To support the integration of renewable energy sources, and increase the transparency for flexibility requirements, EU released the EU directive 2019/944. It enforces distribution system operators to develop network development plans (NDPs) for their respective networks. This paper present the results from an interview study conducted to gain insights from some of the member states that have already implemented the directive, to learn from their experience. The study found overall that current systems inability to export and share data through simple means requires large amount of resources from the DSOs. Furthermore the role of flexibility in NDPs was analysed. With more flexibility from the demand side it was found that behaviour scenario will play a vital role in order to assess the hosting capacity.

This study develops and applies an open data-based reference electricity grid analysis (REGAL) model designed to create a synthetic representation of a low-voltage (LV) grid for a country-size geographic area. The model enables large-scale grid simulation in which new loads, such as electric vehicle charging, can be added to estimate their impacts on the current LV grid. The modeling is carried out in three steps: (1) generation of a synthetic LV grid; (2) addition of residential loads, including electric vehicle charging; and (3) evaluating if the grid capacity is exceeded. The grid is generated by selecting transformers and cables so that the system can fulfill the current demand while meeting national regulations and standards for distribution grids, all at the lowest total cost. This paper presents the results of calibration and validation against real-world data for the predicted electricity demands and synthetic grid generated by the model. Different calibration values were explored, and the accuracy of the estimations of grid capacities was calibrated using proprietary real-world data from grid operators. For a region with multiple grid cells, an average deviation from real-world data of +/- 10 % was achieved. For an average area of 1 km2, the error was 44.5%, which means that the model is not suitable for analysis on this geographic level. However, the level of accuracy is deemed sufficient for initial estimations of hosting capacity for larger geographic areas, such as a region or a country, thereby enabling estimations of hosting capacity in new areas that lack publicly accessible grid capacities.

Through the Just Energy Transition in the Green New Deal, the European Union has emphasized the importance of inequality related to the energy transition. Yet, inequalities related to the hosting capacity for residential solar PV has gotten limited attention. Using estimations of hosting capacity for residential solar PV we analyze how residential solar PV hosting capacity is distributed based on social and economic status in Sweden. We find that solar photovoltaic hosting capacity is not equally distributed and that households with a high socio-economic burden tend to have less available hosting capacity for residential solar PV than households with a low socio-economic burden. A distribution of solar photovoltaic systems that best utilizes the existing grid would therefore on average be unfavorable for households with a high socio-economically burden. These findings could present a barrier for a Just energy transition towards a climate neutral energy system, and lead to increased inequalities in cost and access to renewable energy technologies, especially for countries with lower hosting capacity levels. To reduce potential inequality in grid access, we propose that policies either support the adoption of many small solar PV systems to avoid hosting capacity issues, or support households that currently lacks physical access to the residential solar PV market by allowing investments in solar PV systems at another property.

One option to avoid range issues for electrified heavy vehicles, and the large individual batteries for each such vehicle, is to construct electric road systems (ERS), where vehicles are supplied with electricity while driving. In this article, a model has been developed that calculates the cost for supplying an ERS with electricity from a regional grid to a road in the form of cables and substations, considering the power demand profile for heavy transport. The modeling accounts for electric losses and voltage drop in cables and transformers. We have used the model to exhaustively compute and compared the cost of different combinations of substation sizes and locations along the road, using a European highway in West Sweden as a case study. Our results show that the costs for building an electricity distribution system for an ERS vary only to a minor extent with the location of substations (10% difference between the cheapest cost and the average cost of all configurations). Furthermore, we have varied the peak and average power demand profile for the investigated highway to investigate the impact of a specific demand profile on the results. The results from this variation show that the sum of the peak power demand is the most important factor in system cost. Specifically, a 30% change in the peak power demand for the road has a significant impact on the electricity supply system cost. A reduction in the geographical variation of power demand along the road has no significant impact on the electricity distribution system cost as long as the aggregated peak power demand for all road segments is held constant. The results of the work are relevant as input to future work on comparing the cost–benefit of ERS with other alternatives when reducing CO2 from road traffic—in particular from heavy road traffic.

Electrification of passenger vehicles is rapidly becoming the main alternative for decarbonizing transportation. The high power associated with charging of electric vehicles is likely to require actions from grid operators. Using machine learning and GIS analysis we produce forecasts of electric vehicles in very small cells, down to a few hundred meters for Norway. Using a baseline comparison, we find that a random forest model produces the overall lowest error, with a Mean Absolute Error of 14.0, and Mean Absolute Percentage Error of 33.9%. We find that both the existing vehicle fleet, and forecast shows that there is a large variation in electric vehicle adoption between cells. With knowledge where and when electric vehicles are adopted, grid operators can better plan their future investments related to electric vehicle charging, and thereby reduce investment costs.

Electric vehicles enable the decarbonization of the transportation sector. Yet, system implications of fully electrified private transportation on national low-voltage grids are still unknown. This study presents a geographic analysis of power system violations on a synthetically generated version of the Swedish low-voltage grid due to home charging of private passenger electric vehicles using different charging strategies. We link a national energy system model with a reference network model that together generate low-voltage proxy grids within each inhabited km2 of Sweden. We produce the first national coverage of electric vehicle charging impacts in residential areas. Our results show that the risk of power system violations due to electric vehicle charging are largest in cities and smaller in urban areas, while rural areas show significantly fewer violations. We also find that an electricity price optimized charging strategy primarily increases the risk of low-voltage power system violations in some city areas. In addition, we find large variations in power system violations in each of Sweden's four price areas. Direct charging is preferable in the far north while price optimized charging is preferable in the far south. The results show variations in impacts on distribution system operators from national low-cost renewable energy systems depending on their geographical location. The findings show important connections between national energy system models and local low-voltage grid violations.

Electrification of transportation using electric vehicles has a large potential to reduce transport related emissions but could potentially cause issues in generation and distribution of electricity. This study uses GPS measured driving patterns from conventional gasoline and diesel cars in western Sweden and Seattle, United States, to estimate and analyze expected charging coincidence assuming these driving patterns were the same for electric vehicles. The results show that the electric vehicle charging power demand in western Sweden and Seattle is 50–183% higher compared to studies that were relying on national household travel surveys in Sweden and United States. The after-coincidence charging power demand from GPS measured driving behavior converges at 1.8 kW or lower for Sweden and at 2.1 kW or lower for the United States The results show that nominal charging power has the largest impact on after-coincidence charging power demand, followed by the vehicle’s electricity consumption and lastly the charging location. We also find that the reduction in charging demand, when charging is moved in time, is largest for few vehicles and reduces as the number of vehicles increase. Our results are important when analyzing the impact from large scale introduction of electric vehicles on electricity distribution and generation.

Assessing grid capacity on national and local levels is important in order to formulate renewable energy targets, calculate integration costs of distributed generation (such as residential solar PV and electric vehicles). Currently, 70–96% of the residential solar PV installations in Germany and Italy are found in the low-voltage grid. Previous grid assessments have relied on grid data from individual low-voltage grids, making them limited to a few cases. This article presents synthetic low-voltage grid data from a reference network model. The reference network model generates synthetic low-voltage grids using publicly available data and national regulations and standards. In addition, the article presents data of residential solar photovoltaic hosting capacity in low-voltage grids. The datasets are high-resolution (1 × 1 km) and contains data on electricity peak demand, share of population living in apartments and important grid metrics such as transformer capacity, maximum feeder length and estimations of residential solar photovoltaic hosting capacity. Datasets on grid components are rare and the dataset can be used to assess grid impacts from other residential end-use technologies, and function as baseline for other reference network models.

Off-grid systems, and mini-grids in particular, are expected to play a significant role in improving electricity access to one billion people until 2040. One of the major challenges for mini-grids is associated with their high costs, low financial viability and local development impact. Productive use of electricity can be an important driver of local development and impacts the total load in a mini-grid. By using a mixture of high-resolution (minutes) measurements and long-term data (years) on electricity expenditures and purchased electricity from a mini-grid in the Tanzanian highlands, we analyse the technical and economic impact from household and productive use of electricity, respectively. The high-resolution data is analysed using performance indicators and the long-term data using regression tools. We find that a mixture of household use and productive use of electricity provides both technical and economic benefits for the operator. In addition, we find that while productive use customers only represent 25% of the customers, they generate 44% of the operator's income. Furthermore, productive use of electricity customers are also likely responsible for the peak demand in the mini-grid, which occurs during day time. Lastly, we find empirical evidence suggesting that expenditures and demand are unit elastic, which has implications on economic policies for supporting rural electrification.

Electrification is a known precondition for development and is still lacking in rural and inaccessible parts of the world. Electric conversion of small coastal fishing boats can be an economical driver for small coastal villages in addition to providing environmental benefits. With local standalone solar photovoltaic mini-grids, the system can be fully renewable while still being economically viable. This study shows that such a system could be feasible to build in the Kelan village, Bali, Indonesia. Benefits of modern and smart grid technologies can further increase the economic and environmental benefits. It is also shown that if multiple fishing boat owners join forces to invest in Li-ion batteries the benefit of the even greater.

The electric grid’s available capacity to accommodate solar photovoltaic on national scales is currently uncertain. This makes decisions about grid capacity expansion, which can be very costly for local grid operators, difficult to make. Yet, knowledge of national solar photovoltaic grid capacity is central in order to formulate realistic solar PV targets and strategies. We present a methodology based on publicly available data to estimate the grid’s hosting capacity of residential solar photovoltaic at both the national and local scale. The model is applied to Sweden, Germany and the UK and shows that low-voltage grid capacity for residential solar photovoltaic is very large, 33 (+5/-7) GW (Sweden), 248 (+5/-24) GW (Germany) and 63 (+1/-14) GW UK, and similar to current total generation capacity. Based on our estimations, we find that with the capacity of the present grid Sweden can supply 24%, Germany 60% and UK 21% of their current annual net electricity consumption from residential solar photovoltaic. In addition, we find that the grid-supported individual solar PV system sizes increase as population density decreases. Finally, our work highlights the importance of implementing sizing incentives for customers when installing their solar PV systems.

One option to improve electricity access to the one billion people currently lacking electricity access is autonomous mini-grids. Most of the unelectrified communities are located in rural areas far from the current grids. Rural area communities are characterized by geographical remoteness, dispersed consumers, low consumption, and limited ability to pay. The low consumption and limited ability to pay can affect the revenue of the mini-grid operator and owner, often leading to low cost-effectiveness of the mini-grid. However, as shown in the current work, correctly combining the number of household and productive users in a mini-grid can increase revenue by 40 % compared to only households if an equal tariff is considered for both customer groups.

Due to data restrictions and power system complexity issues, it is difficult to estimate grid capacity for solar PV on regional or national scales. We here present a novel method for estimating low-voltage grid capacity for residential solar PV using publicly available data. High-resolution GIS data on demographics and dwelling dynamics is used to generate theoretical low-voltage grids. Simplified power system calculations are performed on the generated low-voltage grids to estimate residential solar PV capacity with a high temporal resolution. The method utilizes previous developments in reference network modelling and solar PV hosting capacity assessments. The method is demonstrated using datasets from Sweden, UK and Germany. Even though the method is designed to estimate residential solar PV grid capacity, the first block of the method can be utilized to estimate grid capacity or impacts from other residential end-use technologies, such as electric heating or electric vehicle charging. This method presents:•A method for estimating peak demand based on population density and dwelling type.•Generation of low-voltage grids based on peak demand.•Sizing of transformers and cables based on national low-voltage regulations and standards.

Mini-grids are considered important in order to improve access to electricity in developing countries. Their technical and organizational independence from the large national grids and their interactions with local social, economic, and environmental factors suggests that system dynamics is a useful method of analysis. However, the successful implementation of mini-grids in rural electrification has partly been prevented due to complexity issues, making problem formulation difficult. Most problem-solving methods, such as system dynamics, require well-defined problems. Previous work on the problem formulation process in system dynamics is limited. This work presents a conceptual framework for tackling complexity and uncertainties in rural electrification. The conceptual framework is general and draws on research in conceptual modelling and system dynamics. The focus is on the learning that can be achieved from a system description and how it can be used to tackle complexity by reducing uncertainties and improving knowledge.

Microgrids have an important role to play in achieving current international targets of electrifying poor rural communities around the world. In the East-African context, microgrid developers face challenges related to dispersed settlement patterns and high poverty levels that prevent many rural citizens from affording grid connections. Contextual factors influence demand for electricity, leading to uncertainties regarding development of consumption in newly electrified areas. Developers struggle with the sizing of microgrids and often initially oversize the system in anticipation of growing demand, which leads to significant investment costs and economic risk in case projected growth fails to appear. Our focus in this paper is to introduce an approach for flexible and adaptive distribution design – a process that can reduce initial investment cost and still be able to meet the long-term variations of the load in a controlled manner, thereby removing an entry barrier related to microgrid development. We exemplify the usefulness of this design approach in three different application areas: distribution capacity, transformation capacity and level of protection systems. Each application area consists of a number of steps based on mature technologies that correspond to change in capacity. The steps can be taken in sequence or in part, to achieve a system configuration adaptive enough to handle changes in electricity consumption, both increasing and, in some cases, also decreasing. Considerations on how steps would impact on system operation, power transfer capacity and demands on local technical expertise and maintenance are included. Importantly, the technical discussion details socio-economic aspects and the consequences for end-users as well as the utility. We exemplify the feasibility of the approach and provide a context for the discussion using real-world examples from East Africa.

Mini-grids are seen as an important option for increasing access to electricity in non-electrified rural areas where grid-extension is unfeasible. Appropriately dimensioning and constructing mini-grids requires knowledge of electricity usage. There is currently a lack of measured load profiles from mini-grids and the most common method for estimating electricity usage is through appliance data collected via interviews. Thus, this paper compares and investigates the differences between measured daily load profiles and daily load profiles created from appliance data collected through interviews and how the two methods impact the dimensioning and operation of a mini-grid. This is done by comparing load profiles for an entire mini-grid, a household and SME customers with large loads. The paper reports differing results from the two methodologies. Generally, the results show that the interview-based load profiles fail to provide an accurate overall estimate. The calculated performance metrics for the two methods also shows large differences. The interview-based load profiles mainly fail to provide accurate estimates of energy and the energy related (capacity factor and load factor) performance metrics. Accordingly, the implications for mini-grid operators and developers could be significant. The interview-based load profiles indicate the mini-grid system to be considerably less technically and economically desirable than measurements show. Suggestions for how the interview process can be improved are presented.

The causal relationships between electrification and development of poor, rural communities are complex and contextual. The existing literature focuses mainly on the impact of rural electrification and electricity use on local socio-economic development, while the reverse feedbacks of various social and economic changes on electricity demand and supply have not been fully characterized. Most electricity access impact assessments assume linear, one-way effects and linear growth in electricity demand. However, the projections rarely match the reality, creating challenges for rural utilities. From a modelling perspective, the lack of attention to dynamic complexities of the electricity-development nexus prevents the appropriate modelling of electricity demand over time and, hence, informed planning for and sizing of power plants. With the goal to improve modelling of the electricity-development nexus, we undertake a comprehensive review and extensive analysis of the peer reviewed literature on electricity access and its impact on rural socio-economic development, and vice versa. We characterize and describe the nexus between electricity access and development through graphical causal diagrams that allow us to capture, visualise and discuss the complexity and feedback loops. Based on this, we suggest guidelines for developing appropriate models able to include and simulate such complexities. Our analysis confirms that electricity use is interconnected through complex causal relations with multiple dimensions of socio-economic development, viz. income generating activities, market production and revenues, household economy, local health and population, education, and habits and social networks The causal diagrams can be seen as a first step of the conceptualization phase of model building, which aims at describing and understanding the structure of a system. The presence of multiple uncertain parameters and complex diffusion mechanisms that describe the complex system under analysis suggests that systems-dynamic simulations can allow modelling such complex and dynamic relations, as well as dealing with the high uncertainties at stake, especially when coupled with stochastic approaches. (C) 2018 International Energy Initiative. Published by Elsevier Inc. All rights reserved.

In developing countries, mini-grids are seen as an important option to improve electrification rates in rural areas. In order to be successful, mini-grids face issues of operation and sizing of generation capacities. Current studies on the optimal sizing of mini-grids do not include capacity expansion feedbacks regarding the operator's or investor's long-term economic performance on growth in electricity usage, e.g. gap between demand and supply impacting the operator's income. Using a System Dynamics model, this paper compares the impact from two capacity expansion strategies on rural mini-grid operator's long-term economic performance. The two capacity expansion strategies are: a strategy with minimized costs and a strategy where only diesel power is allowed. Research shows that a cost-minimized capacity expansion strategy might not be the most beneficial solution for the operator's long term financial performance. Specifically, the high investment costs prohibit the implementation of the cost-minimized expansion strategy. In addition, the diesel-only expansion strategy suffers from high operational costs, which creates long-term challenges as the share of diesel increase. Therefore, the timeline of the investments and when to implement different strategies is important, creating a benefit for a System Dynamics approach.

Microgrid resource sizing problems typically include the analysis of a combination of value streams such as peak shaving, load shifting, or load scheduling, which support the economic feasibility of the microgrid deployment. However, microgrid benefits can go beyond these, and the ability to provide ancillary grid services such as frequency regulation or spinning and non-spinning reserves is well known, despite typically not being considered in resource sizing problems. This paper proposes the expansion of the Distributed Energy Resources Customer Adoption Model (DER-CAM), a state-of-the-art microgrid resource sizing model, to include revenue streams resulting from the participation in ancillary service markets. Results suggest that participation in such markets may not only influence the optimum resource sizing, but also the operational dispatch, with results being strongly influenced by the exact market requirements and clearing prices.