1. Introduction
Recently, cities and local communities worldwide have faced unprecedented challenges due to natural disasters’ escalating frequency and severity, primarily induced by the ongoing climate change crisis. In light of this heightened awareness of the climate crisis, efforts to concretize international energy transitions have been made, concurrently accentuating the imperative for energy transformation. According to a UN survey, approximately 60% of the world’s population is projected to reside in 100 major cities [
1]. These urban centers are expected to consume 78% of the world’s energy and account for over 60% of global greenhouse gas emissions [
2]. Densely populated cities play a pivotal role in reducing carbon emissions because they are the best locations for deploying clean, energy-efficient, and sustainable technologies [
3,
4]. Governments worldwide, in collaboration with international organizations, are actively pursuing policies to regulate future energy-related discussions and promote the proliferation of energy-sharing communities centered around cities as part of their efforts to achieve net-zero emissions by 2050.
Among its major cities, the United States is building green cities. New York City enacted the Climate Mobilization Act in April 2019 with the aim of reducing greenhouse gas emissions from large buildings by up to 70% by 2050 [
5]. Similarly, Los Angeles has developed a Sustainable City Plan that includes initiatives such as feasibility studies for solar power generation systems in multifamily households and programs for installing solar power systems in low-income communities [
6]. The EU has undertaken initiatives such as the City-Zen project to promote the construction of clean-energy cities. This project aims to improve energy efficiency by renovating buildings and integrating energy systems [
7]. China is committed to implementing green building standards in newly constructed urban buildings by 2025. Furthermore, China is progressively reducing energy consumption through energy-saving renovations of residential and public buildings while enhancing research and development initiatives for low-carbon technologies [
8]. The Japanese government is actively pursuing policies to expand the adoption of solar power, including policies to increase solar panel installations in buildings and apartments from 2040 onwards. In addition, they established a Power Purchase Agreement (PPA) framework to facilitate the expansion of solar power generation and ensure grid capacity for integration [
9]. Since 2021, the Malaysian government has promoted the installation of rooftop solar power generation facilities through the NEM3.0 initiative. Additionally, they implemented energy efficiency standards for household appliances in accordance with the National Energy Efficiency Action Plan from 2016 to 2025 [
10,
11]. Korea is advancing its eco-energy town project by expanding the concept of energy-neutral units and focusing on zero-energy buildings. To enhance energy efficiency, they defined incremental levels of zero-energy building concepts, with plans to expand the adoption of zero-energy buildings in private construction projects starting in 2025 [
12]. Although there may be variations in approaches between countries, proactive policies have led to the emergence of energy-sharing communities worldwide.
Viikki Village, created through a combination of the Finnish government, residents, and the environment, operates as an ecologically sustainable zero-energy town by actively supporting R&D, utilizing new and renewable energy, and monitoring energy data [
13]. The BedZED complex in the UK is an eco-friendly complex that has achieved self-sufficiency rates of 85% for heating and 45% for electricity using energy-saving architectural designs, solar power generation, and biomass plants. This energy self-sufficient community is designed to sustain life solely with the energy it produces within its boundaries [
14]. Germany, known for its active involvement in eco-friendly urban development projects, has the Vauban District in Freiburg. All houses in the Vauban community are passive houses that were designed to minimize internal energy loss through insulation. With its low energy efficiency, the Vauban district can save over 70% more energy than typical German houses [
15]. Additionally, there are energy-sharing communities in Vauban that utilize various technologies, including renewable energy and energy storage systems (ESSs) [
16,
17,
18,
19].
The purpose of a low-carbon energy-sharing community is to increase the energy self-sufficiency rate of the community by increasing the energy produced rather than the energy consumed. Various energy technology certification criteria have been used to evaluate energy efficiency. Switzerland has adopted Minergie as a standard for buildings. The Minergie criteria, calculated based on building type, consider the total energy demand. Certification is granted when a building satisfies the minimum energy demand requirements, electricity self-sufficiency, and other comprehensive assessments [
20]. The United Arab Emirates introduced the Emirates Energy Star (EES) rating system in 2010 to evaluate the energy efficiency of buildings and assign ratings based on energy-saving ratios [
21]. Vietnam employs two certification systems for green buildings: the Leadership in Energy and Environmental Design (LEED) rating system and a tool developed by the Vietnam Green Building Council (VGBC) known as LOTUS. These systems are certified on the basis of specific criteria related to environmental sustainability and energy efficiency [
22]. In South Korea, three evaluation standards exist: building energy efficiency grades, energy self-sufficiency rates, and evaluation methods that use Building Energy Management Systems (BEMSs) or remote metering electronic meters. Many studies have used the energy self-sufficiency rate as an evaluation indicator to assess energy efficiency [
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33]. The energy self-sufficiency rate refers to the ability to meet the energy demand using a certain degree of local energy sources [
34]. In zero-energy smart cities aimed at maximizing energy efficiency, it is crucial to consider city-level self-sufficiency rates during the planning phase. This consideration is related to the analysis of land use plans and the economic feasibility of projects. Therefore, an accurate estimation of the energy self-sufficiency rate is required during the building, city, and community design phases [
28].
Therefore, it is time to research methods to calculate the self-reliance rate of energy-sharing communities to increase the proportion of renewable energy sources. In addition, to increase energy self-sufficiency, accurate predictions for actual buildings, rather than predictions based on past data, are becoming necessary for planning and economic feasibility analyses in the development planning stage of smart cities and energy-sharing communities. Hence, there is a need for research related to the difference between the self-sufficiency rate estimated at the developmental planning stage and the actual self-reliance rate and cause analysis. Previous studies have estimated or calculated self-sufficiency rates. However, there is a lack of prior research that compares the estimated self-sufficiency rates during the building or city design phase with the actual post-design self-sufficiency rates and conducts a detailed analysis of the underlying causes.
We assume that the estimated energy self-sufficiency rate in the design stage will be more than 20% different from the calculated energy self-sufficiency rate in the operation stage, and we want to check the error and identify the cause by comparing the estimated energy self-sufficiency rate and the calculated value. A smart city selected as a national pilot smart city was selected as a research area, and it will be a case that can be used in the future when designing such a city. Data on design parameters and environmental conditions were obtained for 56 houses to estimate and calculate power generation and consumption for one year in the study area. Then, the energy self-sufficiency rate was calculated by collecting PV power generation and electricity consumption data of the study area that are the actual data. The estimated energy independence rate and the actual value were separated into total, district, and season based on each household, and the differences were compared.
This paper is organized as follows.
Section 2 describes the study area.
Section 3 suggests the research methodology for estimating and calculating the energy self-sufficiency rate.
Section 4 presents the results of the comparison between the estimated energy self-sufficiency rate and the actual energy self-sufficiency value for the study area, followed by the discussion and conclusion in
Section 5 and
Section 6.
2. Study Area and Data
This study selected a smart complex located in Gangseo-gu, Busan, South Korea as the target for analyzing the energy-sharing community sufficiency rate. The study area is located in the wide delta plain at the mouth of the Nakdong River. It is a gently sloping area with a slope of less than 5°, and it is flat at an altitude of approximately 10 m. The average annual temperature is 14.8°, and the annual precipitation is 1397 mm, which is 2° higher than the average annual temperature of South Korea (12.8°) and similar to the average annual precipitation (1358 mm). The village, which consists of 56 households in 29 buildings, has a land area of 7202 m2, a built-up area of 2200 m2, and a gross floor area of 3620 m2. Of the 56 households, 18 are detached houses with two to three floors, and the remaining 38 are attached houses with two to three households per building. All of the buildings do not use gas-fired heating systems, and they use electricity for cooling and heating energy, so they are installed with photovoltaic systems on the roofs. The 29 buildings vary in size, type (detached/attached house), installed PV systems, and inverters, so this study classifies them into two complexes and eight building types based on these differences.
First, we classified the buildings into two complexes based on the number of PV modules and inverters installed (
Figure 1). PV modules with a power of 85 Wp and an efficiency of about 0.6% higher than the first were used in the second complex, and two inverters were installed in each household. Only one building (type f) had only one inverter installed.
The 29 buildings were divided into eight types, A, B, C, D, E, F, f, and G, according to the number, arrangement of PVs installed, and households in each building, as shown in
Figure 2. The information for each type is summarized in
Table 1. A, D, E, and F are detached houses, and B, C, f, and G are attached houses. Among the attached houses, B and C have three households, f has two households, and G has six households living in one building. The number of solar panels installed in the attached houses is higher than in the detached houses, but the number of panels per household is similar: 13.75 for detached houses and 13.29 for attached houses. However, f has the smallest capacity with nine panels, and the number of households is two, so the number of panels that can be used by a household is small, at about 4.5.
6. Conclusions
This study has provided valuable insights into estimating and calculating energy self-sufficiency rates within energy-sharing communities, showcasing the interplay of various influencing factors. The energy self-sufficiency rate was estimated by focusing on an energy-sharing community of 56 households and comparing it to the actual energy self-sufficiency rate. The 56 households were divided into eight types according to the facilities, capacity of the PV system, annual energy self-sufficiency rate achieved using facility conditions, TMY, estimated power generation for each type, and average monthly power consumption data for four people provided by KEPCO to estimate the annual energy self-sufficiency rates for each type. The actual energy self-sufficiency rates were calculated using actual power generation and power consumption data in the study area from April 2022 to March 2023. Comparing the annualized energy independence rate, the estimated energy independence rate (171%) was calculated to be 28.57% higher than the actual energy independence rate (133%), which is a larger error than the assumption of a 20% error between the estimated and actual values. An additional seasonal analysis revealed that the error between the estimated and actual self-sufficiency rates is significantly larger in summer than in the winter. An analysis of energy generation and consumption sheds light on the reasons for this discrepancy. In winter, when the self-sufficiency rates were higher, the difference in energy generation was relatively small. However, because of electric heating, the actual energy consumption exceeded the predicted consumption, leading to a lower self-sufficiency rate. Furthermore, by comparing the difference in solar radiation levels between the TMY data, which are generally used to estimate the power generation of PV systems and actual weather conditions, it was confirmed that the correlation between the difference in weather conditions and the energy self-sufficiency rate error was similar. This suggests that variations in weather conditions can contribute to differences in self-sufficiency rate calculations.
Additionally, this study divided residential types into detached and attached housing types and compared the energy self-sufficiency rates by type. The difference in PV system panels installed in each type of building was relatively small, by approximately one panel, but when compared to annual power generation, the differences accumulated, resulting in a lower predicted generation for the attached house type. In the case of electricity consumption, the area of the detached house type is larger than that of the attached house type; therefore, there is a large amount of electricity used for cooling and heating, resulting in a large error. KEPCO’s average monthly electricity consumption (273 kWh) was based on four people, which was calculated for a fixed number of people in each household, as compared to the actual measured power (430 kWh). A difference in the consumption of 157 kWh was observed. The significant difference in power consumption between the detached and attached housing types can be attributed to variations in the number of occupants and the size of each dwelling. It was observed that the detached housing type has approximately 1.7 times more floor area than the attached housing type, leading to higher energy consumption for heating and cooling. To reduce power consumption errors, the power consumption values were corrected using the correlation between the actual power consumption and power consumption per household provided by KEPCO. A seasonal analysis of the corrected values showed notable reductions in the MAE (by 50.5%) and RMSE (by 61.5%) during the winter months, aligning with the energy consumption patterns of the research area, which rely on electric heating.
In this study, the energy self-sufficiency rate was calculated for the electricity production and consumption in the study area, and in the power consumption correction process, and only the correlation between the actual power consumption and the electricity consumption data per household provided by the KEPCO was used. Through this, it was confirmed that the existing electricity consumption was underestimated. An underestimation of electricity consumption can lead to an overestimation of the effectiveness of energy self-sufficiency, and resources may not be available to meet actual needs in the future. It can also distort targets for sustainable energy management, which can lead to instability in long-term energy planning. Therefore, it is necessary to collect more accurate and sufficient electricity consumption data in the future. On the other hand, this study did not consider energy consumption other than electricity, so in the future, it is necessary to calculate the energy self-sufficiency rate including all energy used other than electricity, such as gas and water for heating, and to consider additional variables that are highly correlated with electricity consumption, such as the number of people and area, to improve energy consumption estimation.
This study compared and analyzed the energy self-sufficiency rate estimated in the design phase and the actual energy self-sufficiency rate in the operation phase by selecting a national pilot smart city as a research area. Until now, there has been a lack of studies comparing the estimated energy independence rate of smart cities with the actual value. The error between the estimated value and the actual value can contribute to improving smart city design models and minimizing negative impacts on energy efficiency by considering problems such as errors caused by insolation and the overestimation of consumption when planning smart cities in the future. The comparison of energy self-sufficiency rates in smart cities is expected to provide symbolic meaning for sustainable city building, policy formulation, and field experience in a broader sense. In addition, it is expected that a large amount of data will be available after the operational phase of the study area is developed in the future to enable quality research.