Modelling the Penetration Effect of Photovoltaics and Electric Vehicles on Electricity Demand and Its Implications on Tariff Structures

The shift towards more renewable energy sources is imminent, this shift is accelerated by the technological advancement and the rise of environmental awareness. However, this shift causes major operational problems to the current grid that is optimised for unidirectional power flow. Besides the operational problems, there are problems related to the optimal tariff scheme. In this thesis a study on the effect of the adoption of photovoltaic solar panels and the electric vehicles on the households' electricity demand profile is presented. The change on the demand profile is going to affect the current tariffs, this effect is also explored in this thesis. In this thesis real life data on household electricity use and photovoltaic power production was used. For electric vehicle charging simulated data was used. Besides that, a demand response scheme for electric vehicle is proposed in order to estimate the savings potential of this demand response on the electricity bill. The results show that the change in the demand profile is not merely a change in the total energy consumption, but it is a change in the power peaks as well. The peaks change significantly in condominiums and rental apartments, in this households' type it increases by around 80%, while in detached and row houses little change is noticed on the peaks, yet they still increase by around 10%. The demand response shows around 1- 12% savings in the distribution bill depending on the household, however it showed more incentives for condominiums and rental apartments. The current distribution tariffs perform asymmetrically with the various households. However, one tariff ensures 11.7 MSEK financial revenue for the distribution system operator, this is higher than the other tariffs' revenue by more than 28.5%. The new prospective situation requires totally different tariffs that ensure a balance between firstly a reasonable revenue for the distribution system operator and secondly incentives for consumers to self produce electricity as well as to reduce their peaks.

Modeling and forecasting the load in the future electricity grid : Spatial electric vehicle load modeling and residential load forecasting

The energy system is being transitioned to increase sustainability. This transition has been accelerated by the increased awareness about the adverse effects of the greenhouse gas (GHG) emissions into the atmosphere. The transition includes switching to electricity as the energy carrier in some sectors, e.g., transportation, increasing the contribution of renewable energy sources (RES) to the grid, and digitalizing the grid services. Electric vehicles (EVs) are promoted and subsidized in many countries among the sustainability initiatives. Consequently, the global sales of EVs rapidly increased in the recent years. Many EV owners might charge their EVs only at home, thereby increasing the residential load. The residential load might further increase due to the initiatives to electrify the heating/cooling sector. This thesis contributes to the knowledge about the operation of the future energy system by modeling the spatial charging load of private EVs in cities, and by proposing a forecasting model to predict the residential load. Both models can be used to evaluate the impacts of both technologies on the local electricity grid. In addition, demand response (DR) schemes can be proposed to reduce the adverse effects of both the charging load of EVs and the residential load. A case study of the EV model on the Herrljunga city grid showed that 100% EV penetration with 3.7 kW (charging rate of 14.8 km/h) chargers will not cause voltage violations in the grid. Winter load is responsible for 5% voltage drop at the weakest bus, and EVs add only 1% to this drop. In a Swedish city, charging EVs will require adding extra 1.43 kW/car to the grid capacity—assuming 22 kW (charging rate of 88 km/h) residential chargers. If the EV charging is not restricted to residential locations, an increase of 1.23 kW/car is expected. The proposed forecasting model is comparable in accuracy to previously developed models. As an advantage, the model produces a probability density function (PDF) describing the model’s certainty in the forecast. In contrast, many previous contributions provided only point forecasts

Modeling and Forecasting of Electric Vehicle Charging, Solar Power Production, and Residential Load : Perspectives into the Future Urban and Rural Energy Systems

The urban and rural energy systems are undergoing modernization. This modernization is motivated by the need to increase sustainability in both systems. Some characteristics of this modernization include electrification of industries, transports, and heating and cooling loads. Additionally, there has been an increase in building-applied photovoltaic (PV) systems, and in the flexibility of customer loads. This thesis aims to progress the knowledge regarding the electric power production and consumption in the future urban and rural energy systems. In total, three models were developed and applied to case studies: a spatial electric vehicle (EV) charging model, a residential load forecasting model, and a clear-sky index (CSI) generative model. The results of the EV spatial model showed that there is an aggregation effect for the charging of the EVs. If all EVs charge opportunistically upon arrival using 3.7 kW, at most 19% of the EVs in a large area will charge simultaneously. Delaying the charging to after 22:00 will result in a significant increase in the simultaneity factor — to 59%. Two forecasting models were compared for the residential load. Both models achieved a root mean square error (RMSE) smaller than 4%. One model had a slightly sharper forecast than the other model — by 2.6% — and a variable prediction interval (PI) which decreased at night. As regards the spatiotemporal matching between PV power production and EV charging in rural and urban areas, the results showed that there were no correlations between the building type in each part of the city and the temporal matching. Both residential and workplace areas had similar temporal matching. This is because of the orientations of the roofs in the cities and the sizes of the parking lots. Considering the impacts of EV charging on the distribution grid of a Swedish municipality (Herrljunga), it is shown that 3.7 kW chargers will result in at most a 1% decrease in the voltage of the grid. No under-voltages were witnessed. In conclusion, the urban and rural energy systems can withstand the penetration of PV and EVs in the nearby coming years. Extreme scenarios might, however, require increasing the flexibility or performing upgrades to the systems.

Modeling and Forecasting of Electric Vehicle Charging, Solar Power Production, and Residential Load : Perspectives into the Future Urban and Rural Energy Systems

The urban and rural energy systems are undergoing modernization. This modernization is motivated by the need to increase sustainability in both systems. Some characteristics of this modernization include electrification of industries, transports, and heating and cooling loads. Additionally, there has been an increase in building-applied photovoltaic (PV) systems, and in the flexibility of customer loads. This thesis aims to progress the knowledge regarding the electric power production and consumption in the future urban and rural energy systems. In total, three models were developed and applied to case studies: a spatial electric vehicle (EV) charging model, a residential load forecasting model, and a clear-sky index (CSI) generative model. The results of the EV spatial model showed that there is an aggregation effect for the charging of the EVs. If all EVs charge opportunistically upon arrival using 3.7 kW, at most 19% of the EVs in a large area will charge simultaneously. Delaying the charging to after 22:00 will result in a significant increase in the simultaneity factor — to 59%. Two forecasting models were compared for the residential load. Both models achieved a root mean square error (RMSE) smaller than 4%. One model had a slightly sharper forecast than the other model — by 2.6% — and a variable prediction interval (PI) which decreased at night. As regards the spatiotemporal matching between PV power production and EV charging in rural and urban areas, the results showed that there were no correlations between the building type in each part of the city and the temporal matching. Both residential and workplace areas had similar temporal matching. This is because of the orientations of the roofs in the cities and the sizes of the parking lots. Considering the impacts of EV charging on the distribution grid of a Swedish municipality (Herrljunga), it is shown that 3.7 kW chargers will result in at most a 1% decrease in the voltage of the grid. No under-voltages were witnessed. In conclusion, the urban and rural energy systems can withstand the penetration of PV and EVs in the nearby coming years. Extreme scenarios might, however, require increasing the flexibility or performing upgrades to the systems.

Spatial Markov chain model for electric vehicle charging in cities using geographical information system (GIS) data

In the recent years, the number of electric vehicles (EVs) on the road have been rapidly increasing. Charging this increasing number of EVs is expected to have an impact on the electricity grid especially if high charging powers and opportunistic charging are used. Several models have been proposed to quantify this impact. Multiple papers have observed that the charging stations are used by multiple users during the day. However, this observation was not assumed in any previous model. Moreover, none of the previous models relied on geospatial maps to extract information about the parking lots—where charging stations are installed—and the charging profiles of the potential users of these charging stations. In this paper, a spatial Markov chain model is developed to model the charging load of EVs in cities. The model assumes three distinct charging profiles: Work, Home, and Other. Geospatial maps were used to estimate the charging profile, or mixture of profiles, of the charging stations based on the nearby building types. A case study was made on the city of Uppsala, Sweden—a city with approximately 44,000 cars. The results of the case study indicated that the aggregate load of the EVs in the city reduced the charging impact. For example when using 22 kW chargers, the peak load in the city per EV was estimated to be 1.29 kW/car in case of spatio- temporally opportunistic charging, and 1.47 kW/car in case of residential only opportunistic charging. This is to say that the Swedish grid operators can expect that every EV in the city will increase the peak load by at most 1.47 kW due to aggregation; this is assuming that 22 kW chargers were used. In addition, we showed that the minute-minute variability of the charging load in cities might cause some future challenges. In our case, up to 3% of the EVs in the city simultaneously started charging. This caused a one- minute-ramp in the charging load of 1.1 MW—if charging using 3.7 kW. Charging with higher powers will ex- acerbate these ramps, e.g., charging with 22 kW will cause sudden one-minute-increases as high as 6.7 MW in the charging load. Such a finding indicates that using high charging powers might cause high variability in the charging load of EVs in cities. This high variability might limit the synergy potentials between EVs and variable renewable energy sources (RES). The proposed model can potentially be used along with RES models to estimate the spatio-temporal synergy potentials between the two technologies. Evaluating the synergy potentials might be of value to grid operators, policy makers, market traders, etc

Photovoltaics and opportunistic electric vehicle charging in a Swedish distribution grid

Renewable distributed generation and electric vehicles (EVs) are two important components in the transitions to a more sustainable society. However, both distributed generation and EV charging pose new challenges to the power system due to intermittent generation and high-power EV charging. In this case study, a power system consisting of a low- and medium-voltage distribution grid with more than 5000 customers, high penetration of roof-top mounted photovoltaic (PV) power systems and a fully electrified car fleet is used to assess the impact of the intermittent PV generation and high-power EV charging loads. Two summer weeks and two winter weeks with and without EV charging and a PV penetration varying between 0% and 100% of the annual electricity consumption are examined using measured and simulated data. Results show that the electricity consumption increases with 9% and 18% during the studied periods, and that EV charging only marginally can contribute to lowering the risk of overvoltage for customers resulting from PV overproduction. The most significant result is the increase in undervoltage in the winter when EV charging is introduced. The share of customers affected by undervoltage increases from 0% to close to 1.5% for all PV penetration levels

Photovoltaics and opportunistic electric vehicle charging in a Swedish distribution grid

Renewable distributed generation and electric vehicles (EVs) are two important components in the transitions to a more sustainable society. However, both distributed generation and EV charging pose new challenges to the power system due to intermittent generation and high-power EV charging. In this case study, a power system consisting of a low- and medium-voltage distribution grid with more than 5000 customers, high penetration of roof-top mounted photovoltaic (PV) power systems and a fully electrified car fleet is used to assess the impact of the intermittent PV generation and high-power EV charging loads. Two summer weeks and two winter weeks with and without EV charging and a PV penetration varying between 0% and 100% of the annual electricity consumption are examined using measured and simulated data. Results show that the electricity consumption increases with 9% and 18% during the studied periods, and that EV charging only marginally can contribute to lowering the risk of overvoltage for customers resulting from PV overproduction. The most significant result is the increase in undervoltage in the winter when EV charging is introduced. The share of customers affected by undervoltage increases from 0% to close to 1.5% for all PV penetration levels

Optimal PV-EV sizing at solar powered workplace charging stations with smart charging schemes considering self-consumption and self-sufficiency balance

The integration of photovoltaic (PV) systems and electric vehicles (EVs) in the built environment, including at workplaces, has increased significantly in the recent decade and has posed new technical challenges for the power system, such as increased peak loads and component overloading. Several studies show that improved matching between PV generation and EV load through both optimal sizing and operation of PV-EV systems can minimize these challenges. This paper presents an optimal PV-EV sizing framework for workplace solar powered charging stations considering load matching performances. The proposed optimal sizing framework in this study uses a novel score, called self-consumption-sufficiency balance (SCSB), which conveys the balance between self-consumption (SC) and self-sufficiency (SS), based on a similar principle as the F1-score in machine learning. A high SCSB score implies that the system is close to being self-sufficient without exporting or curtailing a large share of local production. The results show that the SCSB performance tends to be higher with a larger combined PV-EV size. In addition to presenting PV-EV optimal sizing at the workplace charging station, this study also assesses a potential SC and SS enhancement with optimal operation through smart charging schemes. The results show that smart charging schemes can significantly improve the load matching performances by up to 42.6 and 40.8 percentage points for SC and SS, respectively. The smart charging scheme will also shift the combined optimal PV-EV sizes. Due to its simplicity and universality, the optimal sizing based on SCSB score proposed in this study can be a benchmark for future studies on optimal sizing of PV-EV system, or distributed generation-load in general.SweGRIDS FPS 26 - Smart charging strategies and optimal PV-EV sizing to increase the combined PV-EV hosting capacity in the distribution gri

Scenario-based modelling of the potential for solar energy charging of electric vehicles in two Scandinavian cities

In order to contribute to the reduction of greenhouse gas emissions, electric vehicles (EVs) should be charged using electricity from renewable energy sources. This paper describes a study of photovoltaics (PV) utilization for EV charging in two Scandinavian cities: Tromsø in Norway and Uppsala in Sweden, with the objective to evaluate self-sufficiency and self-consumption. The suitable areas for PV were determined using building area statistics and utilization factors. The PV yield was simulated for integration scenarios of 10%-100% of the suitable area. EV charging patterns were generated using a stochastic model based on travel survey data. The scenarios include EV penetration of 10%-100% of the personal vehicle fleet. The results show that the PV energy yield could cover the EV load in most of the scenarios, but that the temporal load match could be improved. The energy balance was positive for all seasons and EV levels if the PV integration was over 50%. The highest self-sufficiency was achieved in Tromsø during summer, due to the longer days. For high EV penetration and low PV integration, the self-sufficiency was higher in Uppsala, indicating that installed PV power is more important than yield profile above a certain number of EVs

Photovoltaics and opportunistic electric vehicle charging in the power system : a case study on a Swedish distribution grid

Renewable distributed generation and electric vehicles (EVs) are two important components in the transition to a more sustainable society. However, both pose new challenges to the power system due to the intermittent generation and EV charging load. In this case study, a power system consisting of a low- and medium-voltage rural and urban distribution grid with 5174 customers, high penetration of photovoltaic (PV) electricity and a fully electrified car fleet were assumed, and their impact on the grid was assessed. The two extreme cases of two summer weeks and two winter weeks with and without EV charging and a PV penetration varying between 0 and 100% of the annual electricity consumption were examined. Active power curtailment of the PV systems was used to avoid overvoltage. The results show an increased electricity consumption of 9.3% in the winter weeks and 17.1% in the summer weeks, a lowering of the minimum voltage by 1% at the most, and a marginal contribution by the EV charging to lower the need of PV power curtailment. This shows the minor impact of EV charging on the distribution grid, both in terms of allowing more PV power generation and in terms of lower voltage levels