• Energy Research

Abstract The use of fossil fuel vehicles is one of the factors responsible for the degradation of air quality in urban areas. In order to reduce levels of air pollution in metropolitan areas, several countries have encouraged the use of electric vehicles in the cities. However, due to the high investment costs in this class of vehicles, it is expected that the spatial distribution of electric vehicles' adopters will be heterogeneous. The additional charging power required by electric vehicles' batteries can change operation and expansion planning of power distribution utilities. In addition, urban planning agencies should analyze the most suitable locations for the construction of electric vehicle recharging stations. Thus, in order to provide information for the planning of electric mobility services in the city, this paper presents a spatiotemporal model for estimating the rate of electric vehicles' adopters per subareas. Results are spatial databases that can be viewed in geographic information systems to observe regions with greater expectancy of residential electric vehicle adopters. These outcomes can help utilities to develop new services that ground on the rising availability of electric mobility in urban zones.

Abstract A considerable amount of governmental agencies have sought to improve their inhabitants' quality of life in recent years, encouraging new sustainable technologies. The characterization of how these new technologies are disseminated in urban areas can help planners carry out campaigns or incentive policies that help achieve decarbonization goals. This paper presents a methodology based on fuzzy logic to characterize the spatiotemporal dynamics as to the adopters of photovoltaic systems in residential subareas. This characterization model proposes to demonstrate the influence that the first inhabitants, who adopted the technology of photovoltaic systems, can exert on the inhabitants of adjacent sub-areas. Considering that the degree of adherence to photovoltaic technology is related to subjective characteristics, the proposal uses three linguistic variables: low, medium, and high to model such adherence using fuzzy sets. The proposed methodology results present heat maps that identify the subareas with more inhabitants adopting photovoltaic systems. Hence, it has been applied in a medium-sized city in the state of Sao Paulo, Brazil. The obtained spatial distribution of photovoltaic adopters is heterogeneous, with subareas presenting medium and high values of photovoltaic adopters for each study year. The percentages of categorized subareas as high in the years 2017, 2018, 2019 are 8,3%, 19,3%, 19,7%, respectively. These results indicate that planners should promote more PV technology, especially in the city's outskirt subareas. A spatiotemporal estimation method and the history of recording installations of photovoltaic systems are used for comparison purposes. The comparison of results shows that the proposed method better characterizes the PV diffusion installed in the first years. This class of results can help planners better assess the diffusion goals of photovoltaic systems in urban areas, which are being used in the integrated planning of sustainable cities and provide information for planners to reach decarbonization goals.

Abstract This paper presents a maximum power point tracking (MPPT) algorithm based on the Monod equation for photovoltaic (PV) systems with DC regulation. This equation is applied to estimate the power output for each current output of PV generator. The proposed MPPT permits to quickly estimate the required duty-cycle by the step-up converter that is associated with the provided maximum power from the generator. The new MPPT algorithm has a lower implementation complexity and with a lower computational effort, which is important to implement using less expensive controllers and low cost processor. The proposed algorithm is tested in a PV system composed by solar module that emulates of PV generator and which the input voltage is regulated by a push-pull converter, supplying a DC load. The experimental results demonstrate that use of the Monod equation improves the dynamics of the PV system and find the maximum value of power for conditions of low irradiance unlike other methodologies.

This paper presents a spatial-temporal approach for estimating the load demand of battery electric vehicles (BEV) charging in small residential areas. This approach is especially suited for simulating the driving pattern of BEVs in cities without this kind of information. The service zone is divided into several sub-zones; each of these has a probability that represents how likely it is for a BEVs to cross the sub-zone. The driving pattern of BEVs is simulated using a multi-agent framework, which estimates the spatial distribution of these in a city. To determine the hourly charge in each place identified in the spatial area, the model considers the battery charging profile via two charging scenarios. The main contribution of this method is the estimation of BEV charging in feeders or transformers using small-scale simulation. The proposed approach was tested on a real distribution system of a mid-sized city in Brazil. For this specific system, the simulation was able to identify two different levels of agglomerations; when the worst-case scenario with a 20 % penetration is analyzed, an increase in peak demand up to 34.04 % was determined in the most affected part of the distribution system while the rest of the distribution system is almost unaffected.

Distribution utilities must perform forecasts in spatial manner to determine the locations that could increase their electric demand. In general, these forecasts are made in the urban area, without regard to the preferences of the inhabitants to develop its activities outside the city boundary. This may lead to errors in decision making of the distribution network expansion planning. In order to identify such preferences, this paper presents a geographically weighted regression that explore spatial patterns to determines the probability of rural regions become urban zones, as part of the urban sprawl. The proposed method is applied in a Brazilian midsize city, showing that the use of the calculated probabilities decreases the global error of spatial load forecasting in 6.5% of the load growth.

This paper presents a novel method for estimating the spatial distribution in geographical space of the nontechnical losses over time. The method progresses in two stages: in the first stage, a generalized additive model is used to generate a map of current loss probabilities. The second stage employs the Markov chain to generate a map that indicates possible future changes in loss probabilities. The method yields an assessment of the location of the nontechnical losses now and in the future at the city subarea level, even indicating the variables that have greater statistical correlation with the nontechnical losses. We apply the method to a city with approximately 81 $\thinspace$ 000 consumers, and the results are compared with those obtained through inspections carried out by a Brazilian power utility. The detection rate surpasses 78% in inspected subareas. The method we propose offers improved estimation of distribution of the nontechnical losses in urban regions.

A multi-agent framework for spatial electric load forecasting, especially suited to simulate the different dynamics involved on distribution systems, is presented. The service zone is divided into several sub-zones, each subzone is considered as an independent agent identified with a corresponding load level, and their relationships with the neighbor zones are represented as development probabilities. With this setting, different kind of agents can be developed to simulate the growth pattern of the loads in distribution systems. This paper presents two different kinds of agents to simulate different situations, presenting some promissory results.

Abstract In recent years, spatial load forecasting studies have helped to direct the expansion of the distribution systems in cities with rapid urban growth, providing maps that showing the spatial distribution of expected load. However, these maps do not allow to determine how load varies on the existing network elements. This information is important to define the reinforcements or the installation of new facilities in the electrical distribution network. In order to help planners in such decisions, a search method to allocate the loads resulting from spatial load forecasting studies is presented. This method treats each of these forecast loads as new load center to be connected to an existing distribution feeder. To find the path from a load center, the proposed method uses a list of its nearby feeders. Allocation depends on the path cost function, which is calculated based on the supply capability of the network elements. The proposal chooses the shortest path with sufficient capacity to supply the new load, i.e., it finds the path with minimal cost function for list of nearby feeders. The result is the final available capability of existing networks (after the allocation process) to supply the expected loads in the geographic area. The method is tested using the results of a spatial load forecast for a real distribution system in a medium-sized Brazilian city. In this test system, the load allocation influenced the number of network elements to be reinforced. The proposal was compared to commercial software, showing a configuration with smaller numbers of overload elements and a lower cost of expansion to the most overloaded feeders.

In recent years, as a policy to increase penetration of electric vehicles and reduce air pollution, the number of electric taxis has increased in the transportation networks. In contrast to private electric vehicles, electric taxis need to recharge in a short time due to their constant operation and different driving patterns. Therefore, fast charging stations are required to meet the demand for recharging electric taxis and should be located at strategic places. To improve the sustainable planning of urban infrastructures, this paper presents a methodology to help in decision making for installing fast charging stations, considering the criteria of all urban planners involved in the installation process. The result of the proposal is a spatial database that identifies locations for charging stations that meet the predefined criteria. Such database can be visualized in geographic information systems of the urban planning department or the electrical service concessionaire. The proposed methodology is tested in a Brazilian medium-sized city showing the importance of this information in decision making. The proposal is compared with another spatial methodology, showing that the proposed method creates a better spatial distribution of charging stations giving better options for the main agents interested in the city.