In particular, voltage-based charging control, by relying only on the local measurements of voltage at the point of connection, provides an autonomous communication-free solution. The controller, attached to the charging equipment, compares the measured voltage to a reference voltage and adapts the charging power using a droop control characteristic. We present a systematic study of the voltage-based droop control method for electric vehicles to establish the usability of the method for all the currently available residential electric vehicle charging possibilities considering a wide range of electric vehicle penetrations. Voltage limits are evaluated according to the international standard EN50160, using long-term load flow simulations based on a real distribution grid topology and real load profiles. The results achieved show that the voltage-based droop controller is able to mitigate the under voltage problems completely in distribution grids in cases either deploying low charging power levels or exhibiting low penetration rates. For high charging rates and high penetrations, the control mechanism improves the overall voltage profile, but it does not remedy the under voltage problems completely. The evaluation also shows the controller’s ability to reduce the peak power at the transformer and indicates the impact it has on users due to the reduction in the average charging rates. The outcomes of the paper provide the distribution grid operators an insight on the voltage-based droop control mechanism for the future grid planning and investments

Voltage-based droop control of electric vehicles in distribution grids under different charging power levels

If left uncontrolled, electric vehicle charging poses severe challenges to distribution grid operation. Resulting issues are expected to be mitigated by charging control. In particular, voltage-based charging control, by relying only on the local measurements of voltage at the point of connection, provides an autonomous communication-free solution. The controller, attached to the charging equipment, compares the measured voltage to a reference voltage and adapts the charging power using a droop control characteristic. We present a systematic study of the voltage-based droop control method for electric vehicles to establish the usability of the method for all the currently available residential electric vehicle charging possibilities considering a wide range of electric vehicle penetrations. Voltage limits are evaluated according to the international standard EN50160, using long-term load flow simulations based on a real distribution grid topology and real load profiles. The results achieved show that the voltage-based droop controller is able to mitigate the under voltage problems completely in distribution grids in cases either deploying low charging power levels or exhibiting low penetration rates. For high charging rates and high penetrations, the control mechanism improves the overall voltage profile, but it does not remedy the under voltage problems completely. The evaluation also shows the controller’s ability to reduce the peak power at the transformer and indicates the impact it has on users due to the reduction in the average charging rates. The outcomes of the paper provide the distribution grid operators an insight on the voltage-based droop control mechanism for the future grid planning and investments