Heat consumption scenarios in the rural residential sector: the potential of heat pump-based demand-side management for sustainable heating

Abstract

Background Poor air quality and inadequate domestic heating sources are tightly connected problems in the Eastern-European countryside. There are a few alternatives to comprehensively solve these problems with an emphasis on economic and environmental sustainability. In this paper, individual heat pumps and energy efficiency measures are analyzed in four scenarios to investigate their role for cleaner and sufficient heating in rural settings. From a high-level perspective, this paper assesses the potential for electricity demand flexibility based on the use of individual heat pumps equipped with hot water storage. Methods In a first step, the current annual residential heating energy consumption of a rural area in Hungary is estimated with a bottom-up model using data from field surveys and official datasets. In a second step, four scenarios estimate the future heating energy consumption considering demographic trends and the average number of building retrofits (thermal insulation) performed yearly in the region. Results The reference scenario is a 20-year projection of heating energy consumption with modest retrofit actions which resulted in small energy savings. Alternative scenarios based on more effective retrofit actions reaching higher rates of the building stock would result in a 23–69% reduction in final energy consumption. Phasing out lignite from the heating energy mix would reduce particulate matter emissions. Each well-insulated dwelling with a heat pump-based system and hot water storage tank could provide theoretical flexibility of approximately 3.4 kWh per 24 h. Conclusions The current energy retrofit practices are not sufficient to solve the challenges faced in this area. Demand-side management policies based on the widespread use of individual heat pumps are an alternative to promote access to cleaner residential heating. An additional benefit is the potential for flexibility in electricity demand. Thus, this paper calls for an integration of energy retrofit with efficient heating (including hot water storage tank) to achieve results that further contribute to a future of sustainable energy.

The heat pumps for better urban air quality✰

“…As regards the combustion efficiency, for the centralized heating systems we consider the percentage distribution relative to 2003 [19], for which the efficiency is for 5.15% of the boilers less than 0.85, for 26.97% between 0.85 and 0.9, for 62.87% between 0.90 and 0.95 and for 5% above 0.95. For individual systems, table 3 shows the data of [11], for boilers maintained and not, by year of installation.…”

Optimal scheduling of DG and EV parking lots simultaneously with demand response based on self‐adjusted PSO and K‐means clustering

“…DR can be modeled using the elasticity matrix of the electricity prices of the load demands. 4,5 Using a constant elasticity matrix over a pre-determined period of time, it was concluded by various studies [6][7][8][9][10][11] that DR has a significant positive impact on the electricity market prices, reliability, and spinning reserves issues. However, this assumption of fixed elasticity of specific over specific time results in the incredibility of EVs of the proposed methods.…”

“…[30][31][32][33][34] The optimal charging management was investigated by Mkahl et al 35 Similar work was achieved by Bin-Humayd and Bhattacharya. 36 The parking coordination of EVs was investigated in the study by Faddel et al 37 The investigation of the above research [2][3][4][5][6][7][8][9][10][11][12][13][15][16][17] reveals that a constant elasticity matrix for a specific interval of time was used, which results in some incredibility. In the study by Srivastava et al, 14 regression methods generally need training, and the accuracy of the regression models depends on the number of available data.…”

Development of an evaluation method on using electric heating in cold rural areas of China

“…Ali et al (Ali et al 2014) proposed that electric heating and thermal storage, when sued in series, can achieve an optimal demand response in smart grid scenarios by simulating and analyzing heat demand and power. Campos et al (Campos et al 2020) conducted energy analysis and prediction for rural heating in Hungary; they suggested that a combination of electric energy retrofitting and efficient heating technologies can be used to meet rural heating.…”