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While the grid-connected capacity of rural household photovoltaics is increasing rapidly, achieving dynamic supply-demand matching despite fluctuations in solar energy is challenging. With the rapid development of rural electrification, battery-powered technologies, such as electric vehicles and electric agricultural machinery, are becoming increasingly popular in rural areas. In this context, utilizing idle mobile batteries to assist in energy storage for rural residential buildings offers a new way to solve the problem of dynamic supply-demand matching. In this study, a field survey was conducted on several typical fruit-growing villages in the Central Shaanxi Plain in Shaanxi Province of China. Typical rural households were selected to calculate the electricity loads of the residential buildings, with due consideration to the intervention of mobile batteries. Under the premise of installing 3 kW household photovoltaic systems in rural households, an economical efficiency-oriented model was built for the optimal regulation of flexible loads. The results were compared in the context of two patterns of electricity consumption, i.e., unidirectional charging of mobile batteries from buildings and bidirectional charging and discharging between mobile batteries and buildings. The bidirectional pattern significantly increased the photovoltaic consumption of typical rural households on various typical days. Specifically, during both scenarios of not implementing time-of-use and implementing time-of-use, the typical day of the winter slack farming season exhibited the best photovoltaic consumption effect among all types of typical days. Additionally, the bidirectional pattern also result in a significant increase in the annual electricity sales revenues for typical rural households.
Alilou M, Tousi B, Shayeghi H (2020). Home energy management in a residential smart micro grid under stochastic penetration of solar panels and electric vehicles. Solar Energy, 212: 6–18.
Arab MB, Rekik M, Krichen L (2022). Suitable various-goal energy management system for smart home based on photovoltaic generator and electric vehicles. Journal of Building Engineering, 52: 104430.
Cai Q, Xu Q, Qing J, et al. (2022). Promoting wind and photovoltaics renewable energy integration through demand response: dynamic pricing mechanism design and economic analysis for smart residential communities. Energy, 261: 125293.
Elkadeem MR, Abido MA (2023). Optimal planning and operation of grid-connected PV/CHP/battery energy system considering demand response and electric vehicles for a multi-residential complex building. Journal of Energy Storage, 72: 108198.
Fernández Bandera C, Bastos Porsani G, Fernández-Vigil Iglesias M (2023). A demand side management approach to increase self-consumption in buildings. Building Simulation, 16: 317–335.
Huy THB, Truong Dinh H, Ngoc Vo D, et al. (2023). Real-time energy scheduling for home energy management systems with an energy storage system and electric vehicle based on a supervised-learning-based strategy. Energy Conversion and Management, 292: 117340.
Iqbal MM, Sajjad MIA, Amin S, et al. (2019). Optimal scheduling of residential home appliances by considering energy storage and stochastically modelled photovoltaics in a grid exchange environment using hybrid grey wolf genetic algorithm optimizer. Applied Sciences, 9: 5226.
Jiang X, Wu L (2020). A residential load scheduling based on cost efficiency and consumer’s preference for demand response in smart grid. Electric Power Systems Research, 186: 106410.
Li B, You L, Zheng M, et al. (2020). Energy consumption pattern and indoor thermal environment of residential building in rural China. Energy and Built Environment, 1: 327–336.
Li Y, Zhang X, Xiao F, et al. (2023). Modeling and management performances of distributed energy resource for demand flexibility in Japanese zero energy house. Building Simulation, 16: 2177–2192.
Lokeshgupta B, Sivasubramani S (2019). Multi-objective home energy management with battery energy storage systems. Sustainable Cities and Society, 47: 101458.
Lu Q, Zhang Z, Lü S (2020). Home energy management in smart households: Optimal appliance scheduling model with photovoltaic energy storage system. Energy Reports, 6: 2450–2462.
Luo Z, Peng J, Cao J, et al. (2022). Demand flexibility of residential buildings: Definitions, flexible loads, and quantification methods. Engineering, 16: 123–140.
Luo X, Yang Y, Liu Y, et al. (2023a). Classification of energy use patterns and multi-objective optimal scheduling of flexible loads in rural households. Energy and Buildings, 283: 112811.
Luo Z, Peng J, Hu M, et al. (2023b). Multi-objective optimal dispatch of household flexible loads based on their real-life operating characteristics and energy-related occupant behavior. Building Simulation, 16: 2005–2025.
Nan S, Zhou M, Li G (2018). Optimal residential community demand response scheduling in smart grid. Applied Energy, 210: 1280–1289.
Niu M, Ji Y, Zhao M, et al. (2024). A study on carbon emission calculation in operation stage of residential buildings based on micro electricity usage behavior: Three case studies in China. Building Simulation, 17: 147–164.
Qi N, Cheng L, Xu H, et al. (2020). Smart meter data-driven evaluation of operational demand response potential of residential air conditioning loads. Applied Energy, 279: 115708.
Sengupta M, Xie Y, Lopez A, et al. (2018). The national solar radiation data base (NSRDB). Renewable and Sustainable Energy Reviews, 89: 51–60.
Shewale A, Mokhade A, Funde N, et al. (2020). An overview of demand response in smart grid and optimization techniques for efficient residential appliance scheduling problem. Energies, 13: 4266.
Wang R, Jiang Z (2017). Energy consumption in China’s rural areas: A study based on the village energy survey. Journal of Cleaner Production, 143: 452–461.
Wang H, Zheng Y, Zhou M (2021). Unit scheduling considering the flexibility of intelligent temperature control appliances under TOU power price. International Journal of Electrical Power & Energy Systems, 125: 106477.
Wang X, Huang W, Li R, et al. (2023a). Frequency-based demand side response considering the discontinuity of the ToU tariff. Applied Energy, 348: 121599.
Wang Z, Luther MB, Horan P, et al. (2023b). On-site solar PV generation and use: Self-consumption and self-sufficiency. Building Simulation, 16: 1835–1849.
Xu J, Gao W, Huo X (2018). Analysis on energy consumption of rural building based on survey in northern China. Energy for Sustainable Development, 47: 34–38.
Zhang G, Shi Y, Maleki A, et al. (2020). Optimal location and size of a grid-independent solar/hydrogen system for rural areas using an efficient heuristic approach. Renewable Energy, 156: 1203–1214.
Zhou N, Fan W, Liu N (2016). Battery storage multi-objective optimization for capacity configuration of PV-based microgrid considering demand response. Power System Technology, 40(06): 1709–1716. (in Chinese)
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